What's new in IBM Cloud Pak for Data?

See what new features and improvements are available in the latest release of IBM Cloud Pak® for Data.

Version 4.6.6

Released: May 2023

This release of Cloud Pak for Data is primarily focused on defect fixes. However, this release also includes new features for DataStage®.

Software Version What does it mean for me?
Cloud Pak for Data platform 4.6.6

Version 4.6.6 of the platform includes various fixes.

Related documentation:
Cloud Pak for Data command-line interface (cpd-cli) 12.0.6

Version 12.0.6 of the Cloud Pak for Data command-line interface includes various fixes.

Cloud Pak for Data scheduling service 1.12.0

Version 1.12.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Related documentation:
Data Replication 4.6.6

Version 4.6.6 of the Data Replication service includes various fixes.

Related documentation:
Data Replication
DataStage 4.6.6

The 4.6.6 release of DataStage includes the following features and updates:

Use Google BigQuery in ELT run mode
You can now use the Google BigQuery connector in ELT as a source and as a target.

For more information, see ELT run mode in DataStage.

Local connection option renamed to Flow connection
The Local connection option, which is on the connector's Stage tab, is renamed to Flow connection.
Screen capture of the DataStage connection properties with Flow connection selected.

Your connections will work the same; the only change is the name of the option in the web client. For information about flow connections, see Connecting to a data source in DataStage.

Version 4.6.6 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Informix® 5.3.0

Version 5.3.0 of the Informix service includes various fixes.

Related documentation:
Informix

Version 4.6.5

Released: April 2023

This release of Cloud Pak for Data is primarily focused on defect fixes. However, this release also includes new features for services such as Data Replication, DataStage, Watson™ Discovery, and Watson Knowledge Catalog.

Software Version What does it mean for me?
Cloud Pak for Data platform 4.6.5

Version 4.6.5 of the platform includes various fixes.

For details, see What's new and changed in the platform.

Related documentation:
Cloud Pak for Data command-line interface (cpd-cli) 12.0.5

Version 12.0.5 of the Cloud Pak for Data command-line interface includes various fixes.

Cloud Pak for Data common core services 6.5.0

Version 6.5.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

If you install or upgrade a service that requires the common core services, the common core services will also be installed or upgraded.

Cloud Pak for Data scheduling service 1.11.0

Version 1.11.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Related documentation:
AI Factsheets 4.6.5

Version 4.6.5 of the AI Factsheets service includes various fixes.

Related documentation:
AI Factsheets
Analytics Engine Powered by Apache Spark 4.6.5

Version 4.6.5 of the Analytics Engine Powered by Apache Spark service includes various fixes.

For details, see What's new and changed in Analytics Engine Powered by Apache Spark.

Important: Version 4.6.5 is available only for x86-64 hardware. This refresh is not available for s390x hardware.
Related documentation:
Analytics Engine Powered by Apache Spark
Cognos® Analytics 23.5.0

The 23.5.0 release of Cognos Analytics includes the following features and updates:

Updated software version for Cognos Analytics
This release of the Cognos Analytics service provides Version 11.2.4 Fix Pack 1 of the Cognos Analytics software. For details, see Release 11.2.4 FP1 - New and changed features in the Cognos Analytics documentation.

Version 23.5.0 of the Cognos Analytics service includes various fixes.

Related documentation:
Cognos Analytics
Cognos Dashboards 4.6.5

Version 4.6.5 of the Cognos Dashboards service includes various fixes.

For details, see What's new and changed in Cognos Dashboards.

Related documentation:
Cognos Dashboards
Data Privacy 4.6.5

The 4.6.5 release of Data Privacy includes the following features and updates:

Redact mask all numeric data to zero
Now all numeric types of data that you mask will be redacted to 0. Numeric types of data, such as integer, float, and decimal, are redacted to 0 for all redact rules, including full, partial, or match source value length.

For details, see Redacting data method (Masking flow).

Version 4.6.5 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Related documentation:
Data Privacy
Data Refinery 6.5.0

Version 6.5.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Related documentation:
Data Refinery
Data Replication 4.6.5

The 4.6.5 release of Data Replication includes the following features and updates:

Replicate data from PostgreSQL
You can now replicate data from PostgreSQL to other databases by using Data Replication. For details on setting up and connecting to PostgreSQL, see Replicating PostgreSQL data.

Version 4.6.5 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Related documentation:
Data Replication
DataStage 4.6.5

The 4.6.5 release of DataStage includes the following features and updates:

Connect to more data sources in DataStage
You can now include data from these data sources in your DataStage flows:
  • Exasol
  • Presto. You can use this connection for source data only.

For the full list of DataStage connectors, see Supported data sources in DataStage.

Save time and effort with transform procedures
A stored procedure is a block of procedural constructs and embedded SQL statements. Because you can reuse stored procedures, you don’t need to write the same SQL statements again. Transform procedures use stored procedure properties that transform data by running various logic decisions at the database itself.

You can now use transform procedures with the following connectors:

  • Db2®
  • Db2 for i
  • Db2 for z/OS®
  • Microsoft SQL Server
  • Microsoft Azure SQL Database
  • Teradata (optimized)

For details, see Using stored procedures.

Additional connectors in ELT run mode
You can now use the following connectors in Extract, Load, and Transform mode:
  • Amazon RDS for PostgreSQL
  • IBM Cloud® Databases for PostgreSQL
  • IBM® Db2
  • IBM Db2 Warehouse
  • IBM Db2 on Cloud
  • Oracle

For details, see ELT run mode in DataStage.

Transform XML data with the new XML Input stage
You can now use the XML Input stage to transform hierarchical XML data into flat relational tables. For details, see XML Input stage.
Manage data sets and file sets as DataStage assets
You can now list, preview, and manage your data sets and file sets as DataStage assets. For details, see:
Updates to the Read mode for the Amazon S3 and Generic S3 connectors require that you update their properties
If you select the Read binary data from multiple files using wildcards read mode, you can also select Read a file to a row. Previously, the output was one binary file. However, starting in 4.6.5, the output is two columns:
  • One column for the file name
  • One column for the contents of the binary file
Important: If you currently have jobs with the Read a file to a row selection, you must update the connector properties for the two output columns. Otherwise, the job will fail.
For information about the Read modes, see:

Version 4.6.5 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Db2 Data Gate 3.2.0

Version 3.2.0 of the Db2 Data Gate service includes various fixes.

For details, see What's new and changed in Db2 Data Gate.

Related documentation:
Db2 Data Gate
Decision Optimization 6.5.0

Version 6.5.0 of the Decision Optimization service includes various fixes.

For details, see What's new and changed in Decision Optimization.

Related documentation:
Decision Optimization
EDB Postgres 12.14, 13.10, 14.7

This release of the EDB Postgres service includes various fixes.

Related documentation:
EDB Postgres
Execution Engine for Apache Hadoop 4.6.5

Version 4.6.5 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Related documentation:
Execution Engine for Apache Hadoop
IBM Match 360 2.3.29

Version 2.3.290 of the IBM Match 360 service includes various fixes.

Related documentation:
IBM Match 360 with Watson
MongoDB 5.0.13, 6.0.2

This release of the MongoDB service include the following features and updates:

Database version 4.4.0 is out of support
Version 4.4.0 is out of support. It is recommended that you deploy Version 5.0.13 or Version 6.0.2 databases.

This release of the MongoDB service include various fixes.

Related documentation:
MongoDB
Planning Analytics 4.6.5

The 4.6.5 release of Planning Analytics includes the following features and updates:

Updated versions of Planning Analytics software
This release of the Planning Analytics service provides the following software versions:
  • Planning Analytics Workspace Version 2.0.85.

    For details about this version of the software, see 2.0.85 - What's new in the Planning Analytics Workspace documentation.

  • Planning Analytics Spreadsheet Services Version 2.0.85.

    For details about this version of the software, see 2.0.85 - Feature updates in the TM1® Web documentation.

  • Planning Analytics for Microsoft Excel Version 2.0.85.

    For details about this version of the software, see 2.0.85 - Feature updates in the Planning Analytics for Microsoft Excel documentation.

  • Planning Analytics Engine Technical Preview Version 12.1.

    For details about this version of the software, see What's new in the Planning Analytics Engine documentation.

Version 4.6.5 of the Planning Analytics service includes various fixes.

Related documentation:
Planning Analytics
RStudio® Server Runtimes 6.5.0

Version 6.5.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Related documentation:
RStudio Server Runtimes
SPSS® Modeler 6.5.0

Version 6.5.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Related documentation:
SPSS Modeler
Voice Gateway 1.0.8

Version 4.6.5 of the Voice Gateway service includes various fixes.

Related documentation:
Voice Gateway
Watson Assistant 4.6.5

Version 4.6.5 of the Watson Assistant service includes various security fixes.

Related documentation:
Watson Assistant
Watson Discovery 4.6.5

The 4.6.5 release of Watson Discovery includes features and updates.

Manage the data in a collection
You can now access a Manage data page for a collection. From the new page, you can see a list of the documents in your collection and get a quick view of information about the documents. You can also delete documents from a collection with just a few clicks. For more information, see Excluding content from query results in the Watson Discovery documentation on IBM Cloud.
The Manage data page shows documents from the collection with associated text passages.
You now have more control over the data that is crawled by the database connector
When you connect to a database as an external data source, you can now specify the column from which to extract data. If you don't specify the column, a column with text or with a single large object is chosen to be crawled. You can also specify the MIME type of the data in the column that you want to crawl. For details, see the Database data source topic in the Watson Discovery documentation on IBM Cloud.

Version 4.6.5 of the Watson Discovery service includes various fixes.

Related documentation:
Watson Discovery
Watson Knowledge Catalog 4.6.5

The 4.6.5 release of Watson Knowledge Catalog includes the following features and updates:

Run data quality rules on additional data sources
You can now run data quality rules on data assets from the following data sources:
  • Amazon S3
  • Apache Cassandra
  • Databricks connected through Generic JDBC
  • SAP ASE
  • SAP HANA

For details, see Supported data sources for metadata import, metadata enrichment, and data quality rules.

Easier recovery from errors that happen during the initial synchronization with the data mart
If the synchronization for a particular item fails, the metadata related to them is not synchronized to the target tables in the data mart. Instead, this data item is skipped until you resolve the underlying problem.

After you resolve the issue that triggers the error, the details of that missing asset or artifact in the data mart are updated. For example, if a connection is missing some required fields, the synchronization of the connection is completed after you add the missing data.

In addition, whenever you change the reporting configuration, all the assets that were skipped during the initial synchronization will be queued for an update.

For details, see Starting, stopping, pausing, and resuming reporting for Watson Knowledge Catalog.

Version 4.6.5 of the Watson Knowledge Catalog service includes various fixes.

For details, see What's new and changed in Watson Knowledge Catalog.

Related documentation:
Watson Knowledge Catalog
Watson Knowledge Studio 4.9.0

Version 4.9.0 of the Watson Knowledge Studio service includes various fixes.

For details, see What's new and changed in Watson Knowledge Studio.

Related documentation:
Watson Knowledge Studio
Watson Machine Learning 4.6.5

Version 4.6.5 of the Watson Machine Learning service includes various fixes.

For details, see What's new and changed in Watson Machine Learning.

Important: Version 4.6.5 is available only for x86-64 hardware. This refresh is not available for s390x hardware.
Related documentation:
Watson Machine Learning
Watson Machine Learning Accelerator 3.5.0

Version 3.5.0 of the Watson Machine Learning Accelerator service includes various fixes.

Related documentation:
Watson Machine Learning Accelerator
Watson OpenScale 4.6.5

Version 4.6.5 of the Watson OpenScale service includes various fixes.

For details, see What's new and changed in Watson OpenScale.

Important: Version 4.6.5 is available only for x86-64 hardware. This refresh is not available for s390x hardware.
Related documentation:
Watson OpenScale
Watson Speech services 4.6.5

The 4.6.5 release of Watson Speech services includes features, fixes, and security updates.

For a complete list of what's new and changed, see:

Related documentation:
Watson Speech services
Watson Studio 6.5.0

Version 6.5.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Important: Version 6.5.0 is available only for x86-64 hardware. This refresh is not available for s390x hardware.
Related documentation:
Watson Studio
Watson Studio Runtimes 6.5.0

Version 6.5.0 of the Watson Studio Runtimes service includes various fixes.

For details, see What's new and changed in Watson Studio Runtimes.

Important: Version 6.5.0 is available only for x86-64 hardware. This refresh is not available for s390x hardware.
Related documentation:
Watson Studio Runtimes

Version 4.6.4

Released: March 2023

This release of Cloud Pak for Data is primarily focused on defect fixes. However, this release also includes new features for the platform, AI Factsheets, Data Replication, Db2, IBM Match 360, and Watson Knowledge Catalog.

Important: Cloud Pak for Data 4.6.4 introduces support for Red Hat® OpenShift® Container Platform Version 4.12. For details, see Software requirements.
Software Version What does it mean for me?
Cloud Pak for Data platform 4.6.4

Version 4.6.4 of the Cloud Pak for Data platform includes the following features and updates:

New resource specification injection (RSI) patch types
You can now create RSI patches that define:
init containers for pods
An init container runs before the app containers in a pod. The container typically contains utilities or setup scripts that are not present in the app images.
sidecar containers for pods
A sidecar container runs alongside the app containers in the pod. You can use a sidecar container to enhance and extend the primary container without modifying the primary container.

For details, see Customizing pods by creating a resource specification injection patch.

Version 4.6.4 of the platform includes various fixes.

For details, see What's new and changed in the platform.

Related documentation:
Cloud Pak for Data command-line interface (cpd-cli) 12.0.4

The 12.0.4 release of the Cloud Pak for Data command-line interface includes the following features and updates:

cpd-cli oadp plug-in
You can now use the cpd-cli oadp plug-in to run online backup and restore commands on Red Hat OpenShift Container Platform Version 4.12.

Version 12.0.4 of the Cloud Pak for Data command-line interface includes various fixes.

Cloud Pak for Data common core services 6.4.0
The 6.4.0 release of the common core services includes changes to support features and updates in Watson Studio and Watson Knowledge Catalog.
Version 6.4.0 of the common core services includes the following features and updates:
Access more data with the new Presto connection
You can now work with data from Presto data sources.
Use a proxy server for Microsoft Azure Data Lake Store connections
You can now select a proxy server for a new or existing Microsoft Azure Data Lake Store connection. A proxy server can provide load balancing, increased security, and privacy for the connection. For details, see Microsoft Azure Data Lake Store connection.

Version 6.4.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

If you install or upgrade a service that requires the common core services, the common core services will also be installed or upgraded.

Cloud Pak for Data scheduling service 1.10.0

Version 1.10.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Related documentation:
AI Factsheets 4.6.4

The 4.6.4 release of AI Factsheets includes the following features and updates:

Save external models to the catalog of your choice
You can now save an external model to any catalog that you can access. (Previously, you could only save external models to the Platform assets catalog.) For details, see Adding an external model to the model inventory.

Version 4.6.4 of the AI Factsheets service includes various fixes.

Related documentation:
AI Factsheets
Analytics Engine Powered by Apache Spark 4.6.4

The 4.6.4 release of Analytics Engine Powered by Apache Spark includes the following features and updates:

Python 3.10 on Power® and Linux on IBM Z and IBM LinuxONE
Previously, Python 3.10 was available only on x86-64 systems. Starting with Version 4.6.4, Python 3.10 is available on Power and Linux on IBM Z and IBM LinuxONE .

For details, see Capabilities on Linux on IBM Z and IBM LinuxONE.

Version 4.6.4 of the Analytics Engine Powered by Apache Spark service includes various fixes.

For details, see What's new and changed in Analytics Engine Powered by Apache Spark.

Related documentation:
Analytics Engine Powered by Apache Spark
Cognos Analytics 23.4.0

The 23.4.0 release of Cognos Analytics includes the following features and updates:

Updated version of the Cognos Analytics software
This release of the Cognos Analytics service provides Version 11.2.4 IF 1003 of the Cognos Analytics software. For details, see Release 11.2.4 - New and changed features.

Version 23.4.0 of the Cognos Analytics service includes various fixes.

Related documentation:
Cognos Analytics
Cognos Dashboards 4.6.4

Version 4.6.4 of the Cognos Dashboards service includes various fixes.

For details, see What's new and changed in Cognos Dashboards.

Related documentation:
Cognos Dashboards
Data Privacy 4.6.4

Version 4.6.4 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Related documentation:
Data Privacy
Data Refinery 6.4.0

Version 6.4.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Related documentation:
Data Refinery
Data Replication 4.6.4

Version 4.6.4 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Related documentation:
Data Replication
DataStage 4.6.4

The 4.6.4 release of DataStage includes the following features and updates:

Use Extract, Load, and Transform (ELT) run mode to run SQL queries more efficiently
The ELT process is different from the traditional Extract, Transform, and Load (ETL) process in that it runs the transform part of the process in the target database, which can be more efficient and cost effective.

When you use ELT run mode, DataStage analyzes your DataStage flow and runs it in ELT mode, mixed ETL and ELT mode, or ETL mode, depending on the analysis.

For details, see ELT run mode in DataStage.

Read storage data from multiple binary files with the Amazon S3 and Generic S3 connectors
The Amazon S3 and Generic S3 connectors have a new Read mode option that you can use if you want to specify a wildcard character in the file name for binary data.

If you use this option, you can read multiple binary files one after another, and each file will be read as a record.

Screen capture of the DataStage Amazon S3 connector panel with the Read mode field set to Read binary data from multiple files using wildcards selected.
For information about the Read modes, see the Amazon S3 connector or the Generic S3 connector.

Version 4.6.4 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Db2 4.6.4

The 4.6.4 release of Db2 includes the following features and updates:

Audit logging for Db2 databases
You can now create and manage customized audit policies for each Db2 database instance. You can use the individualized audit policies to monitor data and detect concerning behaviors.

For more information, see Configuring audit logging for Db2.

Version 4.6.4 of the Db2 service includes various fixes.

Related documentation:
Db2
Db2 Big SQL 7.4.4

Version 7.4.4 of the Db2 Big SQL service includes various fixes.

Related documentation:
Db2 Big SQL
Db2 Data Gate 3.1.0

Version 3.1.0 of the Db2 Data Gate service includes various fixes.

For details, see What's new and changed in Db2 Data Gate.

Related documentation:
Db2 Data Gate
Db2 Data Management Console 4.6.4

Version 4.6.4 of the Db2 Data Management Console service includes various fixes.

For details, see What's new and changed in Db2 Data Management Console.

Related documentation:
Db2 Data Management Console
Db2 Warehouse 4.6.4

The 4.6.4 release of Db2 Warehouse includes the following features and updates:

Audit logging for Db2 Warehouse databases
You can now create and manage customized audit policies for each Db2 Warehouse database instance. You can use the individualized audit policies to monitor data and detect concerning behaviors.

For more information, see Configuring audit logging for Db2 Warehouse.

Version 4.6.4 of the Db2 Warehouse service includes various fixes.

Related documentation:
Db2 Warehouse
Decision Optimization 6.4.0

The 6.4.0 release of Decision Optimization includes the following features and updates:

Deprecation notice for Python 3.9
Python 3.9 is deprecated and will be removed in an upcoming refresh.

Start using Python 3.10 for building and deploying Decision Optimization models. You can change your Python version for existing deployed models by using the REST API.

Version 6.4.0 of the Decision Optimization service includes various fixes.

For details, see What's new and changed in Decision Optimization.

Related documentation:
Decision Optimization
EDB Postgres 12.14, 13.10, 14.7

This release of EDB Postgres includes the following features and updates:

New database versions
You can use this release of EDB Postgres to create and work with the following database versions:
  • 12.14
  • 13.10
  • 14.7

This release of the EDB Postgres service includes various fixes.

Related documentation:
EDB Postgres
Execution Engine for Apache Hadoop 4.6.4

Version 4.6.4 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Related documentation:
Execution Engine for Apache Hadoop
IBM Match 360 2.3.28

The 2.3.28 release of IBM Match 360 includes the following features and updates:

Enhanced control over how entities select their attribute values from member records
Attribute composition rules now give data engineers finer control over how each master data entity's attribute values are selected from its member records. For example, you can specify whether the service prioritizes data that was more recently updated or data that appears more frequently across member records. If your organization has certain trusted record sources, you can specify that those sources should be considered the gold standard for your entity data. By defining and prioritizing a set of rules, you now have more control over which record attribute values are surfaced to the entity.

For details, see Defining attribute composition rules.

Connect relationship data by using the IBM Match 360 platform connection
You can now use IBM Match 360 connections to import and export relationship data between the IBM Match 360 service and connected data assets, catalogs, DataStage flows, or Data Refinery flows. Relationship data helps you to explore the associations and interconnections between records to gain new insights about your master data.

For details about the IBM Match 360 connection type, see IBM Match 360 connection.

Version 2.3.280 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Related documentation:
IBM Match 360 with Watson
Informix 5.2.0

Version 5.2.0 of the Informix service includes various fixes.

Related documentation:
Informix
MongoDB 4.4.0, 5.0.13, 6.0.2

This release of the MongoDB service include various fixes.

Related documentation:
MongoDB
OpenPages® 8.302.1

Version 8.302.1 of the OpenPages service includes various fixes.

Related documentation:
OpenPages
Planning Analytics 4.6.4

The 4.6.4 release of Planning Analytics includes the following features and updates:

Updated versions of Planning Analytics software
This release of the Planning Analytics service provides the following software versions:
  • TM1 Version 2.0.9.16

    For details about this version of the software, see Planning Analytics 2.0.9.16 in the Planning Analytics documentation.

  • Planning Analytics Workspace Version 2.0.84.

    For details about this version of the software, see 2.0.84 - What's new in the Planning Analytics Workspace documentation.

  • Planning Analytics Spreadsheet Services Version 2.0.83

    For details about this version of the software, see 2.0.83 - Feature updates in the TM1 Web documentation.

  • Planning Analytics for Microsoft Excel Version 2.0.83

    For details about this version of the software, see 2.0.83 - Feature updates in the Planning Analytics for Microsoft Excel documentation.

Version 4.6.4 of the Planning Analytics service includes various fixes.

Related documentation:
Planning Analytics
Product Master 3.3.0

Version 3.3.0 of the Product Master service includes various fixes.

For details, see What's new and changed in Product Master.

Related documentation:
Product Master
RStudio Server Runtimes 6.4.0

Version 6.4.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Related documentation:
RStudio Server Runtimes
SPSS Modeler 6.4.0

Version 6.4.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Related documentation:
SPSS Modeler
Watson Knowledge Catalog 4.6.4

The 4.6.4 release of Watson Knowledge Catalog includes the following features and updates:

Adding relationships between assets and artifacts
Now, you can add and manage relationships between catalog assets and governance artifacts.

For example, you can identify which rules an asset adheres to by adding a previously defined custom relationship, such as Adheres to, between an asset and the classification.

To learn more about relationships, see Asset relationships in catalogs.

Create queries, reports, or dashboards based on custom relationships
When you create custom relationships between assets and governance artifacts, the relationships are synced to Watson Knowledge Catalog reporting data mart, so that you can create reports.
For example, you can use the custom relationships reporting to:
  • Obtain quality analytics at various levels of granularity (by domain, by metadata, by user, by team)
  • Certify the data quality of your data
  • Count the number of assets that have a specific privacy property

To learn how to create custom relationships, see Custom properties and relationships for governance artifacts and catalog assets.

To learn how to create reports, see Setting up reporting for Watson Knowledge Catalog.

View custom relationships on lineage graph
You can now view custom relationships between assets as lineage flows in the lineage graph. For details, see Exploring business lineage.
Define a composite key for a reference data set
You can now specify multiple columns to create a composite key for your reference data sets. Without a composite key, reference data values in a set are identified by a unique string in the code column. A composite key is a combination of the code column and up to 5 custom columns in a reference data set.

A composite key is used to uniquely identify each reference data value. With a composite key, the values in the code column no longer need to be unique. Uniqueness is guaranteed only when the values of all the specified columns are combined. For details, see Designing reference data sets.

Drill down into the details of profiling results
You can now access detailed profiling information from within a metadata enrichment or from an asset’s Profile tab in a project or a catalog. For each column, view statistical information about the column data, information about data classes, data types and formats, and the frequency distribution of values in the column. For the statistical information, you can also choose between several types of visualizations.
Statistical information for continuous data
Statistical information for nominal data

For details, see Column-level profile details.

Import lineage from additional data sources
You can now run metadata imports to get lineage data from Amazon Redshift data sources. For details, see Supported data sources for metadata import, metadata enrichment, and data quality rules.
Run data quality rules on additional data sources
You can now run data quality rules on data assets from the following data sources:
  • Watson Query
  • Microsoft Azure Data Lake Store
  • Snowflake

For details, see Supported data sources for metadata import, metadata enrichment, and data quality rules.

Run advanced analyses on enriched assets
When you run metadata enrichment, you can run the following analyses:
Primary key analysis
You can run a primary key analysis to detect primary keys in your data that uniquely identify each record in a data asset. For details, see Identifying primary keys.
Relationship analysis
You can run a relationship analysis to identify relationships between data asset or to find overlapping and redundant data in columns. For details, see Identifying relationships.
New option for binding variables in data quality rules
You can now use job parameters to bind rule variables to data columns and manage those parameters centrally in a project. With this option, you no longer need to update the rules when you want to change the binding to a different column. For details, see Creating rules from data quality definitions.

Version 4.6.4 of the Watson Knowledge Catalog service includes various fixes.

For details, see What's new and changed in Watson Knowledge Catalog.

Related documentation:
Watson Knowledge Catalog
Watson Machine Learning 4.6.4

The 4.6.4 release of Watson Machine Learning includes the following features and updates:

Federated Learning runs on Mac computers with M-series chips
Run your Federated Learning experiments on M1 Mac and M2 Mac computers in the latest runtime. For detailed requirements, see Set up your system.
Runtime 22.1 with Python 3.9 deprecated
Train and deploy your machine learning assets by using the latest frameworks and software specification. For details, see Supported software specifications and frameworks.

Version 4.6.4 of the Watson Machine Learning service includes various fixes.

Related documentation:
Watson Machine Learning
Watson Machine Learning Accelerator 3.4.0

Version 3.4.0 of the Watson Machine Learning Accelerator service includes various fixes.

Related documentation:
Watson Machine Learning Accelerator
Watson OpenScale 4.6.4

Version 4.6.4 of the Watson OpenScale service includes various fixes.

For details, see What's new and changed in Watson OpenScale.

Related documentation:
Watson OpenScale
Watson Pipelines 4.6.4

The 4.6.4 release of Watson Pipelines includes the following features and updates:

Integrate more assets from Git-based projects into your pipelines
Now your pipeline can access and run Python and R scripts, notebooks, and Data Refinery jobs from Git-based projects. For details, see Configuring pipeline nodes.
Create multiple Watson Pipelines instances on a cluster
You can now install Watson Pipelines multiple times on the same cluster for different projects. See Multitenancy support.

Version 4.6.4 of the Watson Pipelines service includes various fixes.

Related documentation:
Watson Pipelines
Watson Query 2.0.4

Version 2.0.4 of the Watson Query service includes various fixes.

For details, see What's new and changed in Watson Query.

Related documentation:
Watson Query
Watson Speech services 4.6.4

The 4.6.4 release of Watson Speech services includes features, fixes, and security updates.

For a complete list of what's new and changed, see:

Related documentation:
Watson Speech services
Watson Studio 6.4.0

The 6.4.0 release of Watson Studio includes the following features and updates:

Deprecation of all Runtime 22.1 environments
The Runtime 22.1 environments on R 3.6 and Python 3.9 (with and without GPU) are deprecated. Switch to the Runtime 22.2 environments. Runtime 22.1 will be removed in a future release.

For details on the Runtime 22.2 environments, see Environment.

Version 6.4.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Related documentation:
Watson Studio
Watson Studio Runtimes 6.4.0

The 6.4.0 release of Watson Studio Runtimes includes the following features and updates:

Deprecation of all Runtime 22.1 environments
The Runtime 22.1 environments on R 3.6 and Python 3.9 (with and without GPU) are deprecated. Switch to the Runtime 22.2 environments. Runtime 22.1 will be removed in a future release.

For details on the Runtime 22.2 environments, see Environment.

Version 6.4.0 of the Watson Studio Runtimes service includes various fixes.

For details, see What's new and changed in Watson Studio Runtimes.

Related documentation:
Watson Studio Runtimes

Version 4.6.3

Released: February 2023

This release of Cloud Pak for Data is primarily focused on defect fixes. However, this release also includes new features for services such as AI Factsheets, DataStage, OpenPages, Watson Machine Learning, and Watson Pipelines.

Important: Cloud Pak for Data 4.6.3 supports only Red Hat OpenShift Container Platform Version 4.10. For details, see Software requirements.
Software Version What does it mean for me?
Cloud Pak for Data platform 4.6.3

Version 4.6.3 of the platform includes various fixes for the IBM Cloud Pak for Data platform operator.

Related documentation:
Cloud Pak for Data command-line interface (cpd-cli) 12.0.3

Version 12.0.3 of the Cloud Pak for Data command-line interface includes various fixes.

Cloud Pak for Data common core services 6.3.0
The 6.3.0 release of the common core services includes changes to support features and updates in Watson Studio and Watson Knowledge Catalog.
Version 6.3.0 of the common core services includes the following features and updates:
Use a proxy server for Amazon S3 connections
You can now select a proxy server for an Amazon S3 connection. A proxy server can provide load balancing, increased security, and privacy for the connection. For details, see Amazon S3 connection.
High availability option for the Apache Hive connection
Select the new option, Use ZooKeeper discovery, to enter a ZooKeeper namespace and a list of alternative servers to ensure continued access to the connection. See Apache Hive connection.
New name for the setting to prevent users from retrieving unmasked sensitive credentials
The setting Prevent users from retrieving decrypted credentials has been renamed Mask sensitive credentials retrieved through API calls. This setting is in the Create connection form.

Any setting that you previously enabled remains in effect. For information on creating a connection, see Connecting to data sources.

Version 6.3.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

If you install or upgrade a service that requires the common core services, the common core services will also be installed or upgraded.

Cloud Pak for Data scheduling service 1.9.0

Version 1.9.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Related documentation:
You can install and upgrade the scheduling service along with the Cloud Pak for Data platform. For details, see:
AI Factsheets 4.6.3

The 4.6.3 release of AI Factsheets includes the following features and updates:

Track more metrics from Watson OpenScale evaluations
Additional Watson OpenScale metrics from Fairness and Global explainability and metrics from Watson OpenScale custom monitors are now synced to AI Factsheets to give you a more comprehensive view of the performance of your machine learning models. For details, see Viewing a factsheet.
Attach files to a factsheet by using the Python client
You can attach images or files that support your use case to a factsheet, using AIGovFacts Python client utility. For example, you might want to attach an ROC curve diagram, a confusion matrix table, or a PDF with a Pandas profiling report as model facts. For details see Customizing details for a factsheet.

Version 4.6.3 of the AI Factsheets service includes various fixes.

For details, see What's new and changed in AI Factsheets.

Related documentation:
AI Factsheets
Analytics Engine Powered by Apache Spark 4.6.3

Version 4.6.3 of the Analytics Engine Powered by Apache Spark service includes various fixes.

For details, see What's new and changed in Analytics Engine Powered by Apache Spark.

Related documentation:
Analytics Engine Powered by Apache Spark
Cognos Analytics 23.3.0

The 23.3.0 release of Cognos Analytics includes the following features and updates:

Upload JDBC drivers for Cognos Analytics
You can now upload JDBC drivers for the Cognos Analytics instance.

Cognos Analytics has specific JDBC driver versions that are tested and approved for use with Cognos Analytics instances. Uploading JDBC drivers for Cognos Analytics ensures that you use the correct version for Cognos Analytics rather than a version that works with Cloud Pak for Data but not necessarily with Cognos Analytics.

Use the Cognos Analytics drivers API to upload and manage the JDBC drivers for Cognos Analytics. For details, see JDBC drivers.

Updated software version for Cognos Analytics
This release of the Cognos Analytics service provides Version 11.2.4 of the Cognos Analytics software. For details about this version of the software, see Release 11.2.4 FP1 - New and changed features in the Cognos Analytics documentation.

Version 23.3.0 of the Cognos Analytics service includes various fixes.

For details, see What's new and changed in Cognos Analytics.

Related documentation:
Cognos Analytics
Cognos Dashboards 4.6.3

Version 4.6.3 of the Cognos Dashboards service includes various fixes.

For details, see What's new and changed in Cognos Dashboards.

Related documentation:
Cognos Dashboards
Data Privacy 4.6.3

The 4.6.3 release of Data Privacy includes the following features and updates:

Catalog and project asset preview performance improvement
The response speed of previewing masked data is faster when the assets contain masked data by data protection rules because data protection rules are applied synchronously to complete the masking process. Using an asynchronous job slows the asset preview response speed. However, as an exception when the data protection rules include row filtering, an asynchronous job remains to mask the data.

Version 4.6.3 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Related documentation:
Data Privacy
Data Refinery 6.3.0

Version 6.3.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Related documentation:
Data Refinery
Data Replication 4.6.3

Version 4.6.3 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Related documentation:
Data Replication
DataStage 4.6.3

The 4.6.3 release of DataStage includes the following features and updates:

Read an entire file as a single column of output from the Sequential file stage
Now you can set the formatting properties of the Sequential file stage to output the contents of an entire file in the form of one column of data. For example, instead of the Sequential file stage parsing out the columns and rows of a CSV file, it reads the entirety of the file as one long VARCHAR string. This feature is useful if you want to feed an entire XML file as a single record to the Hierarchical stage for processing.

For more information, see Sequential file.

Use the Survive stage to consolidate duplicate records
You can now use the Survive stage to group records and consolidate groups into a representative record with the best available data. The Survive stage is a Quality stage.

For more information, see Survive stage.

Connect to more data sources in DataStage
You can now include data from these data sources in your DataStage flows:
  • Cloudera Impala
  • Dremio
  • Dropbox
  • SingleStoreDB

For the full list of supported data sources, see Supported data sources in DataStage.

Enhancements for local connections
You can now perform the following actions in local connections:
  • Launch the Asset browser from within the local connection. On the connector's Stage tab, click Browse connection. Select a schema, table, or columns.
  • Preview the data from a local connection. On the connector's Output tab, go to the Usage section, and then select Preview data.

For information about local connections, see Connecting to a data source in DataStage.

Version 4.6.3 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Decision Optimization 6.3.0

Version 6.3.0 of the Decision Optimization service includes various fixes.

For details, see What's new and changed in Decision Optimization.

Related documentation:
Decision Optimization
EDB Postgres 12.13, 13.9, 14.6

Version 12.13, 13.9, 14.6 of the EDB Postgres service includes various fixes.

Related documentation:
EDB Postgres
Execution Engine for Apache Hadoop 4.6.3

The 4.6.3 release of Execution Engine for Apache Hadoop includes the following features and updates:

Updated version of Cloudera
You can now use Cloudera Data Platform (CDP) Version 7.1.8.
Updates for Jupyter Enterprise Gateway
  • Jupyter Enterprise Gateway is upgraded from Version 2.6.0 to 3.1.0. No action is needed for this upgrade.
  • Jupyter Enterprise Gateway now supports Spark 3.3. To start a Jupyter notebook after you upgrade to Cloud Pak for Data Version 4.6.3, you must run commands as the first cell after the kernel starts. For details, see Building models with JEG.

Version 4.6.3 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Related documentation:
Execution Engine for Apache Hadoop
IBM Match 360 2.2.33

Version 2.2.330 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Related documentation:
IBM Match 360 with Watson
Informix 5.1.0
MongoDB 4.4.0, 5.0.13, 6.0.2

Versions 4.4.0, 5.0.13, 6.0.2 of the MongoDB service include various fixes.

Related documentation:
MongoDB
OpenPages 8.302.0

The 8.302.0 release of OpenPages includes the following features and updates:

On-demand user creation
Users created in Cloud Pak for Data are now automatically given access to the OpenPages instance rather than waiting for the synchronization job to update its users and groups. For more information, see Managing access to OpenPages instances.
Audit logging for security events
The OpenPages service now maintains logs of all relevant security events to support threat detection and investigation activities by using the Cloud Pak for Data audit logging service. For more information about audit logging, see Enabling audit logging for OpenPages.
Improved configuration of the user synchronization schedule
You can now configure the schedule of the user synchronization job by using the OpenPagesInstance custom resource. For more information, see Configuring the user and group synchronization job.
New features from OpenPages with Watson 8.3.0.2
The OpenPages service includes enhancements that were introduced in OpenPages with Watson 8.3.0.2. Highlights include:
Custom workflow stage notifications
You can now edit and create email notification templates within the Workflow Designer and assign templates to workflow stages. For more information, see Custom workflow stage notifications in 8.3.0.2.
IBM OpenPages Risk Management for ESG
The new IBM OpenPages Risk Management for ESG solution helps organizations govern and manage their Environmental, Social, and Governance (ESG) programs. For more information, see IBM OpenPages Risk Management for ESG in the OpenPages with Watson documentation.
Enhancements to AI model integration
  • The Watson Machine Learning Integration was renamed to Custom Machine Learning Model Integration.
  • You can now use the Custom Machine Learning Model Integration to connect to Watson Natural Language Understanding on IBM CloudIBM® Cloud for custom classification models. For more information, see Custom Machine Learning Models.

Enhancements to Ascent integration for IBM OpenPages Regulatory Compliance Management
The Ascent integration has been enhanced with the ingestion of Requirement Versions. The Requirement Version object details previous and future versions of the Requirement regulatory text. The Requirement Version is a child of Requirement. The Requirement Version object will track the regulatory changes over time. For more information, see Enhancements to IBM OpenPages Regulatory Compliance Management in 8.3.0.2.

You can read about the these enhancements and more in the OpenPages with Watson documentation.

Version 8.302.0 of the OpenPages service includes various fixes.

Related documentation:
OpenPages
Planning Analytics 4.6.3

The 4.6.3 release of Planning Analytics includes the following features and updates:

Updated versions of Planning Analytics software
This release of the Planning Analytics service provides the following software versions:
  • Planning Analytics Workspace Version 2.0.83

    For details about this version of the software, see What's new in the Planning Analytics Workspace documentation.

  • Planning Analytics Spreadsheet Services 2.0.82 IF

    For details about this version of the software, see Feature updates in the TM1 Web documentation.

Version 4.6.3 of the Planning Analytics service includes various fixes.

Related documentation:
Planning Analytics
Product Master 3.2.0

Version 3.2.0 of the Product Master service includes various fixes.

For details, see What's new and changed in Product Master.

Related documentation:
Product Master
RStudio Server Runtimes 6.3.0

Version 6.3.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Related documentation:
RStudio Server Runtimes
SPSS Modeler 6.3.0

Version 6.3.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Related documentation:
SPSS Modeler
Watson Assistant 4.6.3

Version 4.6.3 of the Watson Assistant service includes various security fixes.

For details, see What's new and changed in Watson Assistant.

Related documentation:
Watson Assistant
Watson Discovery 4.6.3

Version 4.6.3 of the Watson Discovery service includes various fixes.

Related documentation:
Watson Discovery
Watson Knowledge Catalog 4.6.3

The 4.6.3 release of Watson Knowledge Catalog includes the following features and updates:

Metadata import enhancements
New options for metadata import
To ensure that the project or catalog where you are importing metadata doesn't contain stale data, you can specify which assets are deleted when you rerun the import. You can choose to delete the following types of assets:
  • Assets that are no longer available in the data source.
  • Assets that were removed from the import scope.

For details, see Importing metadata.

Import from additional data sources
You can now run metadata import on the following connections to discover data:
  • Amazon Redshift
  • FTP

For details, see Supported data sources for metadata import, metadata enrichment, and data quality rules.

Metadata enrichment changes and enhancements
New enrichment settings for term assignment
For each project, you can choose whether term rejections should be considered when the confidence score for a term assignment or suggestion is calculated. When this option is enabled, the confidence values of all selected term assignment values are adjusted.
In addition, you can set the training scope for the machine learning models that are used in the project without selecting the machine learning method for term assignment. Depending on the project settings, the training scope applies to one or both of the following models:
  • The build-in model for term assignment
  • The model for adjusting scores

For details, see Default enrichment settings.

Test model configuration
When you set up metadata enrichment to use a custom service for automatic term assignment, you can now test the service configuration. For details, see Adding a custom service for automatic term assignment.
New features for data quality rules in projects
Use more than one data quality definition in a single data quality rule
You can now use more than one data quality definition to build your rule. In addition, you can include an individual definition more than once to apply the same definition to different columns. For details, see Creating rules from data quality definitions.
Download rule output as CSV file
If an output table is defined for a rule, you can also download the rule output as a CSV file from the run history. This enables you to work with the output in other programs, such a spreadsheet program, especially if you want to search and filter output that contains a large number of records. For details, see Checking the run history.
Export and import data quality information
When you export a project, you can now include data quality assets. For details, see Exporting a project.

In addition, you can now export and import data quality assets by using the IBM Cloud Pak for Data export and import utility (the cpd-cli export-import command). For details, see Migrating catalog assets.

Run rules on data from additional data sources
You can now run data quality rules on data from the following data sources:
  • Amazon Redshift
  • Greenplum

For details, see Supported data sources for metadata import, metadata enrichment, and data quality rules.

Send reporting data to Oracle databases
When you send your Watson Knowledge Catalog data to an external database to generate reports, you can now choose an Oracle database, in addition to PostgreSQL and Db2 databases. For details, see Setting up reporting on Watson Knowledge Catalog.
Manage custom relationships with the Watson Knowledge Catalog APIs
Create and manage custom relationships between governance artifacts and assets from projects, catalogs, and spaces with the Watson Knowledge Catalog APIs.
Capture lineage for existing catalog assets with the Watson Knowledge Catalog APIs
Lineage can now be calculated for custom assets that existed in a catalog before the lineage feature was enabled. Use the Watson Data APIs to generate and load existing asset lineage information. For more information on bulk loading assets, see Exploring business lineage.
Monitor authoring tasks
Authoring tasks for governance artifacts are now shown in task inbox and the Task status page. When a category collaborator creates a draft for a governance artifact, all category collaborators with roles Owner, Admin, or Editor can see a new Authoring task in their task inbox. If you claim an authoring task, it disappears from others' inboxes. For more information, see Authoring task type.

Version 4.6.3 of the Watson Knowledge Catalog service includes various fixes.

For details, see What's new and changed in Watson Knowledge Catalog.

Related documentation:
Watson Knowledge Catalog
Watson Knowledge Studio 4.8.0

Version 4.8.0 of the Watson Knowledge Studio service includes various fixes.

Related documentation:
Watson Knowledge Studio
Watson Machine Learning 4.6.3

The 4.6.3 release of Watson Machine Learning includes the following features and updates:

SingleStoreDB available for AutoAI experiments
Use SingleStoreDB as a data source for training or deploying AutoAI models. For details, see Batch deployment input details for AutoAI models.
Train Deep Learning experiments with CPU resources
You can now specify CPU as an alternative to GPU as a resource type when defining a Deep Learning experiment. You can then assign the number of workers to apply for the experiment. For details, seeTraining neural networks using the deep learning experiment builder.

Version 4.6.3 of the Watson Machine Learning service includes various fixes.

For details, see What's new and changed in Watson Machine Learning.

Related documentation:
Watson Machine Learning
Watson Machine Learning Accelerator 3.3.0

The 3.3.0 release of Watson Machine Learning Accelerator includes the following features and updates:

Train Deep Learning experiments with CPU resources
You can now specify CPU as an alternative to GPU as a resource type when defining a Deep Learning experiment. You can then assign the number of workers to apply for the experiment. For details, see Training neural networks using the deep learning experiment builder.

Version 3.3.0 of the Watson Machine Learning Accelerator service includes various fixes.

Related documentation:
Watson Machine Learning Accelerator
Watson OpenScale 4.6.3

Version 4.6.3 of the Watson OpenScale service includes various fixes.

Related documentation:
Watson OpenScale
Watson Pipelines 4.6.3

The 4.6.3 release of Watson Pipelines includes the following features and updates:

Run jobs for Python scripts and code packages
Orchestrate scripts and code package jobs in projects and deployment spaces to expand the capabilities of your pipeline flows. For details, see Configuring pipeline nodes.
Easily access cached results from bash script nodes
If you re-run a bash script node and the run fails, you can access the cached results of your last successful run from the web client. Previously, you needed to use the command prompt to access the cached results. For more information on enabling node caching, see Node caching.

Version 4.6.3 of the Watson Pipelines service includes various fixes.

For details, see What's new and changed in Watson Pipelines.

Related documentation:
Watson Pipelines
Watson Speech services 4.6.3

The 4.6.3 release of Watson Speech services includes features, bug fixes, and security updates.

For a list of what's new and changed, see:

Version 4.6.3 of the Watson Speech services service includes various fixes.

For details, see What's new and changed in Watson Speech services.

Related documentation:
Watson Speech services
Watson Studio 6.3.0

The 6.3.0 release of Watson Studio includes the following features and updates:

Securely store the API key for the Watson Studio extension for Visual Studio Code
You can store the API key that you use for the Watson Studio extension for Visual Studio Code in your operating system keychain. Storing your API key this way means that you do not need to re your API key each time you restart VS Code. For details, see Watson Studio extension for Visual Studio Code.
New built-in models in the Natural Language Processing library
You can start using the following built-in Natural Language Processing models when analyzing data in Python notebooks:
Hierarchical text categorization block
Use the Hierarchical text categorization block to determine the topics of documents on the web. The block categorizes web pages into a taxonomy of general domain topics. For details, see Hierarchical text categorization block.
Relations extraction block
Use the Relations extraction block to assign relations between discovered entities in text. For details, see Relations extraction block.

Version 6.3.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Related documentation:
Watson Studio
Watson Studio Runtimes 6.3.0

The6.3.0 release of Watson Studio Runtimes includes the following features and updates:

New built-in models in the Natural Language Processing library
You can start using the following built-in Natural Language Processing models when analyzing data in Python notebooks:
Hierarchical text categorization block
Use the Hierarchical text categorization block to determine the topics of documents on the web. The block categorizes web pages into a taxonomy of general domain topics. For details, see Hierarchical text categorization block.
Relations extraction block
Use the Relations extraction block to assign relations between discovered entities in text. For details, see Relations extraction block.

Version 6.3.0 of the Watson Studio Runtimes service includes various fixes.

For details, see What's new and changed in Watson Studio Runtimes.

Related documentation:
Watson Studio Runtimes

Version 4.6.2

Released: January 2023

This release of Cloud Pak for Data is primarily focused on defect fixes. However, this release also includes new features for services such as Cognos Dashboards, DataStage, Db2, Watson Assistant, and Watson Discovery.

This release also includes the first release of the Voice Gateway service on Cloud Pak for Data Version 4.6.

Software Version What does it mean for me?
Cloud Pak for Data platform 4.6.2

Version 4.6.2 of the platform includes various fixes.

For details, see What's new and changed in the platform.

Related documentation:
Cloud Pak for Data command-line interface (cpd-cli) 12.0.2

The 12.0.2 release of the Cloud Pak for Data command-line interface includes the following features and updates:

New command to create service instances
The cpd-cli service-instance plug-in includes a new create command that creates Cloud Pak for Data service instances from a JSON request file. The JSON file can store information about different contexts, where each context can refer to a different Cloud Pak for Data service environment. If your service uses the cpd-cli to manage service instances, refer to your specific service documentation for JSON template configuration information.

Version 12.0.2 of the Cloud Pak for Data command-line interface includes various fixes.

Cognos Dashboards 4.6.2

The 4.6.2 release of Cognos Dashboards includes the following features and updates:

Adjust the size of the dashboard
When the dashboard is in View mode, you can use the Zoom icon in the toolbar to adjust the size of the dashboard canvas.

For details, see Zoom levels on the dashboard canvas in the Cognos Analytics documentation.

New zoom bar in visualizations
You can now enable a zoom bar for the following visualizations:
  • Bubble
  • Line and column
  • Scatter

When this property is enabled, a bar with a left and right handle is added to the chart. You can drag the handle on the bar to zoom horizontally.

When the zoom bar is enabled, mouse scrolling is disabled.

For details, see Zoom bar in bubble, line and column, and scatter visualizations in the Cognos Analytics documentation.

Specify bubble size limits in bubble and scatter visualizations
In bubble visualizations, you specify the minimum and maximum values for the bubble size.

In scatter visualizations, all points in the scatter visualization display at the size you specify. The default bubble size in scatter visualizations is 3px.

For details, see Bubble size limits in the Cognos Analytics documentation.

Alignment position for zero in line and column visualizations
In line and column visualizations, you can use Align axes zero option to control whether the line and column sections have zero in the same position.

If you disable the zero alignment poison, the line and column values are independent, which can make the relevant values more visible in some scenarios.

For details, see Alignment position for zero in line and column visualizations in the Cognos Analytics documentation.

Handling missing values in line visualizations
You can specify how lines display missing values on line and line and column visualizations. You can choose from the following options:
Interpolate
The line displays an interpolated value based on adjacent data points. This is the default option.
Show as gaps
The line displays a gap between the adjacent data points.
Show as zero
The line displays missing values as zero.

For details, see Handling missing values in line visualizations in the Cognos Analytics documentation.

Showing value labels in visualizations
You can show value labels in line visualizations and in line and column visualizations so that the data values are clearer. You can also customize the format of the value labels.

For details, see Showing value labels in visualizations in the Cognos Analytics documentation.

Top/bottom calculations can no longer reference different fact tables
You can no longer create a top/bottom calculation that references a fact table that is different from the fact table for the metadata items that are used in the visualization.

Version 4.6.2 of the Cognos Dashboards service includes various fixes.

For details, see What's new and changed in Cognos Dashboards.

Related documentation:
Cognos Dashboards
DataStage 4.6.2

The 4.6.2 release of DataStage includes the following features and updates:

Import and export more DataStage objects from your projects
You can now import and export the following DataStage objects from your Cloud Pak for Data projects:
  • Data Quality assets from Watson Knowledge Catalog
  • DataStage subflows from the DataStage canvas
  • Bulk pipeline flows

For more information, see Downloading and importing a DataStage flow and its dependencies.

Version 4.6.2 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Db2 4.6.2

The 4.6.2 release of Db2 includes the following features and updates:

New capabilities for deploying instances from the web client
You now have more control if you use the web client to deploy database instances. The following options are now available from the web client:
  • You can disable the non-SSL port to prevent non-SSL connections to the database instance.
  • You can create a separate persistent volume for Db2 archive logs.

For details, see Creating a Db2 deployment on the cluster.

Components updated to Python 3
Db2 components that use Python now use Python 3. Any Python 2 scripts that you created are no longer compatible, and you need to update them to work with Python 3.

Version 4.6.2 of the Db2 service includes various fixes.

For details, see What's new and changed in Db2.

Related documentation:
Db2
Db2 Big SQL 7.4.2

Version 7.4.2 of the Db2 Big SQL service includes various fixes.

Related documentation:
Db2 Big SQL
Db2 Data Gate 3.1.0

Version 3.1.0 of the Db2 Data Gate service includes various fixes.

For details, see What's new and changed in Db2 Data Gate.

Related documentation:
Db2 Data Gate
Db2 Data Management Console 4.6.2

Version 4.6.2 of the Db2 Data Management Console service includes various fixes.

For details, see What's new and changed in Db2 Data Management Console.

Related documentation:
Db2 Data Management Console
Db2 Warehouse 4.6.2

The 4.6.2 release of Db2 Warehouse includes the following features and updates:

New capabilities for deploying instances from the web client
You now have more control if you use the web client to deploy database instances. The following options are now available from the web client:
  • You can disable the non-SSL port to prevent non-SSL connections to the database instance.
  • You can create a separate persistent volume for Db2 archive logs.

For details, see Creating a Db2 Warehouse deployment on the cluster.

Components updated to Python 3
Db2 components that use Python now use Python 3. Any Python 2 scripts that you created are no longer compatible, and you need to update them to work with Python 3.

Version 4.6.2 of the Db2 Warehouse service includes various fixes.

For details, see What's new and changed in Db2 Warehouse.

Related documentation:
Db2 Warehouse
Informix 5.1.0

Version 5.1.0 of the Informix service includes various fixes.

Related documentation:
Informix
Voice Gateway 1.0.8

Voice Gateway is now available on Cloud Pak for Data Version 4.6.

Version 4.6.2 of the Voice Gateway service includes various fixes.

Related documentation:
Voice Gateway
Watson Assistant 4.6.2

The 4.6.2 release of Watson Assistant includes the following features and updates:

New Watson Assistant v2 APIs
Watson Assistant provides new methods related to assistants, skills, environments, and releases. For details, see the Watson Assistant v2 API documentation.

Version 4.6.2 of the Watson Assistant service includes various security fixes.

For details, see What's new and changed in Watson Assistant.

Related documentation:
Watson Assistant
Watson Discovery 4.6.2

The 4.6.2 release of Watson Discovery includes features and updates.

Updated built-in enrichments
The version of the built-in Watson natural language processing enrichments was updated.
Use custom entity models in other projects
Now you can use the models that you build with the entity extractor tool in other projects. You can download any custom entity model and upload it as a machine learning model enrichment that you can apply to your collections.
Use a Watson Knowledge Studio corpus as the starting point for entity extractor model training
You can import a corpus of documents that were annotated in Watson Knowledge Studio to use as the training data for an entity extractor in Watson Discovery.

When you import the ZIP file that you exported from Watson Knowledge Studio, any entities that are annotated in the ground truth are added as entity types to the entity extractor workspace.

Entity subtypes, relations, and custom dictionaries that are associated with the corpus in Watson Knowledge Studio are not represented in Watson Discovery. The annotated documents are stored with the entity extractor workspace, not as a new collection in the project. You can continue to annotate the imported documents when you customize the entity extractor model.

For details, see Entity extractor in the Watson Discovery product documentation on IBM Cloud.

Version 4.6.2 of the Watson Discovery service includes various fixes.

Related documentation:
Watson Discovery
Watson Knowledge Studio 4.7.0

Version 4.7.0 of the Watson Knowledge Studio service includes various fixes.

Related documentation:
Watson Knowledge Studio
Watson Query 2.0.2

The 2.0.2 release of Watson Query includes the following features and updates:

Pass-through authentication
Tech preview This is a technology preview and is not supported for use in production environments.

You can now use pass-through authentication when you import connections to Watson Query. When you connect to and interact with data sources, you can use the personal credentials that you have associated with a platform connection, rather than using shared credentials for one user account.

For more information, see Adding connections from existing data source connections to Watson Query by using personal credentials.

Version 2.0.2 of the Watson Query service includes various fixes.

For details, see What's new and changed in Watson Query.

Related documentation:
Watson Query
Watson Speech services 4.6.2

The 4.6.2 release of Watson Speech to Text includes features and updates.

For a list of new features in Watson Speech to Text, see the Watson Speech to Text release notes for IBM Cloud Pak for Data.

Version 4.6.2 of the Watson Speech to Text service includes various fixes.

For details, see What's new and changed in Watson Speech to Text.

Related documentation:
Watson Speech services

Version 4.6.1

Released: December 2022

This release of Cloud Pak for Data is primarily focused on defect fixes. However, this release also includes new features for services such as DataStage, IBM Match 360, Watson Machine Learning, and Watson Knowledge Catalog.

Software Version What does it mean for me?
Cloud Pak for Data platform 4.6.1

Version 4.6.1 of the platform includes various fixes for the IBM Cloud Pak for Data platform operator.

Related documentation:
Cloud Pak for Data command-line interface (cpd-cli) 12.0.1

The 12.0.1 release of the Cloud Pak for Data command-line interface includes the following features and updates:

New cpd-cli manage commands to move images
The cpd-cli manage plug-in includes new commands to help you move images.

The commands simplify the process of moving images that are required to run some of the commands for various cpd-cli plug-ins. For details, see Moving images for cpd-cli plug-ins to a private container registry.

The commands can also be used to move other images as needed. For details, see the following command reference information:

Version 12.0.1 of the Cloud Pak for Data command-line interface includes various fixes.

Cloud Pak for Data common core services 6.1.0
The 6.1.0 release of the common core services includes changes to support features and updates in Watson Studio and Watson Knowledge Catalog.
Version 6.1.0 of the common core services includes the following features and updates:
Prevent users from retrieving decrypted credentials from connection properties
As an extra security measure when you create or edit a connection, you can enable a setting that prevents users, including the owner of the connection, from accessing the credentials through API calls.
Screen capture of the new setting: Prevent users from retrieving decrypted credentials.

This setting is independent of vaults.

Version 6.1.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

If you install or upgrade a service that requires the common core services, the common core services will also be installed or upgraded.

Cloud Pak for Data scheduling service 1.8.0

The 1.8.0 release of the scheduling service includes the following features and updates:

FIPS 140-2 compliance
The scheduling service is now FIPS 140-2 compliant.
Schedule workloads on Nvidia Multi-Instance GPUs
If your environment contains Nvidia Multi-Instance GPUs, the scheduling service share policy and quota policy can automatically schedule workloads on the available partitions.

Version 1.8.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.
Related documentation:
You can install and upgrade the scheduling service along with the Cloud Pak for Data platform. For details, see:
AI Factsheets 4.6.1

The 4.6.1 release of AI Factsheets includes the following features and updates:

Integrate AI Factsheets with IBM OpenPages
You can now integrate AI Factsheets with an external instance of IBM OpenPages. If you are already using an external instance of OpenPages as part of your AI Governance strategy, you no longer need to provision an instance of OpenPages in IBM Cloud Pak for Data. See Enabling integration with an external instance of IBM OpenPages for details.

Version 4.6.1 of the AI Factsheets service includes various fixes.

Related documentation:
AI Factsheets
Analytics Engine Powered by Apache Spark 4.6.1

The 4.6.1 release of Analytics Engine Powered by Apache Spark includes the following features and updates:

Python 3.10 and R 4.2
You can now use Python 3.10 and R 4.2 when you create Spark kernels and applications. You can use Python 3.10 and R 4.6 on x86-64 hardware only.
New V4 API for instance resource quota
The V4 instance resource quota APIs are now available. To use these APIs, you must install the Cloud Pak for Data scheduling service.

The V4 APIs take advantage of queuing and priority setting mechanisms in the scheduling service, which makes the V4 APIs more robust than the V3 resource quota APIs. With the V3 APIs, applications or kernels fail if the quota is exhausted. For details, see Changing instance resource quota in Analytics Engine Powered by Apache Spark.

Version 4.6.1 of the Analytics Engine Powered by Apache Spark service includes various fixes.

For details, see What's new and changed in Analytics Engine Powered by Apache Spark.

Related documentation:
Analytics Engine Powered by Apache Spark
Cognos Analytics 23.1.0
Related documentation:
Cognos Analytics
Data Privacy 4.6.1

The 4.6.1 release of Data Privacy includes the following features and updates:

Some pre-defined data protection rules are now enforced when you integrate with IBM Security Guardium® Data Protection
When you integrate IBM Security Guardium with Cloud Pak for Data 4.6.1, you can specify that a subset of the predefined data protection rules from Cloud Pak for Data is enforced on IBM Security Guardium Data Protection. For more information, see Enforcing data protection rules with Guardium Data Protection.

Version 4.6.1 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Related documentation:
Data Privacy
Data Refinery 6.1.0

Version 6.1.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Related documentation:
Data Refinery
Data Replication 4.6.1

Version 4.6.1 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Related documentation:
Data Replication
DataStage 4.6.1

The 4.6.1 release of DataStage includes the following features and updates:

Operational Decision Manager (ODM) stage
You can now use the ODM stage to build and incorporate complex business rules within the context of a DataStage job. For details, see Operational Decision Manager.
More easily add scratch or resource disks
You can now edit parts of the default dynamic configuration file to use your own pool, resource, and scratch disk values. Editing the configuration file eliminates the need to create your own APT_CONFIG files. For details, see Changing values in the dynamic configuration file.
Download multiple DataStage flows in one file
You can select multiple DataStage flows to download, then bundle them into one compressed file that you can import to a different project in Cloud Pak for Data. For details, see Downloading and importing a DataStage flow and its dependencies.
Parameterize properties with local connections
Previously to connect to a data source, you had to create the connection and a connected data asset for it, and then select the connection from the associated connector in DataStage. Now you can do all these steps in DataStage. You create a local connection within the connector's Stage tab Properties panel. You no longer need to create the connection separately. The benefit of a local connection is that all the connection properties can be parameterized. Parameterized properties make it easier to migrate the flow. For example, if you move from a development environment to a production environment, you need to change only the parameter values. For details, see DataStage connectors.
Screen capture of the DataStage connection properties page with a local connection.
JDBC connector name change
The JDBC connector has been renamed to Generic JDBC. This name change aligns the connector name with the associated connection name: Generic JDBC.
Connect to Apache Hive with SSL
You can now create a connection to Apache Hive with the DataStage service when the server requires SSL. In the connection form select Port is SSL-enabled, and then enter the SSL certificate and the hostname that is in the SSL certificate. See Apache Hive connection.

Version 4.6.1 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Decision Optimization 6.1.0

Version 6.1.0 of the Decision Optimization service includes various fixes.

For details, see What's new and changed in Decision Optimization.

Related documentation:
Decision Optimization
EDB Postgres 12.12, 13.8, 14.5

This release of EDB Postgres includes the following features and updates:

New database versions
You can use this release of EDB Postgres to create and work with the following database versions:
  • 12.12
  • 13.8
  • 14.5

This release of the EDB Postgres service includes various fixes.

Related documentation:
EDB Postgres
Execution Engine for Apache Hadoop 4.6.1

Version 4.6.1 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Related documentation:
Execution Engine for Apache Hadoop
IBM Match 360 2.1.41

The 2.1.41 release of IBM Match 360 includes the following features and updates:

Master data entities are now persisted in the database
Each time the IBM Match 360 matching process runs and creates master data entities, the matching engine now saves the entities to its graph database. When an entity's attributes or composition changes for any reason, the persisted entities in the database are synchronized in near-real-time. Previously, entities were created by virtually linking matched records together by using an entity ID, rather than saving them as their own graph vertices.
With persisted entities, IBM Match 360 can now capture and store new system attributes for each entity in the system, including:
  • The dates when an entity was created, was last updated, or had its record links updated.
  • The users who created or last updated an entity.

These new system attributes enhance your ability to audit the data in IBM Match 360 to help ensure compliance with data governance rules.

Version 2.1.41 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Related documentation:
IBM Match 360 with Watson
MongoDB 4.4.0, 5.0.13, 6.0.2

This release of the MongoDB service include various fixes.

Related documentation:
MongoDB
Planning Analytics 4.6.1

The 4.6.1 release of Planning Analytics includes the following features and updates:

Updated versions of Planning Analytics Workspace and Planning Analytics Spreadsheet Services software
The 4.6.1 release provides the following software versions:
  • Planning Analytics Workspace Version 2.0.82

    For details about this version of the software, see What's new in the Planning Analytics Workspace documentation.

  • Planning Analytics Spreadsheet Services Version 2.0.82

    For details about this version of the software, see Feature updates in the TM1 Web documentation.

Version 4.6.1 of the Planning Analytics service includes various fixes.

Related documentation:
Planning Analytics
Product Master 3.1.0

Version 3.1.0 of the Product Master service includes various fixes.

For details, see What's new and changed in Product Master.

Related documentation:
Product Master
RStudio Server Runtimes 6.1.0

Version 6.1.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Related documentation:
RStudio Server Runtimes
SPSS Modeler 6.1.0

Version 6.1.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Related documentation:
SPSS Modeler
Watson Knowledge Catalog 4.6.1

The 4.6.1 release of Watson Knowledge Catalog includes the following features and updates:

New features for data quality rules in projects
You have more options when you create data quality rules:
  • When you create rules from data quality definitions, you can now choose to send output records to output links in the DataStage flow instead of writing the records to a database. For details, see Creating rules from data quality definitions.
  • You can quickly build custom data filters and data quality checks in rules by using SQL statements and run these rules on data sources that support SQL. For details, see Creating SQL-based rules.
Enhancements for asset and artifact definitions
Filter tables
You can filter governance artifacts, catalog assets and custom properties to find the item you're looking for faster.
Specify predefined values for custom properties on catalog assets
You can now create a custom property for an asset type that contains a set of predefined values. For details, see Custom properties and relationships for governance artifacts.
Bulk load lineage information to a lineage graph bulk
Starting in Version 4.6.1, you can use bulk load to synchronize large amounts of lineage data from a catalog to a lineage graph. You can use bulk load to synchronize the following information from assets in the catalog:
  • Internal details (columns for database tables or views)
  • Term assignments (for both assets and columns)
  • Data flow information that is stored in the data_lineage attribute
  • Referenced connections
  • Assigned classifications

For details, see Exploring business lineage.

New Manage governance artifacts permission
You can now give users the Manage governance artifacts permission, which enables them to view all governance artifacts in all categories, regardless of whether the users are collaborators in those categories. With this permission, users can also run all API calls for governance artifacts.
You can combine the Manage governance artifacts with the following permissions to give users full control over category and governance artifacts:
  • Manage categories
  • Access governance artifacts

For details, see Predefined roles and permissions in Cloud Pak for Data.

Column metadata now available to all users
You can now view and manage column metadata for a data asset from the Overview tab for the asset. Even if you do not have access to the data due to a data protection rule, you can still view the column metadata for the asset.
Enhancements in workflow configuration and management
Configuration properties for custom workflows
In addition to configuration properties for tasks in a workflow, you can now define additional configuration properties for a workflow as a whole. Use these properties to provide values to the workflow logic, for example:
  • Use a toggle to turn on or off some parts of a workflow.
  • Provide an api_key or endpoint URL for calling external services.
  • Provide threshold level for a decision taken by the workflow logic.
  • Provide any other configurable value that is needed by the workflow logic.

You can define workflow configuration properties in the Flowable tool as described in Designing and creating custom workflows.

Task duration
You can now set duration period for each user task in a workflow. When the date is reached, the task is labeled as Overdue. For more information, see Designing and creating custom workflows.
Adding assignees
In the task status page you can now add new assignees to the tasks. This is a helpful feature to use if you need to unblock a task when the current assignee is not available to process it. You can also filter the task list for unassigned tasks and add assignees to them. For more information see Monitoring workflow tasks.

Version 4.6.1 of the Watson Knowledge Catalog service includes various fixes.

For details, see What's new and changed in Watson Knowledge Catalog.

Related documentation:
Watson Knowledge Catalog
Watson Machine Learning 4.6.1

The 4.6.1 release of Watson Machine Learning includes the following features and updates:

Deploy Natural Language Processing with Runtime 22.2
You can now deploy Natural Language Processing models with Runtime 22.2. For details, see Supported machine learning tools, libraries, frameworks, and software specifications.
Train AutoAI experiments in Git-based projects
You can now train AutoAI experiments and save resulting models in Git-based projects. For details and restrictions, seeAutoAI overview.

Version 4.6.1 of the Watson Machine Learning service includes various fixes.

Related documentation:
Watson Machine Learning
Watson Machine Learning Accelerator 3.1.0

The 3.1.0 release of Watson Machine Learning Accelerator includes the following features and updates:

Python 3.10.6
The Watson Machine Learning Accelerator service now uses Python 3.10.6 for running deep learning frameworks.

If you have existing models, be sure to update and test your models to use the latest supported frameworks for Python 3.10.6. For details, see Supported deep learning frameworks in the Watson Machine Learning Accelerator documentation.

Version 3.1.0 of the Watson Machine Learning Accelerator service includes various fixes.

Related documentation:
Watson Machine Learning Accelerator
Watson OpenScale 4.6.1

Version 4.6.1 of the Watson OpenScale service includes various fixes.

Related documentation:
Watson OpenScale
Watson Pipelines 4.6.1

Version 4.6.1 of the Watson Pipelines service includes various fixes.

Related documentation:
Watson Pipelines
Watson Studio 6.1.0

The 6.1.0 release of Watson Studio includes the following features and updates:

Set a new limit for the number of projects each user can create
By default, there is a limit of 200 projects that each user can create. You can now increase or decrease this limit by specifying a new limit value in the Common core services operator. For details, see Post-installation tasks for the Watson Studio service.
Batch import connected data assets
You can now import multiple connected data assets from the same connection at the same time. For details, see Adding data from a connection to a project.
Runtime 22.2 with Python 3.10 includes the Watson Natural Language Processing library
The Watson Natural Language Processing library for Python is now available in the following environments:
  • Runtime 22.2 with Python 3.10
  • JupyterLab with Runtime 22.2 with Python 3.10

Select these runtimes if you want to start using Python 3.10 with Watson Natural Language Processing. For details, see Watson Natural Language Processing Library.

You can also optionally create a custom GPU environment with Python 3.10.

To create a custom environment for Watson Machine Learning, see Managing outdated software specifications or frameworks.

To create a custom environment for a Notebook, see Managing environments.

Insert to code can now access data from SingleStoreDB
You can use the Insert to code function in notebooks to access data in SingleStoreDB connections through the Flight service. For details, see Data load support.

Version 6.1.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Related documentation:
Watson Studio
Watson Studio Runtimes 6.1.0

The 6.1.0 release of Watson Studio Runtimes includes the following features and updates:

Runtime 22.2 with Python 3.10 includes the Watson Natural Language Processing library
The Watson Natural Language Processing library for Python is now available in the following environments:
  • Runtime 22.2 with Python 3.10
  • JupyterLab with Runtime 22.2 with Python 3.10

Select these runtimes if you want to start using Python 3.10 with Watson Natural Language Processing. For details, see Watson Natural Language Processing Library.

You can also optionally create a custom GPU environment with Python 3.10.

To create a custom environment for Watson Machine Learning, see Managing outdated software specifications or frameworks.

To create a custom environment for a Notebook, see Managing environments.

Version 6.1.0 of the Watson Studio Runtimes service includes various fixes.

Related documentation:
Watson Studio Runtimes

What's new in Version 4.6

Cloud Pak for Data 4.6 introduces support for NetApp Trident storage and backup and restore on additional types of persistent storage. The release also introduces a new method for applying changes to the pods in your environment.

This release includes enhancements to existing services, new services, and changes to how services are delivered:
  • AI Factsheets is a new service that replaces the Factsheets functionality in Watson Knowledge Catalog.
  • Data Virtualization is now Watson Query
  • RStudio Server with R3.6 is now RStudio Server Runtimes
  • Watson Pipelines is no longer an Early Adoption offering. The service is now generally available on Cloud Pak for Data.

Platform enhancements

The following table lists the new features that were introduced in Cloud Pak for Data Version 4.6.

What's new What does it mean for me?
New commands for shutting down and restarting services
The cpd-cli manage command now includes shutdown and restart sub-commands to make it easier to shut down and restart the Cloud Pak for Data services on your cluster.

Not all services support shut down and restart. For details, see Shutting down and restarting services.

Faster access to information about alerts and events
If you have the Administer platform, Manage platform health, or View platform health permission, you have a new Alerts card on your home page. The Alerts card provides the current number of critical and warning alerts issued by the platform. The card shows at a glance whether there are any issues that you need to investigate.
Screen capture of the new Alerts card.

From the Alerts card, you can go directly to the Alerts and events page where you can see detailed information about the alerts.

The details for each event use a new bubble chart format, which displays more data in smaller space. You can hover over each data point to get more details.
Screen shot of the new alert bubble chart format.
Forward alerts more easily
Previously, if you wanted to forward alerts to email, Slack, or SNMP, you needed to use the Alerting APIs. You can now use the Cloud Pak for Data web client to forward alerts to:
Update pods with the resource specification injection feature
You can optionally enable the resource specification injection (RSI) feature on your cluster to extend Kubernetes resources that are associated with Cloud Pak for Data by applying patches directly to pods that are associated with Kubernetes deployments, StatefulSets, replicasets, replicacontrollers, jobs, and cronjobs.

When you enable the RSI feature on your cluster, Cloud Pak for Data sets up a cluster-wide webhook that ensures that the specified patches are applied to the appropriate resources when Cloud Pak for Data are started.

You can use the RSI feature to:
  • Add environment variables to pod containers
  • Add labels to pods
  • Add annotations to pods
  • Add node affinity rules to isolate specific workloads
  • Customize resource requests and limits

For details, see Customizing pod specifications with resource specification injection.

Use NetApp Trident persistent storage
You can now install Cloud Pak for Data with NetApp Trident storage. For details about NetApp Trident, see Storage considerations.

NetApp Trident is supported for most services. For details, see Storage requirements.

Backup and restore enhancements
You can now back up and restore Cloud Pak for Data installations that use the following types of persistent storage:
  • NetApp Trident
  • Amazon Elastic File System
  • Amazon Elastic File System and Amazon Elastic Block Store

For details, see Backing up and restoring Cloud Pak for Data.

Broader adoption of the Cloud Pak for Data Audit Logging Service
Starting in Cloud Pak for Data Version 4.6, more services send information about auditable events to the Cloud Pak for Data Audit Logging Service.
Monitor user activity
Administrators can now easily check whether a user is online or offline and get information about a user's current sessions and their previous session. Administrators can use this information in tandem with audit records to identify suspicious or unusual activity. For details, see Monitoring Cloud Pak for Data user activity.
cpd-cli updates and enhancements
Version 12.0.0 of the Cloud Pak for Data command-line interface, which you can download from https://github.com/IBM/cpd-cli/releases, includes the following features and updates:
cpd-cli cpdctl plug-in
The new plug-in includes commands for managing configuration settings and automating an end-to-end flow that includes model training, saving models, creating a deployment space, and deploying models.

For details, see the asset, code-package, connection, datastage, environment, job, ml, notebook, project, and space commands in the cpd-cli command reference.

Updated cpd-cli export-import commands
The following cpd-cli export-import commands include new options that specify the Cloud Pak for Data instance name and service type.
New cpd-cli manage commands
The cpd-cli manage plug-in includes several new commands:
New cpd-cli oadp dpa command
The cpd-cli oadp dpa command includes subcommands for working with OADP DataProtectionApplication (dpa) commands.
New cpd-cli service-instance apply-acl command
The cpd-cli service-instance apply-acl command applies an access control list (ACL) to the specified service instance.
New and updated cpd-cli user-mgmt commands
The cpd-cli user-mgmt plug-in includes several new commands:

In addition, the cpd-cli user-mgmt list-users was updated so that you can output the list of users to a CSV file.

Version 12.0.0 of the Cloud Pak for Data command-line interface includes various fixes.

Service enhancements

The following table lists the new features that are introduced for existing services in Cloud Pak for Data Version 4.6:

Software Version What does it mean for me?
Cloud Pak for Data common core services 6.0.0
The 6.0.0 release of the common core services includes changes to support features and updates in Watson Studio and Watson Knowledge Catalog.
Version 6.0.0 of the common core services includes the following features and updates:
Access more data with new connection types
You can now work with data from these data sources:
New name for the IBM Data Virtualization connection
The IBM Data Virtualization connector is renamed to IBM Watson Query. The associated platform connection name also changed. Your settings for the connection remain the same. Only the connection name has changed.

Version 6.0.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

If you install or upgrade a service that requires the common core services, the common core services will also be installed or upgraded.

Cloud Pak for Data scheduling service 1.7.0

The 1.7.0 release of the scheduling service includes the following features and updates:

High availability and resiliency
The scheduling service supports Active/Standby mode for its pods when the service is scaled to medium or large. To change the scale of the scheduling service, see Manually scaling resources for services.

Version 1.7.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Related documentation:
You can install and upgrade the scheduling service along with the Cloud Pak for Data platform. For details, see:
Analytics Engine Powered by Apache Spark 4.6.0

The 4.6.0 release of Analytics Engine Powered by Apache Spark includes the following features and updates:

New version of the Spark jobs REST APIs
V4 of the Spark jobs REST APIs is available. The V3 and V2 APIs are deprecated. Although you can still use the deprecated APIs, you should start using the V4 APIs in your Spark applications. For details, see Submitting Spark jobs via API.
Manage your application metadata in an external metastore
You can now use a Hive metastore to manage the metadata that is related to any of your applications that use Spark SQL. If you store the metadata externally, it can be persisted seamlessly across different Spark SQL workloads. For details, see Working with Spark SQL and an external metastore.
Run applications on Spark 3.3
You can now use Spark 3.3 to run your applications on Analytics Engine Powered by Apache Spark service instances.

Although you can still use Spark 3.2 in your applications, Spark 3.2 is deprecated. Consider using Spark 3.3 in your existing applications. For details, see Submitting Spark jobs via API.

Version 4.6.0 of the Analytics Engine Powered by Apache Spark includes various fixes.

For details, see What's new and changed in Analytics Engine Powered by Apache Spark.

Related documentation:
Analytics Engine Powered by Apache Spark
Cognos Analytics 23.0.0

The 23.0.0 release of Cognos Analytics includes the following features and updates:

Change your plan size
You can now change the size of a Cognos Analytics service instance after you provision it. Previously, you could set the plan size only when you provisioned the service instance. Now, you can increase or decrease the size of the instance based on your workload. For details, see Changing the plan size of a Cognos Analytics service instance.
Updated software version for Cognos Analytics
This release of the service provides Version 11.2.3 of the Cognos Analytics software. For details, see Releases 11.2.3 - New and changed features.

Version 23.0.0 of the Cognos Analytics service includes various fixes.

For details, see What's new and changed in Cognos Analytics.

Related documentation:
Cognos Analytics
Cognos Dashboards 4.6.0

The 4.6.0 release of Cognos Dashboards includes the following features and updates:

Filter improvements
The release includes the following filtering enhancements:
Cascading filters
You can now allow filter selections in the All tabs area on the Filters tab to affect the available selections in a different category. For details, see Cascading filters in the Cognos Analytics documentation.
Select filter values from a drop-down list
You can use the drop-down list visualization tool to filter other visualizations based on the values that you select. For details, see Select filter values from a drop-down list in the Cognos Analytics documentation.
Area visualization enhancements

You can now use a gradient fill effect in an area visualization.

Screen shot of the gradient fill effect
Area visualizations now support the following interpolation options:
  • Basis open
  • Cardinal open
  • Monotone
Screen shot of the various interpolation options.

For details, see Set the interpolation between data points in a visualization in the Cognos Analytics documentation.

Handling null values in area and line visualizations
Now you can choose whether to interpolate null values or show null values as a gap in area and line visualizations. By default, null is shown as zero.
Screen shot that shows different ways of visualizing null values.
New value label formats in bar visualizations
The bar chart visualization now includes the following value label formats:
  • Value and percentage of category
  • Value and percentage of color
  • Value delta of target
  • Percentage delta of target
Changed default for the contrast label property in some visualizations
The Contrast label property is now turned on by default for Line and column visualizations and Waterfall visualizations. The property makes it easier to see the value.
Screen shot of the contrast label.

For details, see Changed default for the contrast label property in some visualizations in the Cognos Analytics documentation.

Zoom bar in area, line, and bar visualizations
The Enable zoom bar property is now available for area, bar, and line visualizations. When this property is enabled, a bar with a left and right handle is added to the chart so that users can zoom horizontally.
Screen shoot of horizontal zoom feature
Leaf label formats in treemap visualizations
Leaf labels in treemap visualizations can now be shown as value, item, or a combination of value and item.
Screen shot of leaf label formats
Display multiple measures on rows instead of columns
You can now display the measures on the rows instead of the columns in the Crosstab visualization. For details, see Display multiple measures on rows instead of columns in the Cognos Analytics documentation.

Version 4.6.0 of the Cognos Dashboards service includes various fixes.

For details, see What's new and changed in Cognos Dashboards.

Related documentation:
Cognos Dashboards
Data Privacy 4.6.0
The 4.6.0 release of Data Privacy includes the following features and updates:
The Advanced Data Privacy tool is renamed to Masking flow
Starting in the Cloud Pak for Data Version 4.6 release, the tool for Advanced Data Privacy is renamed to Masking flow. However, the Data Privacy service remains a separately installed service within Cloud Pak for Data with a dependency on Watson Knowledge Catalog. For more information on how to install the tools for Masking flow, see Installing Data Privacy.
Enforce data protection rules with IBM Security Guardium Data Protection
You can extend the enforcement of data protection rules to the data assets that are managed by IBM Security Guardium Data Protection outside of Cloud Pak for Data. After you integrate Watson Knowledge Catalog with IBM Security Guardium Data Protection, Guardium enforces the data protection rules that control access to data on all the data that is managed by Guardium.

For details, see Configuring Watson Knowledge Catalog data protection in the IBM Security Guardium documentation.

Enhancements to data protection rules
Data protection rules are improved so that you can:
  • Define rule conventions to determine whether access to data is allowed or denied by default.
  • Define rule actions and masking precedence to determine how multiple rules combine different actions and masking methods into a single decision.

Defining the rule conventions and precedence before you create data protection rules streamlines how the rules that are created behave and protects data consistently.

Manage rule settings screen capture.

For details, see Managing rule settings.

Simplified process for creating masking flow jobs
The updated interface makes it easier to create and run masking flow jobs to deliver data sets that are masked by data protection rules. The explanations of the masking flow options guide you in selecting and identifying the target location for newly masked data. For details, see Creating masking flows.

Version 4.6.0 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Related documentation:
Data Privacy
Data Refinery 6.0.0

The 6.0.0 release of Data Refinery includes the following features and updates:

New flow settings give you more options for Data Refinery flows
The Data Refinery flow settings give you more properties to control the data in your flows and offer a new capability to edit the sample size of the data while you refine it. Access the flow settings from the toolbar in Data Refinery.
Data Refinery flow settings
Source data sets
In source data sets, you can use the flow settings to:
  • Adjust the sample size while you are refining the data. Reducing the sample size can help you run flows faster when you have a large data set.
  • Edit the source properties. Previously you could specify format options for only CSV or delimited files. Now you have options for other file types and more options for data from connections.
  • Change the source of a flow. For Join and Union operations, you can replace more than one source data set.
Target data sets
In target data sets, you can use the flow settings to:
  • Change the target location of a Data Refinery flow.
  • Edit the target properties. The new settings provide more options for the different types of data, including data from connections.
  • Enter a description of the target data.

For details, see Managing Data Refinery flows.

Refine data from a specific Excel worksheet in a connection or a connected data asset
If you have an Excel file with multiple worksheets in a connection or in a connected data asset, you can select an individual worksheet of data in Data Refinery. Previously only the first worksheet was read.
New Spark 3.3 environment for running Data Refinery flow jobs
When you select an environment for a Data Refinery flow job, you can now select Default Spark 3.3 & R 3.6, which includes enhancements from Spark.

The Default Spark 3.2 & R 3.6 environment is deprecated and will be removed in a future update.

Update your Data Refinery flow jobs to use the new Default Spark 3.3 & R 3.6 environment. For details, see Data Refinery environments.

Split column GUI operation is faster for flows that use large data assets
The Split column operation has been enhanced to work faster on large data assets. If you have existing Data Refinery flows that use the Split column operation, you must update the flows. To update a flow, open it, save it, and run a job for the flow. For details, see Managing Data Refinery flows.

Version 6.0.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Related documentation:
Data Refinery
DataStage 4.6.0

The 4.6.0 release of DataStage includes the following features and updates:

New Java libraries component
You can now create a Java library component, which you can add to your project and use in DataStage flows. The component makes it easy to add Java libraries to Java Integration stages in your DataStage flows. For details, see Java libraries.
Java libraries are included in DataStage flow downloads and imports
A DataStage flow that contains Java Integration stages can have associated Java libraries. When you download and import this flow by using a ZIP file, the Java libraries are now automatically included. For details, see Downloading and importing a DataStage flow and its dependencies
Use new Oracle and Db2 database sequences as connections
You can use Oracle and Db2 database sequences in your DataStage jobs as connections in the following operators:
  • Surrogate Key Generator operator
  • Slowly Changing Dimension operator
  • Transformer operator

For details, see Updating the state file and Surrogate keys in a DataStage Slowly Changing Dimension stage.

New Data Service connector for Cloud Pak for Data
Now you can use the Data Service connector in DataStage to set up a data service on your Cloud Pak for Data cluster. For details, see Data service in DataStage.
Improved security with LDAP and TCPS connections to Oracle data sources that use the Optimized Oracle connector
You can now configure LDAP and TCPS connections to Oracle data sources that use the Optimized Oracle connector. Use these connections for improved data security. For details, see Connecting to Oracle data sources with LDAP and TCPS authentication in DataStage.
Connect to more data sources in DataStage
You can now include data from these data sources in your DataStage flows:
  • Elasticsearch
  • SAP IDoc

For the full list of connectors, see DataStage connectors.

New name for the IBM Data Virtualization connector
The IBM Data Virtualization connector is renamed to IBM Watson Query. The associated platform connection name also changed. Your settings for the platform connection and for the DataStage connector remain the same.
New options available for the Apache HDFS connector
Use new connector properties that are specific to DataStage. These properties provide more features and more granular control of the flow execution, similar to the optimized connectors. To use the options, select Use DataStage properties in the properties panel.
Kerberos authentication for the Apache HDFS connection
You can now use the Kerberos authentication protocol to connect to an Apache HDFS data source when you create the connection from the DataStage service. For information, see Apache HDFS connection.
Authenticate with credentials stored in a vault
You can use credentials that are stored in a vault to connect to your data sources from DataStage. For details, see:
Apache Cassandra and ODBC connectors can now have multiple input links
Previously the Apache Cassandra and ODBC connectors had only one input link. Now, the connectors can have multiple input links.

For the ODBC connector, each link can have a different property. Therefore, each link can have an individual action, such as read, write, or append.

Stored procedures in Teradata connectors
You can now use stored procedures in the Teradata and Teradata (optimized) connectors. For details, see Using stored procedures.
Use parameters from a parameter set in DataStage jobs within pipelines
You can now add a parameter set to a pipeline flow to use individual parameters in DataStage jobs within that pipeline. When you use parameters, you no longer have to change the value of a variable in each expression. You can now set the value of the parameter in one place at runtime. For details, see Orchestrating flows with Watson Pipelines.
Use the PROJDEF parameter set to contain the parameters and environment variable values
A project can now have a parameter set named PROJDEF that contains the parameters and environment variable values for a DataStage flow, job, or job run. Because it is a parameter set, you can use the existing parameter set UI to manage these variables. The parameter set can be imported into other projects. For more information, see PROJDEF parameter set in DataStage.
Use triggers to run routines
With triggers, you can choose routines to be run at specific execution points as the Transformer stage runs in a job. The available execution points are Before-stage and After-stage. The available built-in routines are SetCustomSummaryInfo and SetUserStatus. You can also define custom routines to be run. For more information, see Triggers in the Transformer stage.
Migrate Data Rules stage from traditional DataStage
If you have DataStage Enterprise Plus, you can now migrate jobs from the traditional version of DataStage that contain the Data Rules stage.
Create match specifications with the Match Designer
You can now use the Match Designer, a new tool that you can use to create, edit, and test match specifications for the One-source Match stage and Two-source Match stages using the Match Designer. For details, see Designing match specifications.
Use multiple reject links in DataStage connectors
You can now set up multiple output links as reject links for connectors in your DataStage flows. Use the output links to extract rejected data and sort it based on the input link that the data came from. For the full list of supported connections, see Using multiple links in DataStage connectors.
Databand integration for DataStage data observability and runtime metrics monitoring
You can track the execution and proactively identify problems with the health of your DataStage jobs by using Databand. For more information, see DataStage observability with Databand.

Version 4.6.0 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Db2 4.6.0

The 4.6.0 release of Db2 includes the following features and updates:

TLS certificates are automatically replaced before they expire
If you use the default certificates that are provided with Cloud Pak for Data, the TLS certificates will be automatically replaced. Shortly before a TLS certificate reaches its expiry date, a new certificate will automatically replace the expiring certificate.
Scale memory and resources
You can now scale the CPU and memory for a Db2 database after you provision it. For example, if you need more resources to support high availability or to increase processing capacity, you can run a patch command to modify the allocated resources. For details, see Scaling up Db2.

Version 4.6.0 of the Db2 service includes various fixes.

Related documentation:
Db2
Db2 Big SQL 7.4.0

The 7.4.0 release of Db2 Big SQL includes the following features and updates:

Inferring the table structure from a remote file
You can now use the LIKE clause on the CREATE TABLE (HADOOP) statement to infer the table structure from a data file with a TEXTFILE or JSONFILE file format. For details, see Inferring the table structure from a remote file.
New object storage configuration options
When you connect Db2 Big SQL to an object store, the following configuration options are now available:
Vault support
You can now store credentials and certificates that are used to connect to an object store in a vault. For details, see Setting up a connection to a remote data source.
Trusted certificate for connections to internal services
Db2 Big SQL can now use the CA certificate that you can optionally provide for connections to internal services. If you use a CA certificate, you no longer need to provide a separate certificate for internal object store service connections. For details on using a CA certificate to connect to internal services, see Using a CA certificate to connect to internal servers from the platform.
Path-style access
You can now specify that an S3 object store bucket must be accessed by using path-style access instead of virtual-hosted-style access. With support for path style access, Db2 Big SQL can now connect to more object store services, such as MinIO. For details, see Setting up a connection from Db2 Big SQL to a remote data source.
Diagnostic logs are now stored in a dedicated persistent volume
In new Db2 Big SQL deployments, diagnostic logs for the Db2 Big SQL instance are stored in a dedicated persistent volume, which prevents overflow problems and improves reliability.

For more information about diagnostic logs, see Gathering diagnostic information.

Version 7.4.0 of the Db2 Big SQL service includes various fixes.

Related documentation:
Db2 Big SQL
Db2 Data Gate 3.0.0
Version 3.0.0 of the Db2 Data Gate service includes the following features and updates:
Store database and table metadata in centralized instance of Watson Knowledge Catalog
Previously, if you had multiple deployments of Db2 Data Gate on Cloud Pak for Data, and you wanted to publish metadata to Watson Knowledge Catalog, you needed to maintain an instance of Watson Knowledge Catalog for each Cloud Pak for Data instance where Db2 Data Gate was installed.

Starting with Db2 Data Gate Version 3.0.0, you can publish metadata to an instance of Watson Knowledge Catalog on a different instance of Cloud Pak for Data. This enables you to store and manage all of your Db2 Data Gate metadata in a single catalog. For details, see Publishing Db2 Data Gate to Watson Knowledge Catalog .

Seamless instance upgrades
You can now keep your existing Db2 Data Gate instances when you upgrade from Cloud Pak for Data Version 4.5 to Cloud Pak for Data Version 4.6. It is no longer necessary to remove the old instance and provision a new one when you upgrade your Cloud Pak for Data installation. By keeping your existing Db2 Data Gate instances, you can preserve the instance configuration, your table definitions, and more across releases.
New synchronization workload metrics
On the Db2 Data Gate monitoring dashboard, you can now view:
  • The number of rows inserted to the target database
  • The number of rows deleted from the target database
  • The number of net-effect operations that occurred during the synchronization process

    The number of net-effect operations is the number of operations that the database engine skips to optimize the performance.

    For example, two updates of the same row value would likely involve the following sequence of operations: delete - insert - delete -insert. However, the database engine reduces the number of operations; only the first delete job and the last insert job are executed.

You can use the new metrics to evaluate the synchronization workload and pinpoint performance bottlenecks. For details, see Monitoring a Db2 Data Gate instance.

Export monitoring data for reports
You can now export monitoring data from the Db2 Data Gate dashboard to a file with a comma-separated values (CSV) format. With this versatile format, you can open and analyze your monitoring data in various tools. For details, see Monitoring a Db2 Data Gate instance.

Version 3.0.0 of the Db2 Data Gate service includes various fixes.

For details, see What's new and changed in Db2 Data Gate.

Related documentation:
Db2 Data Gate
Db2 Data Management Console 4.6.0

The 4.6.0 release of Db2 Data Management Console includes the following features and updates:

New actions for event monitoring
The Event monitor profile page now has the following options to manage event monitoring data:
  • View the list of event motoring objects.
  • View the CREATE EVENT MONITOR statement in the object list.
  • Delete a table space.
  • Delete event monitoring objects.

For more information, see Setting up event monitoring profile.

New monitoring profile options for collecting storage data
You can now use the following options in the Monitoring profile page to configure the collection of storage metric data for a monitored database:
Collect storage data
Collects table performance and table storage data for both tables and indexes.
Storage data collection time
Specifies the collection time with the format hh:mm.

For more information, see Setting up monitoring profile.

New SQL editor
Db2 Data Management Console now has a new version of SQL editor that includes a database object tree view. When you write a query, the SQL editor helps to quickly find a target table and other table-like objects. It can also generate DDL or get column details to help you complete the query.

Version 4.6.0 of the Db2 Data Management Console service includes various fixes.

For details, see What's new and changed in Db2 Data Management Console.

Related documentation:
Db2 Data Management Console
Db2 Warehouse 4.6.0

The 4.6.0 release of Db2 Warehouse includes the following features and updates:

TLS certificates are automatically replaced before they expire
If you use the default certificates that are provided with Cloud Pak for Data, the TLS certificates will be automatically replaced. Shortly before a TLS certificate reaches its expiry date, a new certificate will automatically replace the expiring certificate.
Scale memory and resources
You can now scale the CPU and memory for a Db2 Warehouse database after you provision it. For example, if you need more resources to support high availability or to increase processing capacity, you can run a patch command to modify the allocated resources. For details, see Scaling up Db2 Warehouse.

Version 4.6.0 of the Db2 Warehouse service includes various fixes.

Related documentation:
Db2 Warehouse
Decision Optimization 6.0.0

The 6.0.0 release of Decision Optimization includes the following features and updates:

Change data types in tables in Decision Optimization experiments
You now have the flexibility to change the data types (number or string) of table columns in the Prepare data view of your Decision Optimization experiment. These types are used when you save your scenario as a model for deployment.
Screen shot of the Prepare data view

For details, see Prepare data view.

New Python extensions
You can now add Python extensions to your Decision Optimization experiment environments so that you can include additional Python libraries.
Screen shot of the interface for adding Python extensions.

For details, see Configuring Environments and adding Python extensions

Python 3.10 now supported
Python 3.10 is now supported in Decision Optimization experiments in Watson Studio and for deployment in Watson Machine Learning. The default version remains Python 3.9.

For details, see Configuring Environments and Model deployment.

For DOcplex notebooks the new Runtime 22.2 with Python 3.10 and CPLEX 22.1 is now available.

Version 6.0.0 of the Decision Optimization service includes various fixes.

For details, see What's new and changed in Decision Optimization.

Related documentation:
Decision Optimization
EDB Postgres 4.8.0

The EDB Postgres service is available on Cloud Pak for Data Version 4.6. You can use the service to install the following versions of EDB Postgres:

  • 12.11
  • 13.7

Version 12.11, 13.7 of the EDB Postgres service includes various fixes.

Related documentation:
EDB Postgres
Execution Engine for Apache Hadoop 4.6.0

The 4.6.0 release of Execution Engine for Apache Hadoop includes the following features and updates:

Python 3.10 now supported
You can now use Python 3.10 in Execution Engine for Apache Hadoop in Watson Studio. The default version remains Python 3.9.

Version 4.6.0 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Related documentation:
Execution Engine for Apache Hadoop
IBM Match 360 2.0.27

The 2.0.27 release of IBM Match 360 is available on Cloud Pak for Data Version 4.6.

Version 2.0.27 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Related documentation:
IBM Match 360 with Watson
Informix 5.0.0

The 5.0.0 release of Informix includes the following features and updates:

Integration with the Cloud Pak for Data Audit Logging Service
Informix now sends auditing records for important database and security events as JSON files to the Cloud Pak for Data Audit Logging Service. For details, see Audit events for Informix.
Optimized images for faster loading
The Informix operand images are now optimized to reduce their size, which speeds up load times during service deployment.

Version 5.0.0 of the Informix service includes various fixes.

For details, see Fix list for Informix Server 14.10.xC9 release.

Related documentation:
Informix
MongoDB 4.8.0

The MongoDB service is available on Cloud Pak for Data Version 4.6. You can use the service to install the following versions of MongoDB:

  • 4.4.0
  • 5.0.13
  • 6.0.2

This release of MongoDB includes various fixes.

Related documentation:
MongoDB
OpenPages 8.301.0

The 8.301.0 release of OpenPages includes the following features and updates:

New features from OpenPages with Watson 8.3.0.1
The OpenPages service includes enhancements that were introduced in OpenPages with Watson 8.3.0.1, including:
Tags
You can now apply tags to objects to categorize the objects and perform searches that are based on those categories. You can search on tags in any grid other than a relationship grid on a Task or Admin tab.
Integration of AI models
You can now build or bring any type of AI model and use the model insights directly in OpenPages views and workflows. You can use AI models that are built with AutoAI in Watson Studio or developed by a data science team, and then deployed on Watson Machine Learning on IBM Cloud. The OpenPages integration can then send data to the models to fetch live predictions.

You can read about these enhancements and more in the OpenPages with Watson documentation.

Version 8.301.0 of the OpenPages service includes various fixes.

Related documentation:
OpenPages
Planning Analytics 4.6.0

The 4.6.0 release of Planning Analytics includes the following features and updates:

Updated versions of Planning Analytics software
The 4.6.0 release includes the following software versions:

Version 4.6.0 of the Planning Analytics service includes various fixes.

Related documentation:
Planning Analytics
Product Master 3.0.0

The 3.0.0 release of Product Master includes the following features and updates:

Simplified method for customizing Product Master
Previously, if you wanted to customize Product Master after it was installed, you needed to create a new image (even if you only wanted to make a single change). Starting in Version 3.0.0 of the Product Master service, you can use the default image and customize it by placing your modified files in the directories that are created during installation. For more information, see Customizing Product Master.

Version 3.0.0 of the Product Master service includes various fixes.

For details, see What's new and changed in Product Master.

Related documentation:
Product Master
RStudio Server Runtimes 6.0.0

RStudio Server with R 3.6 has been renamed to RStudio Server Runtimes.

The 6.0.0 release of RStudio Server Runtimes includes the following features and updates:

New runtime
You can now open RStudio in the RStudio with Runtime 22.2 on R 4.2 environment to create your scripts and Shiny apps.

If you want to continue using R 3.6, select the RStudio with R 3.6 environment; however consider that this environment is deprecated.

For details, see RStudio environments.

Apache Spark 3.3 in RStudio
You can start using Apache Spark 3.3 to run your scripts and Shiny apps. You can use Spark 3.3 with
  • R 4.2 in the RStudio with Runtime 22.2 on R 4.2 environment
  • R 3.6 in the RStudio with R 3.6 environment

For details, see Using Spark in RStudio.

Version 6.0.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Related documentation:
RStudio Server Runtimes
SPSS Modeler 6.0.0

The 6.0.0 release of SPSS Modeler includes the following features and updates:

Improvements to the Data Audit node
The Data Audit node provides a comprehensive first look at the data that you bring to SPSS Modeler. The node properties are simplified to make the node easier to use. In addition, the node output is now interactive and includes information such as summary statistics, histograms, box plots, bar charts, pie charts, and more. For more information, see Data Audit node.
Screen shot of the Data Audit output.
Copy nodes to other flows
You can now copy and paste nodes between SPSS Modeler flows, even between flows in different projects. This can save a lot of time if you have complex nodes (for example, a large Type node, Restructure node, or SuperNode) that you want to use in multiple flows without having to recreate them.
New model results for the Auto Data Prep node
After running an Auto Data Prep node, you can now view advanced model results. Right-click the node and select View Model.
Screen shot of the Auto Data Prep node model results.
Improvements to Text Analytics
The Text Analytics user interface has been improved. In the interactive Text Analytics Workbench, you can now sort the columns. And dynamic paging is now used, which improves performance (rather than all records being loaded at once, they're now quickly loaded as you scroll through pages).
SQL pushback is now available for Teradata
To improve performance when using Teradata, you can now push back many operations to the database. SQL pushback for Teradata is available if you install a custom runtime image and a Teradata ODBC driver. For details, see:
New versions of Apache Spark and Python
You can now use Apache Spark 3.3 and Python 3.10 in your scripts to take advantage of the latest updates. For details, see Extension nodes.
Native Python is now available
You can now use Native Python for scripting in the Extension nodes. Invoke native Python APIs from your scripts to interact with SPSS Modeler. For details, see Native Python APIs.
New Type node prompt
The Type node is one of the most commonly used nodes in SPSS Modeler. It can be a vital step in building the right model, helping you evaluate and correct field metadata. In cases where a Type node is required in your flow, you'll now be prompted to add one automatically in the proper location, making it much easier and quicker to create a flow that meets your needs.

Version 6.0.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Related documentation:
SPSS Modeler
Watson Assistant 4.6.0

The 4.6.0 release of Watson Assistant includes the following features and updates:

Improved user experience
With the new Watson Assistant user interface, you can build, test, publish, and analyze your assistant from one simple and intuitive interface that focuses on using actions to build customer conversations.
Screen shot of the new Watson Assistant interface

To learn more about building your assistant with actions, see Welcome to the new Watson Assistant in the Watson Assistant documentation on IBM Cloud.

New algorithm version setting
You can now choose one of three Watson Assistant algorithm version settings to apply to your future trainings:
  • Beta
  • Latest
  • Previous

The Algorithm Version setting replaces the Intent Detection setting in the dialog skill.

The Beta and Latest versions use more enhanced intent detection and support the following languages: Arabic, Chinese (Simplified), Chinese (Traditional) Czech, Dutch, French, German, Japanese, Korean, Italian, Portuguese, and Spanish. For details, see Algorithm version.

Credential rotation
You can now rotate your credentials for added data store security. For details, see Managing security for your Watson Assistant data stores.
Autoscaling
Watson Assistant now provides the capability to enable or disable the Red Hat OpenShift Horizontal Pod Autoscaler. You can optionally enable autoscaling for Watson Assistant so that the service dynamically scales up and down to make efficient use of cluster resources. For details, see Automatically scaling resources for services.

Version 4.6.0 of the Watson Assistant service includes various security fixes.

Related documentation:
Watson Assistant
Watson Discovery 4.6.0
Version 4.6.0 of the Watson Discovery service includes the following features and updates:
Build a custom type system based on key terms in your data with the Entity extractor
Now you can build a custom type system into Watson Discovery that can recognize, tag, and retrieve important information from your unstructured data. With the new Entity extractor tool, you label examples of significant terms in a sample of your business data. The tool uses your examples to generate a machine learning model for you. When you apply the model to new data, it finds and tags information that is uniquely significant to your business. For details, see Entity extractor in the Watson Discovery product documentation on IBM Cloud.
Visualize the enrichments found in your documents
When you view the passage from a search result, the document preview shows a representation of the original document where the search result was found. For most document types, you can now open an advanced view of the document. The advanced view provides a useful summary, including the total number of occurrences of any enrichments that are detected in the document. You can also select one of the enrichments to highlight every occurrence of the element in the document.
Screen capture of the advanced view of a search result with the IBM Organization entity mention highlighted inline in a PDF.
New and updated external data source connectors
You can use the following new and updated data source connectors:
Box
Now you can use the App Access Only access level when you set up the Watson Discovery connector to crawl files that are stored in a Box data store. If you don't want the crawler to have access to all of your enterprise data, you can configure it with more targeted access control so that it can crawl only content that is designated for use by the app. For details, see Box in the Watson Discovery documentation on IBM Cloud.
Windows File System
The connector now supports Microsoft Windows Server 2022. The Windows Agent Service that you use to install the connector on your file system runs on a 64-bit version of Windows. To use the new agent service, you must uninstall and delete the prior version of the agent service. For details on migrating to the new connector, see Windows File System in the Watson Discovery documentation on IBM Cloud.
API improvements
The following APIs are now available from a Cloud Pak for Data instance:
  • The Get collection method now returns the smart_document_understanding field, which specifies whether an SDU model is enabled for the collection and indicates the model type. For more information, see the API reference documentation for Watson Discovery on IBM Cloud.
  • The Query method now includes the similar parameter, which you can use to find documents that are similar to documents of interest to you. For more information, see the API reference documentation.

Version 4.6.0 of the Watson Discovery service includes various fixes.

Related documentation:
Watson Discovery
Watson Knowledge Catalog 4.6.0

The 4.6.0 release of Watson Knowledge Catalog includes the following features and updates:

Enhancements to metadata import
The following enhancements are available when you import metadata:
  • You can now keep customized asset information when data is re-imported. For each metadata import configuration, you can specify whether assets names, asset descriptions, or column descriptions are updated whenever the data is re-imported. For details, see Discovering data.
  • You can now import lineage information from Netezza® Performance Server data sources.
Customized term assignment and sampling in metadata enrichment
The following enhancements were made to metadata enrichment:
Customize ML-based term assignment
For each project, you can now specify whether your models for ML-based term assignment are trained from assets in the project or from a catalog.
Screen shot of the user interface that shows how you can choose which assets are used for training.

If Watson Machine Learning is installed in your Cloud Pak for Data environment, you can create and use your own ML model for term assignment instead of the built-in model.

For details, see Default enrichment settings.

Capture data changes more accurately with improved sampling
When you set up customized sampling, you can choose between sequential and random sampling, if the data source supports random sampling. In addition, you can optionally choose to include a certain percentage of the table rows in the sample, rather than a fixed number of rows.
Screen shot of the user interface that shows how you can choose which sampling method and sample size to use.

For details, see Enriching your data assets.

More options for binding variables in data quality rules
You can now use job parameters to bind a rule to literal values that are centrally managed in a project. Literal values can change at run time, by using job parameters, you don’t need to update the rule when values change.
Screen shot of the user interface that shows how you can configure job parameters.

For details, see Managing data quality rules.

Asset and artifacts definitions
With the new asset and artifacts definitions feature, you can create your own custom properties and relationships for glossary artifacts and catalog assets. Additionally, you can import and export custom property and relationship definitions for glossary artifacts. For details, see Custom properties and relationships for governance artifacts.
Enhancements to lineage graph
The lineage graph includes the following improvements:
  • You can now track the flow of a node's cycles as well as its member, which helps provide more accurate and detailed data.
  • The Show columns dialog has been reorganized so that you can more easily select the columns to show on the graph.
  • You can now set your preferences for how to display the legend and the overview of a graph.
  • The legend is now dynamic and displays only the most common asset types. You can see all your asset types that are in a graph by selecting.
For details, see Exploring business lineage.
Usability enhancement when creating governance artifacts

Now you can create a new category on the fly with the New category button. For example, when you create a governance artifact but you do not have access to any existing categories, or the categories that are available do not meet your requirements, you might want to define a new category without leaving the open window. For more information, see Managing categories.

New industry-specific data classes with Knowledge Accelerators
The Knowledge Accelerators extend the set of data classes provided in Watson Knowledge Catalog. The Knowledge Accelerators data classes:
  • Are based on industry standards such as ISO, FHIR, and CIM.
  • Describe commonly used information in data sources from industries such as Energy & Utilities, Financial Services, Healthcare, and Insurance.
  • Define expected patterns in business data formats or leverage a known set of values from new and existing Knowledge Accelerators reference data sets

The data classes enhance the Watson Knowledge Catalog metadata enrichment process so that it can recognize industry-specific data. When the data classes are mapped to the Knowledge Accelerators glossary, metadata enrichment can automatically assign these discovered data assets to their respective business terms.

The data classes are fully customizable and can be adjusted to focus on client-specific reference data sets or column-name-matching restrictions.

For details, see Knowledge Accelerators.

Knowledge Accelerators data classes in Watson Knowledge Catalog.
View metadata of blocked assets in catalogs
Users who cannot access based on data protection rule can now see the metadata of the assets. For example, when users click on a blocked asset in a catalog they can now see the description, assigned terms, custom properties, relationships, and column names of the blocked asset.

For more information on how users can interact with data under a “deny access” data protection rule, see the action table in Designing data protection rules.

Extensive new query capabilities
You can now create custom reports on:
  • Workflow data
  • Metadata imports
  • User profiling
  • Metadata enrichment

For example, to ensure the quality of automatic term assignments for your discovered data sets and columns, you can generate a report to list the assigned and rejected terms for the data sets and columns.

To learn more about creating custom reports, see Setting up reporting for Watson Knowledge Catalog.

Show usernames and emails in reports
Now managers and reporting administrators can enable user profiling for a catalog in Reports setup. If you enable user profiling, user information such as emails and usernames are reported to the data mart along with the user ID. This information can be displayed in reports.

For more information, see Setting up reporting for Watson Knowledge Catalog

Easily access your activity history
Now you can easily access your activity history for projects and catalogs by clicking the View asset activities icon (Screen shot of the View asset activities icon) in the action bar of an asset.

Version 4.6.0 of the Watson Knowledge Catalog service includes various fixes.

For details, see What's new and changed in Watson Knowledge Catalog.

Related documentation:
Watson Knowledge Catalog
Watson Knowledge Studio 4.6.0

The 4.6.0 release of Watson Knowledge Studio on Cloud Pak for Data Version 4.6 includes several security fixes and stability enhancements.

Related documentation:
Watson Knowledge Studio
Watson Machine Learning 4.6.0

The 4.6.0 release of Watson Machine Learning includes the following features and updates:

Use incremental learning to train AutoAI experiments with large data sets
In prior releases of AutoAI, you were limited in the amount of data that you could use to train an AutoAI experiment. Now, if you are training an AutoAI experiment with a large data set, you can prepare the experiment in the UI to train incrementally with batches of data. Then, save the best pipeline as an auto-generated notebook to complete training with the remaining batches of data. Note that data used must be of non-timeseries type. For details, see Using incremental learning to train with a large data set.
Support for joined data in AutoAI removed
You can no longer join multiple data sources in AutoAI for training an experiment. Use a data preparation tool such as Data Refinery or DataStage to join and prepare data, then use the resulting data file in AutoAI for training an experiment.
The Spaces interface is organized like the Projects interface
The Spaces user interface is now organized more like the Projects user interface to improve the ease of use and collaboration within a space. Explore the enhanced asset organization, asset import flow, and improved navigation. For details, see Deployment spaces.
Screen capture of the updated deployment space UI
Deploy a Natural Language Processing model with a Python function
In prior releases, you could train a Natural Language Processing model in a notebook but could not deploy it from Watson Machine Learning. Now, you can create an online deployment for a Natural Language Processing model that's created in a notebook to get results back in real time. To create the deployment, add the notebook to a deployment space, then create a Python function to create the deployment. Note that you can only deploy Natural Language Processing models by using runtimes based on Python 3.9. For details, see Deploying a Natural Language Processing model.
Train and deploy assets with Spark 3.3
Train and deploy your machine learning assets by using the latest frameworks and software specification, including Spark 3.3. Although you can still use Spark 3.2 in your applications, Spark 3.2 is deprecated. For details, see Supported software specifications and frameworks.
Enable additional sources for custom packages
All deployment runtimes in Watson Machine Learning include a predefined set of packages and libraries. If you need libraries or packages that are not included in the standard deployment runtimes, you can add them by customizing the software specifications that control how conda and pip packages are obtained. You can change the specification to download packages from:
  • A hosted channel
  • A proxy server
  • Your local cc-home directory
You can use the custom software specifications with:
  • Scikit-learn models
  • XGBoost models
  • Tensorflow models
  • Python functions
  • Python scripts

For details, see Creating a custom software specification in a project.

New, more secure log files for batch deployment jobs
In earlier versions of Cloud Pak for Data, the JSON output for a batch scoring job was retrieved by calling the /ml/v4/deployment_jobs API, and the data was provided as the log. This process and the resulting output will continue for existing jobs. However, starting in Cloud Pak for Data Version 4.6, log data for new jobs is written to an attached text file that you can review. The log does not include the job data, but provides the metadata for the job, including the job start time, end time, associated space, and status. The job data can be directly retrieved from WML APIs . For details, see the Watson Machine Learning API documentation.

Version 4.6.0 of the Watson Machine Learning service includes various fixes.

Related documentation:
Watson Machine Learning
Watson Machine Learning Accelerator 3.0.0
Version 3.0.0 of the Watson Machine Learning Accelerator service includes the following features and updates:
New deep learning libraries

You can now use the following deep learning libraries with Watson Machine Learning Accelerator:

  • TensorFlow 2.9.2
  • PyTorch 1.12.1
  • NVIDIA CUDA Toolkit 11.4.4
  • Python 3.10.4
New NVIDIA GPU Operator version
You can now use Watson Machine Learning Accelerator with the following versions of the NVIDIA GPU Operator:
x86-64
  • On OpenShift 4.8, use NVIDIA GPU Operator 1.10 or 1.9
  • On OpenShift 4.10, use NVIDIA GPU Operator 1.10 or 1.9
For details, see: Installing the NVIDIA GPU Operator on OpenShift.
Power
On OpenShift 4.8 and 4.10, use Rocket Software GPU Operator v22.9.0. For details, see: GPU Operator for POWER.

Version 3.0.0 of the Watson Machine Learning Accelerator service includes various fixes.

Related documentation:
Watson Machine Learning Accelerator
Watson OpenScale 4.6.0

The 4.6.0 release of Watson OpenScale includes the following features and updates:

New fairness metrics
You can now configure the following fairness metrics in Watson OpenScale:
  • Statistical parity difference
  • Average odds difference
  • Average absolute odds difference
  • False negative rate difference
  • False positive rate difference
  • False discovery rate difference
  • False omission rate difference
  • Error rate difference

For more information, see Configuring the Fairness monitor

Configure explainability methods
When you configure your model evaluations in Watson OpenScale, you can now select different settings to generate local and global explanations.
  • For global explanations, you can use the SHAP (SHapley Additive exPlanations) method.
  • For local explanations, you can use the SHAP method or the LIME (local interpretable model-agnostic explanations) method.

For more information, see Configuring explainability.

Authenticate with credentials stored in a vault
When you set up Watson OpenScale, you can use credentials that are stored in a vault to authenticate to your data and service connections. For details on storing credentials in a vault, see Managing secrets and vaults.
Upload payload data
To provide model details to configure model evaluations for production deployments, you can now use a CSV file to upload payload data to Watson OpenScale. For more information, see Configuring endpoint evaluation.
Simplified batch deployment configuration
When you configure evaluations for a self-managed batch deployment, you can run a custom notebook to generate configuration files. You can upload the configuration files in Watson OpenScale to specify settings for each type of evaluation and explainability. For more information, see Configuring the batch processor in Watson OpenScale. You can also now use APIs from the Watson OpenScale Python SDK to connect to your training data to evaluate batch deployments.
New batch deployment features
When you configure a batch deployment, you can now use features that were previously available only for online deployments:
  • Perturbation-based fairness evaluation
  • Indirect bias evaluations
  • Perturbation-based explainability methods
Deprecation notice for V2 REST API explanation tasks
The subscription ID will be required for the V2 REST API explanation tasks in an upcoming release. Any /v2/explanation_tasks request that doesn't specify the subscription ID will not be supported. Start specifying the subscription ID as a query parameter for the /v2/explanation_tasks GET request and specifying the subscription ID when you provide the scoring data for the /v2/explanation_tasks POST request.

Version 4.6.0 of the Watson OpenScale service includes various fixes.

Related documentation:
Watson OpenScale
Watson Pipelines 4.6.0

The Watson Pipelines service is no longer an Early Adoption offering.

The 4.6.0 release of Watson Pipelines includes the following features and updates:

Automate end-to-end flows with Watson Pipelines
Use Watson Pipelines to build a pipeline that can automate your machine learning process from creating a model to deploying a model. Use the Pipeline editor canvas to:
  • Configure nodes that connect to data
  • Run a Data Refinery flow or DataStage job
  • Run notebooks
  • Promote from projects to spaces
  • Create deployments
Watson Pipelines tool for automating model lifecycle flows

For details, see Watson Pipelines.

Use parameter sets and Pipeline components to customize your pipelines
  • You can now create parameter sets as a project asset to specify a group of parameters that you can use in pipelines. Add these parameter sets to your pipelines and assign them to nodes to supply values at runtime. For details, see Configuring global objects.
  • You can create reusable components with Python for use in pipelines. For example, you can create reusable scripts as project assets and add them to pipelines. For details, see Creating a custom component.
Related documentation:
Watson Pipelines
Watson Query 2.0.0
Version 2.0.0 of the Watson Query service includes the following features and updates.
Data Virtualization is now Watson Query

The Data Virtualization service was renamed to Watson Query, and you will notice some changes in the user interface. To get started using Watson Query, click Data > Data virtualization on the navigation menu.

The IBM Data Virtualization connection is also renamed to IBM Watson Query. Your previous settings for the connection remain the same. Only the connection name has changed.

Improvements for data sources in object storage
  • You can now create connections and virtualize files for the following data sources in object storage:
  • You can now create virtualized tables from externally compressed CSV or TSV files that are stored in object storage.
  • You can now use credentials that are stored in a vault when you create a connection to a data source in object storage. For more information, see Managing secrets and vaults.
  • You can now use an optional parameter to identify a delimiter character (quoteChar) that surrounds field values in flat files. You can use this parameter for previewing and virtualizing flat files that are stored in object storage.

For more information, see Creating a virtual table from files in object storage.

Predicate pushdown improvements
Predicate pushdown is an optimization that reduces query times and memory usage. This release includes the following improvements to predicate pushdown.
  • Queries that include COUNT (DISTINCT) or GROUP BY clauses can now be pushed down with trailing blanks comparison rules for Teradata, Netezza, Microsoft SQL Server, Db2 for z/OS, and Db2 Database data sources.
  • Queries that include a string comparison operation such as a GROUP BY or WHERE predicate against CHAR or VARCHAR data for the Teradata data source can handle case sensitivity.
  • The Greenplum data source now supports push down of predicates.

For more information, see Supported data sources in Watson Query.

Faster cache refreshes

You can now set the refresh rate for a cache based on minutes. For example, setting the Minutes element to 5 causes the cache to refresh every 5 minutes. For more information, see Adding data caches.

New caching APIs
Cache entries can be managed through REST APIs that the caching service exposes. These APIs can be invoked by any application. You can use new caching APIs to do the following tasks:
The following caching APIs are deprecated:

For more information, see Caches in the Watson Query 2.0.0 API docs.

Diagnostic logs are now stored in a dedicated persistent volume

In new Watson Query deployments, diagnostic logs for the Watson Query instance are stored in a dedicated persistent volume, which prevents overflow problems and improves reliability. For more information, see Gathering diagnostic information.

The Cloud Pak for Data CA certificate can be used in Watson Query connections
For more information, see Using a CA certificate to connect to internal servers from the platform.

Version 2.0.0 of the Watson Query service includes various fixes.

For details, see What's new and changed in Watson Query.

Related documentation:
Watson Query
Watson Speech services 4.6.0

The 4.6.0 release of the Watson Speech services includes various features and enhancements, such as language updates.

For a list of new features in Watson Speech services, see:

Version 4.6.0 of the Watson Speech services includes various fixes.

For details, see What's new and changed in Watson Speech to Text.

Related documentation:
Watson Speech services
Watson Studio 6.0.0

The 6.0.0 release of Watson Studio includes the following features and updates:

Import and export visualizations with a project
You can now import and export visualizations with a project. For details, see:
Watson Natural Language Processing Library
Use the Watson Natural Language Processing Library to turn unstructured data into structured data, which can make data easier to understand and use in your Python notebooks. The library gives you instant access to pre-trained, high-quality text analysis models in over 20 languages. The models are created, maintained, and evaluated for quality by experts from IBM Research and IBM Software. For details, see Watson Natural Language Processing Library.
Runtime 22.2 with Python and R
You can now use Runtime 22.2, which includes the latest data science frameworks on Python 3.10 and on R 4.2 to run Watson Studio Jupyter notebooks, train models, and run Watson Machine Learning deployments.

Runtime 22.2 with R 4.2 in notebooks is supported on x86 hardware only.

You can still use the Runtime 22.1 releases for Python 3.9 and R 3.6; however, Runtime 22.1 on R 3.6 is deprecated. Update your assets and deployments to start using Runtime 22.2. To change environments, see Changing the environment of a notebook.

New Watson Studio extension for Visual Studio Code
If your preferred tool for analyzing data, and building and testing your models is Visual Studio Code, you can now use the Watson Studio extension to connect to a Cloud Pak for Data cluster directly from VS Code. With the extension you can:
  • Start and stop your runtimes
  • Securely connect to your runtimes on the cluster through SSH
  • Edit the files inside your Watson Studio Git-based project through SSH

For details, see Watson Studio extension for Visual Studio Code.

Apache Spark 3.3 in Notebooks and JupyterLab
You can start using Apache Spark 3.3 to run your notebooks and scripts. Spark 3.3 is supported with Python 3.9, and with R 3.6.

Spark 3.2 in Notebooks and JupyterLab is deprecated. Although you can still use Spark 3.2 to run your notebooks and scripts, you should consider moving to Spark 3.3.

Deprecation of Scala notebooks
Working with Scala notebooks in Watson Studio to analyze data and build models is deprecated. Use Python or R instead. Support for Scala notebooks will be removed in a future release.

Version 6.0.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Related documentation:
Watson Studio
Watson Studio Runtimes 6.0.0

The 6.0.0 release of Watson Studio Runtimes includes the following features and updates:

Runtime 22.2 with Python and R
You can now use Runtime 22.2, which includes the latest data science frameworks on Python 3.10 and on R 4.2 to run Watson Studio Jupyter notebooks, train models, and run Watson Machine Learning deployments.

Runtime 22.2 with R 4.2 in notebooks is supported on x86 hardware only.

You can still use the Runtime 22.1 releases for Python 3.9 and R 3.6; however, Runtime 22.1 on R 3.6 is deprecated. Update your assets and deployments to start using Runtime 22.2. To change environments, see Changing the environment of a notebook.

Watson Natural Language Processing Library
The Watson Natural Language Processing for Python library is available in:
  • Runtime 22.1 on Python 3.9
  • JupyterLab with Runtime 22.1 on Python 3.9

Select these runtimes to turn unstructured data into structured data, which can make data easier to understand and use in your Python notebooks. The library gives you instant access to pre-trained, high-quality text analysis models in over 20 languages. For details, see Watson Natural Language Processing Library.

Version 6.0.0 of the Watson Studio Runtimes service includes various fixes.

For details, see What's new and changed in Watson Studio Runtimes.

Related documentation:
Watson Studio Runtimes

New services

The following table lists the new services that are introduced in Cloud Pak for Data Version 4.6:

Category Service Pricing What does it mean for me?
AI AI Factsheets Included with Cloud Pak for Data

In Cloud Pak for Data Version 4.5, AI Factsheets was an optional component of Watson Knowledge Catalog. Starting in Version 4.6, AI Factsheets is a separately installed service.

AI Factsheets supports your AI Governance strategy by tracking details and metadata for machine learning models. The AI Factsheets service is linked to a model inventory that you can use to organize your machine learning models and factsheets. The inventory enables you to track details for each model and deployment. Validators and approvers can use the model data, which is securely gathered and stored in the factsheets, to get an accurate, up-to-date view of the model lifecycle details.

This release includes the following enhancements to the AI Factsheets functionality:
Customize report templates for model use cases and model details
You can now customize the report template for model use cases or model details to meet the needs of your organization. For details, see Model inventory and AI Factsheets.
Related documentation:
AI Factsheets
Data source Data Replication Separately priced
Data Replication is a new service on Cloud Pak for Data that replicates data changes across heterogeneous data stores without impacting the performance of your systems of record. Data Replication can replicate data in the following environments:
  • From on-premises data system to cloud data systems
  • From one cloud data system to another cloud data system
With Data Replication, you can:
Improve business insight
Move data in near real time to the environment where you perform analytics or event processing, which enables you to quickly react to changes and critical business events.
Ensure business continuity
Synchronize data stores to support continuous operations in the event of planned and unplanned outages.
Related documentation:
Data Replication

Installation enhancements

What's new What does it mean for me?
Red Hat OpenShift Container Platform support
You can deploy Cloud Pak for Data Version 4.6 on the following versions of Red Hat OpenShift Container Platform:
  • Version 4.8.0 or later fixes
  • Version 4.10.0 or later fixes
Mirror only the images that are needed for your cluster hardware
If you mirror images to a private container registry, you can now use the --arch option to mirror only the images that are required to run the software on your cluster hardware.

Previously, all of the images were mirrored. Now, you can save space in your private container registry be mirroring only the images that you need. For details, see Mirroring images to a private container registry.

Removals and deprecations

What's changed What does it mean for me?
Submitting a request for data
The data requests (Data > Data requests) feature is deprecated and will be removed in a future release. You should consider using workflows instead.
Offline backup and restore
Offline backup and restore is deprecated and will be removed in a future release. It is recommended that you create online backups. For more information, see Backing up and restoring Cloud Pak for Data.

Previous releases

Looking for information about previous releases? See the following topics in IBM Documentation: