Refining data (Data Refinery)

To refine data, you take it from one location, cleanse and shape it, and then load the result into a different location. You can cleanse and shape tabular data with a graphical flow editor tool called Data Refinery.

Service The Data Refinery service is not available by default. An administrator must install either the Watson Studio service or the Watson Knowledge Catalog service on the IBM Cloud Pak for Data platform. To determine whether the service is installed, open the Services catalog and check whether the Data Refinery service is enabled.

When you cleanse data, you fix or remove data that is incorrect, incomplete, improperly formatted, or duplicated. When you shape data, you customize it by filtering, sorting, combining or removing columns.

You create a Data Refinery flow as a set of ordered operations on data. Data Refinery includes a graphical interface to profile your data to validate it and over 20 customizable charts that give you insights into your data.

Required service
Watson Studio or Watson Knowledge Catalog

Data format Avro, CSV, JSON, Parquet, SAS with the "sas7bdat" extension (read only), TSV (read only), or delimited text data asset Tables in relational data sources

Data size Any. Data Refinery operates on a sample subset of rows in the data set. The sample size is 1 MB or 10,000 rows, whichever comes first. However, when you run a job for the Data Refinery flow, the entire data set is processed.


Before you can refine data, you need a project.

If you have data in cloud or on-premises data sources, you'll need to add connections to those sources and you'll need to add data assets from each connection. If you want to be able to save refined data to cloud or on-premises data sources, create connections for this purpose as well. Source connections can be used only to read data; target connections can be used only to load (save) data. When you create a target connection, be sure to use credentials that have Write permission or you won't be able to save your Data Refinery flow output to the target.

Source file limitations

CSV files

Be sure that CSV files are correctly formatted and conform to the following rules:

White-space characters are considered as part of the data

If your data includes columns that contain white space (blank) characters, Data Refinery considers those white-space characters as part of the data, even though you can't see them in the grid. Some database tools might pad character strings with white-space characters to make all the data in a column the same length and this change affects the results of Data Refinery operations that compare data.

Column names

Be sure that column names conform to the following rules:

Data sets with columns with the "Other" data type are not supported in Data Refinery flows

If your data set contains columns that have data types that are identified as "Other" in the Watson Studio preview, the columns will show as the String data type in Data Refinery. However, if you try to use the data in a Data Refinery flow, the job for the Data Refinery flow will fail. An example of a data type that shows as "Other" in the preview is the Db2 DECFLOAT data type.

Target file limitations

The following limitations apply if you save Data Refinery flow output (the target data set) to a file:

Data set previews

Data Refinery provides support for large data sets, which can be time-consuming and unwieldy to refine. To enable you to work quickly and efficiently, it operates on a subset of rows in the data set while you interactively refine the data. When you run a job for the Data Refinery flow, it operates on the entire data set.

Refine your data

The following video shows you how to refine data.

This video provides a visual method as an alternative to following the written steps in this documentation.

1. Access Data Refinery from within a project. Click Add to project, and then choose Data Refinery flow. Then [select the data that you want to work with. Alternatively, from the Assets tab of a project page, you can do one of the following actions:

Tip: If your data doesn't display in tabular form, go to the Data tab. Scroll down to the SOURCE FILE information. Click the "Specify data format" icon. For more information, see specify the format of your data source.

2. Use steps to apply operations that cleanse, shape, and enrich your data. Browse operation categories or search for a specific operation, then let the UI guide you. You can enter R code in the command line and let autocomplete assist you in getting the correct syntax. As you apply operations to a data set, Data Refinery keeps track of them and builds a Data Refinery flow. For each operation that you apply, Data Refinery adds a step.

Data tab
Data tab

If your data contains non-string data types, the Convert column type GUI operation is automatically applied as the first step in the Data Refinery flow when you open a file in Data Refinery. Data types are automatically converted to inferred data types, such as Integer, Date, or Boolean. You can undo or edit this step.

3. Click the Profile tab to validate your data throughout the data refinement process.

Profile tab
Profile tab

4. Click the Visualizations tab to visualize the data in charts. Uncover patterns, trends, and correlations within your data.

Visualizations tab
Visualizations tab

5. Refine the sample data set to suit your needs.

6. Optional: In the Information pane Details tab, click the Edit button to change the Data Refinery flow details and output file information and location.

In the DATA REFINERY FLOW DETAILS pane, click the Edit icon to edit the Data Refinery flow name and description. By default, Data Refinery uses the name of the data source to name the Data Refinery flow and the target data set. You can change these names, but you can't change the project that these data assets belong to.

In the DATA REFINERY FLOW OUTPUT pane, click Edit output to edit the target data set's name description or location. Select whether the first line of the output file contains the column headers. You can save the target data set to the project, to a connection, or to a connected data asset. If you save it to the project, you can save it as a new data asset (default) or you can replace an existing data asset. Edit the location to save the target data set to a connection or to replace an existing data asset or an existing connected data asset. Alternatively, if the location is set to Data assets, you can edit the name in the Data set name field to specify a data asset as the target. The target data set must be a different data set than the source data set.

If you select an existing relational database table or view or you select a connected relational data asset as the target for your Data Refinery flow output, select an option for the existing data set:

For the Update and Upsert options, you need to select the columns in the output data set to compare to columns in the existing data set. The output and target data sets must have the same number of columns, and the columns must have the same names and data types in both data sets.

If you select a file in a connection as the target for your Data Refinery flow output, you can select one of the following formats for that file:

7. Click Save and create a job or Save and view jobs in the toolbar to run the Data Refinery flow on the entire data set. Select the runtime and add a one-time or repeating schedule. For information about jobs, see Creating jobs in Data Refinery.

For the actions that you can do as you refine your data, see Managing Data Refinery flows.

Learn more

Manage Data Refinery flows

Parent topic: Integrating and preparing data