Data Refinery environments (Watson Studio and Watson Knowledge Catalog)

In Data Refinery, a Spark R runtime is started when you shape your data in Data Refinery and when you run a Data Refinery flow in a job.

• Shaping data in Data Refinery
• Running a Data Refinery flow
• Environment options in jobs
• Default environment definitions
• Runtime logs for jobs

Shaping data in Data Refinery

When you select to refine data in Data Refinery, a Data Refinery runtime is started under the covers and is listed as an active runtime on the Environments page of your project.

Remember to stop the runtime from the Environments page of your project.

Running a Data Refinery flow

You can create a job in which to run your Data Refinery flow:

• Directly in Data Refinery by clicking from the Data Refinery toolbar and creating a job
• From your project's Jobs page
• From your project's Assets page by selecting the Data Refinery flow and clicking ACTIONS > Create job

Environment options in jobs

When you create a job in which to run a Data Refinery flow, you can select to use one the following environments:

• Spark & R 3.6 environments

  Service Spark environments are not available by default. An administrator must install the Analytics Engine Powered by Apache Spark service on the IBM Cloud Pak for Data platform. To determine whether the service is installed, open the Services catalog and check whether the service is enabled.

  With a Spark & R 3.6 environment, the Data Refinery flow runs in its own Spark cluster. Each Spark environment consists of one SparkR kernel as a service. The kernel has a dedicated Spark cluster and Spark executors. You should use a Spark & R 3.6 environment only if you are working on a large data set. If your data set is small, you should select the Default Data Refinery XS runtime. The reason is that, although the SparkR cluster in a Spark R environment is fast and powerful, it requires time to create, which is noticeable when you run a Data Refinery job on small data set.

  You can select a default environment definition included in Watson Studio or create your own Spark & R 3.6 environment definition.

  If you create your own Spark & R 3.6 environment definition, you can configure the size of the Spark driver and the size and number of the executors dependent on the size of the data set.

  You should always select a Spark & R 3.6 environment to run Data Refinery flows that operate on large data sets.

  All Spark & R 3.6 environments are HIPAA ready.

• Default Data Refinery XS

  The Default Data Refinery XS runtime is used when you refine data in Data Refinery and can also be selected as the environment runtime when you create a job in which to run your Data Refinery flow.

  You should select the Default Data Refinery XS runtime to run Data Refinery flows that operate on small data sets because the runtime is instantly available and doesn't first have to be started before the job can run.

  The Default Data Refinery XS runtime is HIPAA ready.

• Hadoop cluster

  Service Hadoop environments are not available by default. An administrator must install the Analytics Engine Powered by Apache Spark service on the IBM Cloud Pak for Data platform. To determine whether the service is installed, open the Services catalog and check whether the service is enabled.

  If you want to refine HDFS data on a Hadoop cluster, you can run your Data Refinery jobs directly on the Hadoop cluster.

After the runtime was started, it is listed as an active runtime on the Environments page of your project. The runtime is stopped when the Data Refinery job stops running.

Default environment definitions

Watson Studio offers the following default Spark R environment definition that you can use when you create a job in which to run a Data Refinery flow. Selecting this Spark environment definition helps you to quickly get started running Data Refinery jobs without having to create your own Spark R environment definition. The default environment definition is listed on the project's Environments page.

Runtime logs for jobs

To view the accumulated logs for a Data Refinery job:

1. From the project's Jobs page, click the job that ran the Data Refinery flow for which you want to see logs.
2. Click the job run. You can view the log tail or download the complete log file.

Next steps

• Creating your own environment definition
• Creating jobs in Data Refinery
• Running a Data Refinery flow in a Hadoop environment
• Stopping active runtimes

Parent topic: Environments