About Data Flows

Data flows enable you to organize and integrate your data to produce curated datasets that your users can visualize.

Use data flows to manipulate your data visually without requiring manual coding skills.

For example, you might use a data flow to:

• Create a dataset.

• Combine data from different sources.
• Aggregate data.
• Train machine learning models or apply a predictive machine learning model to your data.
• Perform object detection, image classification, or text detection using artificial intelligence via the OCI Vision service.

You create data flows in the data flow design pane.

Description of the illustration data-flow-designer-new.png

To build a data flow, you add steps. Each step performs a specific function, for example, add data, join tables, merge columns, or transform data. Use the data flow editor to add and configure your steps. Each step is validated when you add or change it. When you've configured your data flow, you execute it to create or update a dataset.

When you add your own columns or transform data, you can use a wide range of SQL operators (for example, BETWEEN, LIKE, IN), conditional expressions (for example, CASE), and functions (for example, Avg, Median, Percentile).

Updating Datasets Generated by Data Flows

You can rerun data flows to keep your datasets up-to-date.

Note:

When you rerun a data flow, any transformations applied directly to the output dataset outside of the data flow are lost. The dataset is recreated from scratch.

Data Flow Limits

If you're processing large amounts of data, note that there're data flow limits. See Data Flow Limits.

Database Support for Data Flows

With data flows you can curate data from datasets and subject areas.

You can execute data flows individually or in a sequence. You can include multiple data sources in a data flow and specify how to join them.

Use the Add Data step to add data to a data flow, and use the Save Data step to save output data from a data flow.

You can save the output data from a data flow in either a dataset or in one of the supported database types. If you save data to a database, you can transform the data source by overwriting it with data from the data flow. The data source and data flow tables must be in the same database and have the same name. Before you start, create a connection to one of the supported database types.

Note:

You can add data from remote databases connected with Data Gateway. However, you can't save data back to remote databases connected with Data Gateway.

Data Output

You can save output data from data flows to these database types:

• Oracle Autonomous AI Lakehouse
• Oracle Autonomous AI Transaction Processing
• Oracle Database
• Apache Hive
• Hortonworks Hive
• MapR Hive
• Spark

For database version information, see Supported Data Sources.

Data Input

In data flows you can process data from datasets and subject areas. You can't pull data directly from databases - you have to create a dataset from the database table(s) first.

Working in the Data Flow Designer

Data flow designer enables you to curate and transform your data in a graphical design environment.

Working in the data flow designer:

Use data flow designer to organize and configure the data flow steps that transform your data.

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Use these features in the data flow designer.

Feature	Icon	Description
Compact layout		Group steps into a smaller view area to reduce scrolling.
Expanded layout		Align input data source steps at the left to improve readbility.
Incomplete join or union		Indicates a data source that isn't joined or unioned. Hover over the blue link icon to see a suggested join target (dotted line), and click again to complete the join or union (solid line). To remove a connection, right-click the Join step or Union step then select Delete.
Zoom enhancements		Zoom in and out.