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Merging data


Combine data from the primary and secondary paths into a single track to merge an incomplete record with a data record that comes from the secondary data source. After you merge data from two paths, the output records keeps only the unique data from both paths. The Merge shape outputs one or multiple records for every incoming data record depending on the number of records that match the merge condition.

  1. In a data flow, click the Plus icon on a shape, and select Merge.

  2. Double-click the secondary Source shape and configure it.

    When you select a data set, it must be a data set that you can browse by keys, for example, Database Table, Decision Data Store, Event Store, HBase, or Interaction History data set.
  3. Click Submit.

  4. Double-click the Merge shape to configure it.

  5. In the Name field, enter a name for the shape.

  6. Click Add condition and select a property that needs to match between two sources. You can add more than one condition. For example, When .CustomerID is equal to .ID.

  7. Optional:

    Select the Exclude source component results that do not match merge option when there is no data match. If one of the specified properties does not exist, the value of the other property is not included in the class that stores the merge results.

  8. Select which source takes precedence when there are properties with the same name but different values.

    • Primary path - The merge action takes the value in the primary source.
    • Secondary path - The merge action takes the value in the secondary source.
  9. Click Submit.

You can merge a data record that contains Customer ID with banking transactions of this customer. When there are five banking transactions for a single customer, the Merge shape outputs five records for one incoming data record that contains Customer ID. Each of the five records contains the Customer ID and details of a single banking transaction.

  • Branching a data flow

    You create multiple branches in a data flow to create independent paths for processing data in your application. By splitting your data flow into multiple paths, you can decrease the number of Data Flow rules that are required to process data from a single source.

  • Creating a data flow

    Create a data flow to process and move data between data sources. Customize your data flow by adding data flow shapes and by referencing other business rules to do more complex data operations. For example, a simple data flow can move data from a single data set, apply a filter, and save the results in a different data set. More complex data flows can be sourced by other data flows, can apply strategies for data processing, and open a case or trigger an activity as the final outcome of the data flow.

  • Types of data flows

    Data flows are scalable data pipelines that you can build to sequence and combine data based on various data sources. Each data flow consists of components that transform data and enrich data processing with business rules.

  • Types of Data Set rules

    Learn about the types of data set rules that you can create in Pega Platform.

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