You can create a pipeline that writes back data from an Anaplan Data Orchestrator (ADO) object to a target table in Google BigQuery.

Prerequisites

You need a connection to Google BigQuery to create a pipeline. Make sure you meet the prerequisites in these sections before you create a connection to BigQuery and a writeback pipeline.

Create a connection to Google BigQuery

You will need your Google BigQuery credentials to connect the BigQuery data with Data Orchestrator. View the BigQuery documentation for more information about your credentials.

To create a connection:

  1. Select Data Orchestrator from the top-left navigation menu.
  2. Choose a dataspace from the list.
  3. Select Connections from the left-side panel.
  4. Select Create connection.
  5. Select the BigQuery connector and then select Next.
    If you can't find the connector, enter a search term in the Find... field.
  6. Enter these details on the Connection details screen, and then select Next:
    • Name: Create a name for your connection. The name can contain alphanumeric characters and underscores.
    • Description: Enter a description about your connection.
  7. Enter your BigQuery credentials on the Connection credentials screen, and then select Next.
    • Project ID: The identifier for the BigQuery project that contains the dataset (for example, myproject-1234).
    • Credentials Json: The BigQuery JSON code for authentication.
    • Dataset ID: The BigQuery dataset ID.
      (If you use the Google BigQuery connection to extract data, only the views defined under the Dataset ID for the connection definition are visible under Path name.)
  8. After the connection test is complete, select Done.

Create a writeback pipeline

When you set up the writeback pipeline, you'll use your connection to export an object from ADO to Google BigQuery.

To create a writeback pipeline:

  1. Select Data Orchestrator from the top-left navigation menu.
  2. Choose a dataspace from the list.
  3. Select Pipelines from the left-side panel.
  4. Select Create pipeline.
  5. Enter a Name for your pipeline and then select Create.
    You are taken to the pipeline designer view.
  6. Select the Source icon, and then complete these steps in the right-side panel:
    1. Select Anaplan from the Connection type dropdown. 
    2. Select Datasets from the Choose connection dropdown.
    3. Enter a new Label to change the source display name in the designer view.
    4. Select Source location > Source, choose a Data Orchestrator dataset, and then select Confirm.
      The dataset is used as the source for your pipeline.
    5. Select Columns, choose which columns from the dataset you want to include, optionally choose a cursor column, and then select Done.
  7. Optionally, select the add icon that appears between the Source and Sink nodes.
    You can add steps to your pipeline to process data.
  8. Select the Sink icon, and then complete these steps in the right-side panel:
    1. Select BigQuery from the Connection type dropdown.
    2. Select the BigQuery connection you created from the Choose connection dropdown.
    3. Enter a new Label to change the sink name that displays in the designer view.
    4. Select Target location > Table, select a BigQuery table, and then select Done.
    5. Select Target mapping > Mapping, map the Data Orchestrator source values to the BigQuery target values, and then select Done.
    6. Select a Write option for the target table: Append, Full replace, or Upsert.
      This determines how data is written to the target table. See the Write options section below for more details.
  9. Select Publish, and then select Run to execute the data transfer.

Write options

Review this table to determine which write option to select.

Write optionsDescription
Append

Adds all rows from the source data to the existing rows in the target table.

If you select Append, a staging table in BigQuery is required:

  • To automatically create a staging table in BigQuery:
    1. Expand the Advanced options section.
    2. Select Create a staging table automatically from the Staging table dropdown.
  • To manually specify an existing staging table in BigQuery: 
    1. Expand the Advanced options section.
    2. Select an existing staging table from the Staging table dropdown.

See the Staging table section below for more information.

Note: Append load isn't suitable for target tables with primary keys. 

Full replace

Deletes all existing data in the target table and replaces it with the source data.

If you select Full replace, a staging table in BigQuery is optional. A staging table is recommended and selected by default in the Advanced options section: 

  • If you don't want to create a staging table, don't select ‌the Stage data before replacing the target checkbox.
  • If you want to create a staging table: 
    1. Select the Stage data before replacing the target checkbox.
    2. Specify the staging table details:
      • To automatically create a staging table, select Create a staging table automatically from the Staging table dropdown.
      • To manually specify an existing staging table, select an existing staging table from the Staging table dropdown.

See the Staging table section below for more information.

Upsert

Updates existing rows and adds new rows based on a specified key. 

If you select Upsert, a staging table in BigQuery is required:

  1. Select a Primary key.
    The columns you select are used to identify existing rows when using the upsert write option.
  2. Expand the Advanced options section, and specify the staging table details:
    • To automatically create a staging table, select Create a staging table automatically from the Staging table dropdown.
    • To manually specify an existing staging table, select an existing staging table from the Staging table dropdown.

See the Staging table section below for more information.

Staging tables

Staging tables are used to compare and process data before Data Orchestrator loads the data to the final target table in BigQuery.

If you want to use a staging table, you have the option to:

  • Automatically let Data Orchestrator create a staging table in BigQuery.
  • Manually specify an existing staging table in BigQuery.

This table describes how staging tables are used in BigQuery for each option.

Staging table optionsResults
Automatically create a staging table in BigQuery
  • Data Orchestrator automatically creates a temporary staging table in BigQuery. BigQuery then merges the data in the temporary table with the target BigQuery table you selected for the mapping. 
  • After the data has been successfully loaded into the target table, ADO deletes the temporary staging table from BigQuery.
Manually specify an existing staging table from BigQuery
  • The data is pushed from Data Orchestrator to the BigQuery staging table you specified.
  • BigQuery automatically merges the Data Orchestrator data in the staging table with the target BigQuery table you selected for the mapping.
  • At the end of the merge, the manually created staging table remains in BigQuery.

Note: To prevent data mismatch errors with BigQuery, make sure your data in Data Orchestrator and in BigQuery have a schema alignment. This means the tables in Data Orchestrator and in BigQuery must have the same columns and data types.

Verify the writeback in Google BigQuery 

Once the pipeline is successfully completed, you can log in to your BigQuery account to verify the data load. 

Navigate to the target database, schema, and table specified in the pipeline configuration. The data written from the ADO object is available in the target table, based on the selected write option (append, full replace, or upsert).