You can create a pipeline that reads a CSV file from Google Cloud Storage (GCS). The read pipeline imports the data from the CSV file in GCS to Anaplan Data Orchestrator. It then uses the imported data to create or update a source dataset.

Prerequisites

You need a connection to Google Cloud Storage to create a pipeline. Make sure you meet the prerequisites in these sections before you create a connection to GCS and a read pipeline.

Create a connection to Google Cloud Storage

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 Google Cloud Storage 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: Optionally, enter a description about your connection.
  7. Enter your Google credentials on the Connection credentials screen, and then select Next:
    • GCP Service Account Email: Enter your GCP service account email (for example, XXXXXXXXXXX-compute@developer.gserviceaccount.com). 
    • GCP Private Key: Enter GCP private key
      For example:

-----BEGIN PRIVATE KEY-----

************************************************************

... (Your private key content in Base64) ...

... multiple lines ...

-----END PRIVATE KEY-----

    • Bucket name: The GCS bucket where your data is stored (for example, GCS-EXAMPLE-BUCKET).
  1. After the connection test is complete, select Done.

Create a read pipeline

Use the Google Cloud Storage connection you created to create the read pipeline. The read pipeline uses the connection to import data from a CSV file in GCS to a dataset in Data Orchestrator.

To create a read 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 GCS from the Connection type dropdown. 
    2. Select the GCS connection you created from the Choose connection dropdown.
    3. Enter a new Label to change the source name that displays in the designer view.
    4. Enter the Path pattern / name for the GCS CSV file.
    5. Optionally, select the Incremental load checkbox.
    6. Select the Column Separator used in the CSV file from the dropdown.
    7. Select the Text delimiter used in the CSV file from the dropdown.
    8. Optionally, enter the Escape character used in the CSV file.
    9. Select the Contains header row checkbox if the CSV file contains a header row.
    10. Select the Skip first data row checkbox if you want the pipeline to skip the first row in the CSV file.
    11. Select Configure columns, review the column types and aliases, 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 Anaplan from the Connection type dropdown.
    2. Select Datasets from the Choose connection dropdown.
    3. Select Target location > Table, and choose a dataset destination:
      • Choose existing dataset: If you choose this option, you will be asked to select an existing source dataset.
      • Create new dataset: If you choose this option, you will be asked to enter a Name and Description for the new dataset.
    4. Choose a write option for the dataset.
      If you chose Create a new dataset in the previous step, the write option only applies to later pipeline runs if you update the CSV file in GCS.
      • Upsert: Updates the existing rows and adds new rows if needed.
      • Append: Adds new data to the dataset without overwriting existing data.
      • Replace: Replaces all existing data with the new data being extracted, and overwrites any previous data.
    5. Review the source data from the CSV file that's being imported to the dataset, and then select Done.
  9. Select Publish, and then select Run to execute the data transfer.

The dataset displays in the Source datasets screen in Data Orchestrator. If you don't see the dataset, refresh the screen.

Run the pipeline with a Workflow

After you create the pipeline, optionally, you can choose to run the pipeline as part of an Anaplan Workflow. This enables you to automate data transfers based on a schedule, or trigger them manually as part of a larger sequence of tasks.