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

Prerequisites

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

Create a connection to Azure Blob Storage

You will need your Azure Blob Storage credentials to connect the Azure Blob Storage data with Data Orchestrator. View the Azure Blob Storage 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 Azure Blob 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: Enter a description about your connection.
  7. Enter your Azure Blob Storage credentials on the Connection credentials screen, and then select Next.
    For information about the fields on this page, see Authentication options for Azure Blob Storage connections.
  8. After the connection test is complete, select Done.

Create a read pipeline

Use the Azure Blob Storage connection you created to create the read pipeline. The read pipeline uses the connection to import data from a CSV file in Azure Blob Storage 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 Azure Blob from the Connection type dropdown. 
    2. Select the Azure 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 Azure 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 Azure.
      • 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.