You can create a pipeline that reads a CSV file from Amazon S3. The read pipeline imports the data from the CSV file in Amazon S3 to Anaplan Data Orchestrator. It then uses the imported data to create or update a source dataset.

Prerequisites

Before you can create a pipeline, you need the prerequisite items listed in this table.

ItemNotes
Amazon Web Services (AWS) S3 bucket 

An S3 bucket hosted on AWS or any S3-compatible cloud service.

You must have active access to a S3 bucket where the exported file will be stored. 

Configured S3 connection You must configure a valid S3 connection in Data Orchestrator. This process requires an AWS access key ID and an AWS secret access key associated with an IAM user. 
Appropriate permissions The IAM user whose credentials are used in the connection must have the necessary permissions to write to the target S3 bucket. This typically includes the s3:PutObject permission for the specified bucket and object key prefix. 
CSV file in Amazon S3

For the CSV file, make sure that:

  • You specify the prefix or folder path if you restrict access to a subset of the bucket.
  • CSV files are UTF-8 encoded.

See File requirements for Anaplan Data Orchestrator for more information about CSV file requirements.

Create a connection with Amazon S3

Use the S3 connector in Data Orchestrator to create a connection.

You will need your S3 credentials to connect your S3 data with Data Orchestrator. View the S3 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 on the left-side panel.
  4. Select Create connection.
  5. On the Create connections page, select S3 and then select Next.
    If you can't find the connector, enter a search term in the Find... field.
  6. On the Connection details page, enter these details and 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. On the Connection Credentials page, enter your S3 credentials and select Next:
    • AWS Key: The access key ID (for example, AKIAIOSFODNN7EXAMPLE). 
    • AWS Secret Key: The secret access key (for example, wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY).
    • Bucket: The S3 bucket where your data is stored (for example, S3-EXAMPLE-BUCKET).
  8. After the connection test is complete, select Done.

Create a read pipeline

Use the Amazon S3 connection you created to create the read pipeline. The read pipeline uses the connection to import data from a CSV file in Amazon S3 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 S3 from the Connection type dropdown. 
    2. Select the S3 connection you created from the Choose connection dropdown.
    3. Enter a Label that displays as the source name in the designer view.
    4. Enter the Path pattern / name for the S3 CSV file.
      To find the S3 CSV path, open the CSV file in S3, and copy the Key shown under the Properties tab.
    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.
        When you select a write option for the pipeline, it only applies to later pipeline runs if you update the CSV file in S3.
    4. Choose a write option for the dataset:
      • 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.