How to Build an ETL App for Anaplan Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK and the petl framework, you can build Anaplan-connected applications and pipelines for extracting, transforming, and loading Anaplan data. This article shows how to connect to Connect AI and use petl to extract, transform, and load Anaplan data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to Anaplan in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Anaplan" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Anaplan.

   Authenticating to Anaplan

   The driver supports authenticating with Basic, Certificate, or OAuth. In every case, set Region to the region where your Anaplan account data is hosted (e.g., US1, which is the default).

   Using Basic Authentication

   Set AuthScheme to Basic, then supply your Anaplan User and Password. If your workspace uses single sign-on (SSO), you must be assigned as an Exception User to use Basic authentication.

   Using Certificate Authentication

   Set AuthScheme to Certificate, then supply the Certificate, CertificateType, and PrivateKey properties (and the matching CertificatePassword / PrivateKeyPassword if either is encrypted). The certificate must be a CA-issued X.509 certificate registered with your Anaplan tenant administrator.

   Using OAuth Authentication

   Register a custom OAuth application in Anaplan, then set the following properties:

   • OAuthClientId: The client Id assigned when you registered your custom OAuth application.
   • OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.
   • CallbackURL: The redirect URI defined when you registered your application.
   • InitiateOAuth: Set to GETANDREFRESH to have the driver manage the OAuth token exchange and refresh automatically.

   See the Getting Started chapter of the help documentation for a guide to creating a custom OAuth app and using OAuth.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for Anaplan Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query Anaplan

Use SQL to create a statement for querying Anaplan. In this article, we read data from the Sales entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Anaplan1).

sql = (
    "SELECT Region, Product "
    "FROM [Anaplan1].[Anaplan].[Sales] "
    "WHERE Value = '100'"
)

Extract, Transform, and Load the Anaplan Data

With a connection and query in hand, use petl to extract, transform, and load the Anaplan data. In this example, we extract Anaplan data, sort the data by the Product column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Product')

etl.tocsv(table2, 'sales_data.csv')

Anaplan is a read-only source in Connect AI, so this pipeline can extract and transform Anaplan data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with Anaplan data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live Anaplan data through petl using the CData Connect AI Python SDK. For more information on connecting to Anaplan (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Anaplan data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT Region, Product "
    "FROM [Anaplan1].[Anaplan].[Sales] "
    "WHERE Value = '100'"
)

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Product')

etl.tocsv(table2, 'sales_data.csv')
conn.close()