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Python Connector Libraries for Microsoft Dataverse Connectivity. Integrate Microsoft Dataverse with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to Build an ETL App for Microsoft Dataverse Data in Python with CData

Create ETL applications and real-time data pipelines for Microsoft Dataverse data in Python with petl.

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

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Microsoft Dataverse data in Python. When you issue complex SQL queries from Microsoft Dataverse, the driver pushes supported SQL operations, like filters and aggregations, directly to Microsoft Dataverse and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Microsoft Dataverse Data

Connecting to Microsoft Dataverse data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

You can connect without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.

  • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
  • OrganizationUrl: Set this to the organization URL you are connecting to, such as
  • Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.

When you connect the Common Data Service OAuth endpoint opens in your default browser. Log in and grant permissions. The OAuth process completes automatically.

After installing the CData Microsoft Dataverse Connector, follow the procedure below to install the other required modules and start accessing Microsoft Dataverse through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Microsoft Dataverse Data in Python

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

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.cds as mod

You can now connect with a connection string. Use the connect function for the CData Microsoft Dataverse Connector to create a connection for working with Microsoft Dataverse data.

cnxn = mod.connect("OrganizationUrl=;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Microsoft Dataverse

Use SQL to create a statement for querying Microsoft Dataverse. In this article, we read data from the Accounts entity.

sql = "SELECT AccountId, Name FROM Accounts WHERE Name = 'MyAccount'"

Extract, Transform, and Load the Microsoft Dataverse Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Microsoft Dataverse data. In this example, we extract Microsoft Dataverse data, sort the data by the Name column, and load the data into a CSV file.

Loading Microsoft Dataverse Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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


In the following example, we add new rows to the Accounts table.

Adding New Rows to Microsoft Dataverse

table1 = [ ['AccountId','Name'], ['NewAccountId1','NewName1'], ['NewAccountId2','NewName2'], ['NewAccountId3','NewName3'] ]

etl.appenddb(table1, cnxn, 'Accounts')

With the CData Python Connector for Microsoft Dataverse, you can work with Microsoft Dataverse data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Microsoft Dataverse to start building Python apps and scripts with connectivity to Microsoft Dataverse data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.cds as mod

cnxn = mod.connect("OrganizationUrl=;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT AccountId, Name FROM Accounts WHERE Name = 'MyAccount'"

table1 = etl.fromdb(cnxn,sql)

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


table3 = [ ['AccountId','Name'], ['NewAccountId1','NewName1'], ['NewAccountId2','NewName2'], ['NewAccountId3','NewName3'] ]

etl.appenddb(table3, cnxn, 'Accounts')