Extract, Transform, and Load Active Directory Data in Python

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Active Directory Python Connector

Python Connector Libraries for Active Directory Data Connectivity. Integrate Active Directory with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Active Directory enables you to create ETL applications and pipelines for Active Directory 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 Active Directory and the petl framework, you can build Active Directory-connected applications and pipelines for extracting, transforming, and loading Active Directory data. This article shows how to connect to Active Directory with the CData Python Connector and use petl and pandas to extract, transform, and load Active Directory data.

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

Connecting to Active Directory Data

Connecting to Active Directory 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.

To establish a connection, set the following properties:

  • Valid User and Password credentials (e.g., Domain\BobF or cn=Bob F,ou=Employees,dc=Domain).
  • Server information, including the IP or host name of the Server, as well as the Port.
  • BaseDN: This will limit the scope of LDAP searches to the height of the distinguished name provided.

    Note: Specifying a narrow BaseDN may greatly increase performance; for example, cn=users,dc=domain will only return results contained within cn=users and its children.

After installing the CData Active Directory Connector, follow the procedure below to install the other required modules and start accessing Active Directory 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 Active Directory 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.activedirectory as mod

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

cnxn = mod.connect("User=cn=Bob F,ou=Employees,dc=Domain;Password=bob123;Server=;Port=389;")

Create a SQL Statement to Query Active Directory

Use SQL to create a statement for querying Active Directory. In this article, we read data from the User entity.

sql = "SELECT Id, LogonCount FROM User WHERE CN = 'Administrator'"

Extract, Transform, and Load the Active Directory Data

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

Loading Active Directory Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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


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

Adding New Rows to Active Directory

table1 = [ ['Id','LogonCount'], ['NewId1','NewLogonCount1'], ['NewId2','NewLogonCount2'], ['NewId3','NewLogonCount3'] ]

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

With the CData Python Connector for Active Directory, you can work with Active Directory 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 Active Directory Python Connector to start building Python apps and scripts with connectivity to Active Directory 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.activedirectory as mod

cnxn = mod.connect("User=cn=Bob F,ou=Employees,dc=Domain;Password=bob123;Server=;Port=389;")

sql = "SELECT Id, LogonCount FROM User WHERE CN = 'Administrator'"

table1 = etl.fromdb(cnxn,sql)

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


table3 = [ ['Id','LogonCount'], ['NewId1','NewLogonCount1'], ['NewId2','NewLogonCount2'], ['NewId3','NewLogonCount3'] ]

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