How to Build an ETL App for Apache Airflow Data in Python with CData

Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Create ETL applications and real-time data pipelines for Apache Airflow 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 API Driver for Python and the petl framework, you can build Apache Airflow-connected applications and pipelines for extracting, transforming, and loading Apache Airflow data. This article shows how to connect to Apache Airflow with the CData Python Connector and use petl and pandas to extract, transform, and load Apache Airflow data.

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

Connecting to Apache Airflow Data

Connecting to Apache Airflow 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.

Start by setting the Profile connection property to the location of the ApacheAirflow Profile on disk (e.g. C:\profiles\ApacheAirflow.apip). Next, set the ProfileSettings connection property to the connection string for ApacheAirflow (see below).

ApacheAirflow API Profile Settings

Apache Airflow 3 uses JWT Bearer tokens for API authentication. You can generate a token from the Airflow web UI under Settings or via the Airflow CLI using airflow users create and the /api/v2/auth/token endpoint. Note that this profile targets Apache Airflow 3.x using the /api/v2 REST API. The legacy /api/v1 endpoint used by Airflow 2.x is not supported.

After setting the following connection properties, you are ready to connect:

  • AuthScheme: Set this to APIKey.
  • APIKey: Set this to your Apache Airflow JWT Bearer token.
  • Server: Set this to the base URL of your Airflow instance (e.g. http://localhost:8080).

After installing the CData Apache Airflow Connector, follow the procedure below to install the other required modules and start accessing Apache Airflow 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 Apache Airflow 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.api as mod

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

cnxn = mod.connect("Profile=C:\profiles\ApacheAirflow.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_jwt_token;Server=http://localhost:8080';")

Create a SQL Statement to Query Apache Airflow

Use SQL to create a statement for querying Apache Airflow. In this article, we read data from the DagRuns entity. 

sql = "SELECT DagRunId, State FROM DagRuns WHERE DagId = 'example_dag'"

Extract, Transform, and Load the Apache Airflow Data

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

Loading Apache Airflow Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Apache Airflow 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 API Driver for Python to start building Python apps and scripts with connectivity to Apache Airflow 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\ApacheAirflow.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_jwt_token;Server=http://localhost:8080';")

sql = "SELECT DagRunId, State FROM DagRuns WHERE DagId = 'example_dag'"

table1 = etl.fromdb(cnxn,sql)

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

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

Ready to get started?

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