How to Visualize Apache Airflow Data in Python with pandas

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Apache Airflow-connected Python applications and scripts for visualizing Apache Airflow data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Apache Airflow data, execute queries, and visualize the results.

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).

Follow the procedure below to install the required modules and start accessing Apache Airflow through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Apache Airflow Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Apache Airflow data.

engine = create_engine("api:///?Profile=C:\profiles\ApacheAirflow.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_jwt_token&Server=http://localhost:8080'")

Execute SQL to Apache Airflow

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT DagRunId, State FROM DagRuns WHERE DagId = 'example_dag'", engine)

Visualize Apache Airflow Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Apache Airflow data. The show method displays the chart in a new window.

df.plot(kind="bar", x="DagRunId", y="State")
plt.show()

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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("api:///?Profile=C:\profiles\ApacheAirflow.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_jwt_token&Server=http://localhost:8080'")
df = pandas.read_sql("SELECT DagRunId, State FROM DagRuns WHERE DagId = 'example_dag'", engine)

df.plot(kind="bar", x="DagRunId", y="State")
plt.show()