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

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 Datadog-connected applications and pipelines for extracting, transforming, and loading Datadog data. This article shows how to connect to Datadog with the CData Python Connector and use petl and pandas to extract, transform, and load Datadog data.

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

Connecting to Datadog Data

Connecting to Datadog 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 Datadog Profile on disk (e.g. C:\profiles\Datadog.apip). Next, set the ProfileSettings connection property to the connection string for Datadog (see below).

Datadog API Profile Settings

In your Datadog account, navigate to Organization Settings > API Keys to create an API Key, and Organization Settings > Application Keys to create an Application Key. Both are required.

After installing the CData Datadog Connector, follow the procedure below to install the other required modules and start accessing Datadog 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 Datadog 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 Datadog Connector to create a connection for working with Datadog data.

cnxn = mod.connect("Profile=C:\profiles\Datadog.apip;ProfileSettings='APIKey=your_api_key;ApplicationKey=your_app_key';")

Create a SQL Statement to Query Datadog

Use SQL to create a statement for querying Datadog. In this article, we read data from the APMRetentionFilters entity.

sql = "SELECT FilterId, Name FROM APMRetentionFilters WHERE IsEnabled = 'true'"

Extract, Transform, and Load the Datadog Data

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

Loading Datadog Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Datadog 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 Datadog 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\Datadog.apip;ProfileSettings='APIKey=your_api_key;ApplicationKey=your_app_key';")

sql = "SELECT FilterId, Name FROM APMRetentionFilters WHERE IsEnabled = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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