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Extract, Transform, and Load Airtable Data in Python

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

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

Connecting to Airtable Data

Connecting to Airtable 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.

APIKey, BaseId and TableNames parameters are required to connect to Airtable. ViewNames is an optional parameter where views of the tables may be specified.

  • APIKey : API Key of your account. To obtain this value, after logging in go to Account. In API section click Generate API key.
  • BaseId : Id of your base. To obtain this value, it is in the same section as the APIKey. Click on Airtable API, or navigate to https://airtable.com/api and select a base. In the introduction section you can find "The ID of this base is appxxN2ftedc0nEG7."
  • TableNames : A comma separated list of table names for the selected base. These are the same names of tables as found in the UI.
  • ViewNames : A comma separated list of views in the format of (table.view) names. These are the same names of the views as found in the UI.

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

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

cnxn = mod.connect("APIKey=keymz3adb53RqsU;BaseId=appxxN2fe34r3rjdG7;TableNames=TableA,...;ViewNames=TableA.ViewA,...;")

Create a SQL Statement to Query Airtable

Use SQL to create a statement for querying Airtable. In this article, we read data from the SampleTable_1 entity.

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column1 = 'Value1'"

Extract, Transform, and Load the Airtable Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Airtable data. In this example, we extract Airtable data, sort the data by the Column1 column, and load the data into a CSV file.

Loading Airtable Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')

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

cnxn = mod.connect("APIKey=keymz3adb53RqsU;BaseId=appxxN2fe34r3rjdG7;TableNames=TableA,...;ViewNames=TableA.ViewA,...;")

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column1 = 'Value1'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')