Process & Analyze Airtable Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Airtable data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Airtable data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Airtable data. When you issue complex SQL queries to 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). Its built-in dynamic metadata querying allows you to work with and analyze Airtable data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Airtable data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.airtable.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Airtable Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Airtable data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Airtable, and create a basic report.

Configure the Connection to Airtable

Connect to Airtable by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.airtable.AirtableDriver"
url = "jdbc:airtable:RTK=5246...;APIKey=keymz3adb53RqsU;BaseId=appxxN2fe34r3rjdG7;TableNames=Table1,...;ViewNames=Table1.View1,...;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Airtable JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

Fill in the connection properties and copy the connection string to the clipboard.

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.

Load Airtable Data

Once you configure the connection, you can load Airtable data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "SampleTable_1") \
	.load ()

Display Airtable Data

Check the loaded Airtable data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze Airtable Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Airtable data for reporting, visualization, and analysis.

% sql

SELECT Id, Column1 FROM SAMPLE_VIEW ORDER BY Column1 DESC LIMIT 5

The data from Airtable is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Airtable and start working with your live Airtable data in Databricks. Reach out to our Support Team if you have any questions.