How to connect and process Airtable data from Azure Databricks

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 Azure, 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 Azure

To work with live Airtable data in Databricks, install the driver through Azure Data Lake Storage (ADLS). (Please note that the method of connecting through DBFS, which previous versions of this article described, has been deprecated, but has not published an end-of-life.)

1. Upload the JDBC JAR file to a blob container of your choice (i.e. "jdbcjars" container of the "databrickslibraries" storage account).
2. Fetch the Account Key from the storage account by expanding "Security + networking" and clicking on "Access Keys". Show and copy whichever of the two keys you wish to use.
3. Get the JDBC JAR file's URL by navigating to Containers, opening the specific container storing the JAR, and selecting the entry for the JDBC JAR file. This should open the file's details, where there should be a convenient button to copy the URL button to clipboard. This value will look similar to the below, though the "blob" component may vary depending on storage account type:

   https://databrickslibraries.blob.core.windows.net/jdbcjars/cdata.jdbc.salesforce.jar

   
4. In the Configuration tab of your Databricks cluster, click on the Edit button and expand "Advanced options". From there, add the following Spark option (derived from the JAR URL's domain name) with your copied Account key as its value and click Confirm: spark.hadoop.fs.azure.account.key.databrickslibraries.blob.core.windows.net
5. In the Libraries tab of your Databricks cluster, click on "Install new", and select the ADLS option. Specify the ABFSS URL for the driver JAR (also derived from the JAR URL's domain name), and click Install. The ABFSS URL should resemble the below:

   abfss://[email protected]/cdata.jdbc.salesforce.jar

   

Connect to Airtable from Databricks

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 workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

Configure the Connection to Airtable

Connect to Airtable by referencing the class for the JDBC Driver 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.

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.

java -jar cdata.jdbc.airtable.jar

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

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.

display (remote_table.select ("Id"))

Analyze Airtable Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the Airtable data for analysis.

result = spark.sql("SELECT Id, Column1 FROM SAMPLE_VIEW WHERE Column2 = 'SomeValue'")

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 Azure Databricks. Reach out to our Support Team if you have any questions.