How to work with Airtable Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Airtable, Spark can work with live Airtable data. This article describes how to connect to and query Airtable data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Airtable data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Airtable data using native data types.

Install the CData JDBC Driver for Airtable

Download the CData JDBC Driver for Airtable installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Airtable Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Airtable JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Airtable/lib/cdata.jdbc.airtable.jar
   

2. With the shell running, you can connect to Airtable with a JDBC URL and use the SQL Context load() function to read a table.

   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.

   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.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Airtable, using the connection string generated above.

   scala> val airtable_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:airtable:APIKey=keymz3adb53RqsU;BaseId=appxxN2fe34r3rjdG7;TableNames=Table1,...;ViewNames=Table1.View1,...;").option("dbtable","SampleTable_1").option("driver","cdata.jdbc.airtable.AirtableDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Airtable data as a temporary table:

   scala> airtable_df.registerTable("sampletable_1")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> airtable_df.sqlContext.sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = SomeValue").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Airtable in Apache Spark, you are able to perform fast and complex analytics on Airtable data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.