Process & Analyze BigQuery 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 BigQuery data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live BigQuery data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live BigQuery data. When you issue complex SQL queries to BigQuery, the driver pushes supported SQL operations, like filters and aggregations, directly to BigQuery 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 BigQuery data using native data types.

Getting Started

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Install the CData JDBC Driver in Databricks

To work with live BigQuery 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.googlebigquery.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

👁 Loading the JDBC JAR File into AWS

Access BigQuery Data in your Notebook: Python

With the JAR file installed, we are ready to work with live BigQuery 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 BigQuery, and create a basic report.

Configure the Connection to BigQuery

Connect to BigQuery by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the 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.googlebigquery.GoogleBigQueryDriver"
url = "jdbc:googlebigquery:RTK=5246...;DataSetId=MyDataSetId;ProjectId=MyProjectId;InitiateOAuth=GETANDREFRESH;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.googlebigquery.jar

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

Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.

In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

Load BigQuery Data

Once you configure the connection, you can load BigQuery 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" , "Orders") \
	.load ()

Display BigQuery Data

Check the loaded BigQuery data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("OrderName"))

👁 Displaying BigQuery Data

Analyze BigQuery 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 BigQuery data for reporting, visualization, and analysis.

% sql

SELECT OrderName, Freight FROM SAMPLE_VIEW ORDER BY Freight DESC LIMIT 5

👁 Displaying BigQuery Data

The data from BigQuery 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 Google BigQuery and start working with your live BigQuery data in Databricks. Reach out to our Support Team if you have any questions.