Process & Analyze Spark Data in Databricks (AWS)

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Apache Spark JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Apache Spark.



Host the CData JDBC Driver for Spark in AWS and use Databricks to perform data engineering and data science on live Spark data.

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 Spark data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Spark data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live Spark 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.sparksql.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Spark\lib).

Access Spark Data in your Notebook: Python

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

Configure the Connection to Spark

Connect to Spark by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.sparksql.SparkSQLDriver"
url = "jdbc:sparksql:Server=127.0.0.1;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.sparksql.jar

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

Set the Server, Database, User, and Password connection properties to connect to SparkSQL.

Load Spark Data

Once you configure the connection, you can load Spark 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" , "Customers") \
	.load ()

Display Spark Data

Check the loaded Spark data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("City"))

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

% sql

SELECT City, Balance FROM SAMPLE_VIEW ORDER BY Balance DESC LIMIT 5

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