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

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

Install the CData JDBC Driver in Databricks

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

👁 Loading the JDBC JAR File into AWS

Access Redshift Data in your Notebook: Python

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

Configure the Connection to Redshift

Connect to Redshift 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.redshift.RedshiftDriver"
url = "jdbc:redshift:RTK=5246...;User=admin;Password=admin;Database=dev;Server=examplecluster.my.us-west-2.redshift.amazonaws.com;Port=5439;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.redshift.jar

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

To connect to Redshift, set the following:

• : Set this to the host name or IP address of the cluster hosting the Database you want to connect to.
• : Set this to the port of the cluster.
• : Set this to the name of the database. Or, leave this blank to use the default database of the authenticated user.
• : Set this to the username you want to use to authenticate to the Server.
• : Set this to the password you want to use to authenticate to the Server.

You can obtain the and values in the AWS Management Console:

1. Open the Amazon Redshift console (http://console.aws.amazon.com/redshift).
2. On the Clusters page, click the name of the cluster.
3. On the Configuration tab for the cluster, copy the cluster URL from the connection strings displayed.

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

Load Redshift Data

Once you configure the connection, you can load Redshift 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 Redshift Data

Check the loaded Redshift data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("ShipName"))

👁 Displaying Redshift Data

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

% sql

SELECT ShipName, ShipCity FROM SAMPLE_VIEW ORDER BY ShipCity DESC LIMIT 5

👁 Displaying Redshift Data

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