How to connect and process IBM Cloud Data Engine 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 IBM Cloud Data Engine data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live IBM Cloud Data Engine data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live IBM Cloud Data Engine data in Databricks, install the driver on your Azure cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "DBFS" as the Library Source and "JAR" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.ibmclouddataengine.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for IBM Cloud Data Engine\lib).

Connect to IBM Cloud Data Engine from Databricks

With the JAR file installed, we are ready to work with live IBM Cloud Data Engine 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 IBM Cloud Data Engine

Connect to IBM Cloud Data Engine 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.ibmclouddataengine.IBMCloudDataEngineDriver"
url = "jdbc:ibmclouddataengine:RTK=5246...;Api Key=MyAPIKey;Instance CRN=myInstanceCRN;Region=myRegion;Schema=mySchema;OAuth Client Id=myOAuthClientId;OAuth Client Secret=myOAuthClientSecret;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

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

IBM Cloud Data Engine uses the OAuth and HMAC authentication standards. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

Load IBM Cloud Data Engine Data

Once the connection is configured, you can load IBM Cloud Data Engine 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" , "Jobs") \
	.load ()

Display IBM Cloud Data Engine Data

Check the loaded IBM Cloud Data Engine data by calling the display function.

display (remote_table.select ("Id"))

Analyze IBM Cloud Data Engine 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 IBM Cloud Data Engine data for analysis.

result = spark.sql("SELECT Id, Status FROM SAMPLE_VIEW WHERE UserId = [email protected]")

The data from IBM Cloud Data Engine 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 IBM Cloud Data Engine and start working with your live IBM Cloud Data Engine data in Azure Databricks. Reach out to our Support Team if you have any questions.