Process & Analyze IBM Cloud SQL Query Data in Databricks (AWS)

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IBM Cloud SQL Query JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with IBM Cloud SQL Query.



Host the CData JDBC Driver for IBM Cloud SQL Query in AWS and use Databricks to perform data engineering and data science on live IBM Cloud SQL Query 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 IBM Cloud SQL Query data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live IBM Cloud SQL Query data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live IBM Cloud SQL Query 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.ibmcloudsqlquery.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for IBM Cloud SQL Query\lib).

Access IBM Cloud SQL Query Data in your Notebook: Python

With the JAR file installed, we are ready to work with live IBM Cloud SQL Query 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 IBM Cloud SQL Query, and create a basic report.

Configure the Connection to IBM Cloud SQL Query

Connect to IBM Cloud SQL Query by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.ibmcloudsqlquery.IBMCloudSQLQueryDriver"
url = "jdbc:ibmcloudsqlquery: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 SQL Query JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.ibmcloudsqlquery.jar

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

IBM Cloud SQL 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 SQL Query Data

Once you configure the connection, you can load IBM Cloud SQL Query 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" , "Jobs") \
	.load ()

Display IBM Cloud SQL Query Data

Check the loaded IBM Cloud SQL Query data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze IBM Cloud SQL Query 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 IBM Cloud SQL Query data for reporting, visualization, and analysis.

% sql

SELECT Id, Status FROM SAMPLE_VIEW ORDER BY Status DESC LIMIT 5

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