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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 Vault CRM data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Vault CRM data in Databricks.
With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Vault CRM data. When you issue complex SQL queries to Vault CRM, the driver pushes supported SQL operations, like filters and aggregations, directly to Vault CRM 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 Vault CRM data using native data types.
To work with live Vault CRM data in Databricks, install the driver on your Databricks cluster.
With the JAR file installed, we are ready to work with live Vault CRM 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 Vault CRM, and create a basic report.
Connect to Vault CRM 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.
driver = "cdata.jdbc.vaultcrm.VaultCRMDriver" url = "jdbc:vaultcrm:RTK=5246...;User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;"
For assistance in constructing the JDBC URL, use the connection string designer built into the Vault CRM JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.vaultcrm.jar
Fill in the connection properties and copy the connection string to the clipboard.
You are ready to connect after specifying the following connection properties:
Once you configure the connection, you can load Vault CRM 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" , "NorthwindProducts") \ .load ()
Check the loaded Vault CRM data by calling the display function.
display (remote_table.select ("ProductId"))
๐ Displaying Vault CRM DataIf you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.
remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )
With the Temp View created, you can use SparkSQL to retrieve the Vault CRM data for reporting, visualization, and analysis.
% sql SELECT ProductId, ProductName FROM SAMPLE_VIEW ORDER BY ProductName DESC LIMIT 5๐ Displaying Vault CRM Data
The data from Vault CRM 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 Vault CRM and start working with your live Vault CRM data in Databricks. Reach out to our Support Team if you have any questions.
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