Use the CData JDBC Driver for Spark in MicroStrategy Desktop

MicroStrategy is an analytics and mobility platform that enables data-driven innovation. When you pair MicroStrategy with the CData JDBC Driver for Spark, you gain database-like access to live Spark data from MicroStrategy, expanding your reporting and analytics capabilities. In this article, we walk through adding Spark as a data source in MicroStrategy Desktop and creating a simple visualization of Spark data.

The CData JDBC Driver offers unmatched performance for interacting with live Spark data in MicroStrategy due to optimized data processing built into the driver. When you issue complex SQL queries from MicroStrategy 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can use native MicroStrategy data types to visualize and analyze Spark data.

Connect to and Visualize Spark Data using MicroStrategy Desktop

In addition to connecting to Spark in MicroStrategy enterprise products, you can connect to Spark in MicroStrategy Desktop. Follow the steps below to add Spark data as a dataset using JDBC and create visualizations and reports of Spark data.

1. Open MicroStrategy Desktop and create a new dossier.
2. In the datasets panel, click New Data, select Databases, and choose Select a Table as the Import Option.
3. Add a new data source and choose the DSN-less data sources option.
4. Choose Generic in the Database menu and Generic DBMS in the Version menu.
5. Click the link to show the connection string and opt to edit the connection string. In the Driver menu, select MicroStrategy Cassandra ODBC Driver. (MicroStrategy requires a certified driver to interface through JDBC; the actual driver will not be used.)
6. Set the connection string as follows and click OK:
   • Add the JDBC keyword to the connection string.
   • Set MSTR_JDBC_JAR_FOLDER to the path of the JAR file for the JDBC driver. (C:\Program Files\CData JDBC Driver for Spark\lib\ on Windows.)
   • Set DRIVER to cdata.jdbc.sparksql.SparkSQLDriver, the driver class.
   • Set URL to the JDBC URL for the Spark driver, setting the necessary connection properties.

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

     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.

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

     When you configure the JDBC URL, you may also want to set the Max Rows connection property. This will limit the number of rows returned, which is especially helpful for improving performance when designing reports and visualizations.

   A typical connection string follows:

   JDBC;MSTR_JDBC_JAR_FOLDER=PATH\TO\JAR\;DRIVER=cdata.jdbc.sparksql.SparkSQLDriver;URL={jdbc:sparksql:Server=127.0.0.1;};
   

   👁 Creating a new data source for Spark.
   
7. Right-click on the new data source, and choose Edit catalog options.
8. Edit the SQL Statement to SELECT * FROM SYS_SCHEMAS to read the metadata from the JDBC Driver.
9. Select the new data source to view the available tables. You may need to manually click the search icon in the Available Tables section to see the tables.
10. Drag tables into the pane to import them. 👁 Select tables to import.
    Note: Since we create a live connection, we can import whole tables and utilize the filtering and aggregation features native to the MicroStrategy products.
11. Click Finish and choose the option to connect Live. Live connections are possible and effective, thanks to the high-performance data processing native to CData JDBC Drivers.
12. Choose a visualization, choose fields to display, and apply any filters to create a new visualization of Spark data. Data types are discovered automatically through dynamic metadata discovery. Where possible, the complex queries generated by the filters and aggregations will be pushed down to Spark, while any unsupported operations (which can include SQL functions and JOIN operations) will be managed client-side by the CData SQL engine embedded in the driver. 👁 Visualize Spark data.
    
13. Once you are finished configuring the dossier, click File -> Save.

Using the CData JDBC Driver for Spark in MicroStrategy Desktop, you can easily create robust visualizations and reports on Spark data. Read our other articles for connecting to Spark data in MicroStrategy Developer and connecting to Spark data in MicroStrategy Web for more examples.