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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 TigerGraph data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live TigerGraph data in Databricks.
With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live TigerGraph data. When you issue complex SQL queries to TigerGraph, the driver pushes supported SQL operations, like filters and aggregations, directly to TigerGraph 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 TigerGraph data using native data types.
To work with live TigerGraph data in Databricks, install the driver on your Databricks cluster.
With the JAR file installed, we are ready to work with live TigerGraph 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 TigerGraph, and create a basic report.
Connect to TigerGraph 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.tigergraph.TigerGraphDriver" url = "jdbc:tigergraph:RTK=5246...;User=MyUserName;Password=MyPassword;URL=MyURL;"
For assistance in constructing the JDBC URL, use the connection string designer built into the TigerGraph JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.tigergraph.jar
Fill in the connection properties and copy the connection string to the clipboard.
To authenticate with your TigerGraph instance, set the User, Password, and URL properties to valid TigerGraph credentials. By default connections are made on port 14240.
๐ Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)Once you configure the connection, you can load TigerGraph 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" , "person") \ .load ()
Check the loaded TigerGraph data by calling the display function.
display (remote_table.select ("id"))
๐ Displaying TigerGraph 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 TigerGraph data for reporting, visualization, and analysis.
% sql SELECT id, locationId FROM SAMPLE_VIEW ORDER BY locationId DESC LIMIT 5๐ Displaying TigerGraph Data
The data from TigerGraph 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 TigerGraph and start working with your live TigerGraph data in Databricks. Reach out to our Support Team if you have any questions.
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