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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 HubDB data. This article explains how to host the CData JDBC Driver in Azure, as well as connect to and process live HubDB data in Databricks.
With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live HubDB data. When you issue complex SQL queries to HubDB, the driver pushes supported SQL operations, like filters and aggregations, directly to HubDB 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 HubDB data using native data types.
To work with live HubDB data in Databricks, install the driver through Azure Data Lake Storage (ADLS). (Please note that the method of connecting through DBFS, which previous versions of this article described, has been deprecated, but has not published an end-of-life.)
https://databrickslibraries.blob.core.windows.net/jdbcjars/cdata.jdbc.salesforce.jarπ Get JAR URL
abfss://[email protected]/cdata.jdbc.salesforce.jarπ Install ADLS Library
With the JAR file installed, we are ready to work with live HubDB 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).
π Attaching to an existing compute resourceConnect to HubDB 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 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.hubdb.HubDBDriver" url = "jdbc:hubdb:RTK=5246...;AuthScheme=OAuth;OAuthClientID=MyOAuthClientID;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;"
For assistance in constructing the JDBC URL, use the connection string designer built into the HubDB JDBC Driver. Either double-click the JAR file or execute the JAR file from the command-line.
java -jar cdata.jdbc.hubdb.jar
Fill in the connection properties and copy the connection string to the clipboard.
There are two authentication methods available for connecting to HubDB data source: OAuth Authentication with a public HubSpot application and authentication with a Private application token.
AuthScheme must be set to "OAuth" in all OAuth flows. Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).
Follow the steps below to register an application and obtain the OAuth client credentials:
Under Scopes, select any scopes you need for your application's intended functionality.
A minimum of the following scopes is required to access tables:
To connect using a HubSpot private application token, set the AuthScheme property to "PrivateApp."
You can generate a private application token by following the steps below:
To connect, set PrivateAppToken to the private application token you retrieved.
π Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)Once the connection is configured, you can load HubDB 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 HubDB data by calling the display function.
display (remote_table.select ("PartitionKey"))
π Displaying HubDB DataIf 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 HubDB data for analysis.
result = spark.sql("SELECT PartitionKey, Name FROM SAMPLE_VIEW WHERE Id = '1'")
The data from HubDB 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" )π Displaying HubDB Data
Download a free, 30-day trial of the CData JDBC Driver for HubDB and start working with your live HubDB data in Azure Databricks. Reach out to our Support Team if you have any questions.
Download a free trial of the HubDB Driver to get started:
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