How to connect and process Bitbucket data from Azure Databricks

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 Bitbucket data. This article explains how to host the CData JDBC Driver in Azure, as well as connect to and process live Bitbucket data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Bitbucket 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.)

1. Upload the JDBC JAR file to a blob container of your choice (i.e. "jdbcjars" container of the "databrickslibraries" storage account).
2. Fetch the Account Key from the storage account by expanding "Security + networking" and clicking on "Access Keys". Show and copy whichever of the two keys you wish to use. 👁 Get Access Key
   
3. Get the JDBC JAR file's URL by navigating to Containers, opening the specific container storing the JAR, and selecting the entry for the JDBC JAR file. This should open the file's details, where there should be a convenient button to copy the URL button to clipboard. This value will look similar to the below, though the "blob" component may vary depending on storage account type:

   https://databrickslibraries.blob.core.windows.net/jdbcjars/cdata.jdbc.salesforce.jar

   👁 Get JAR URL
   
4. In the Configuration tab of your Databricks cluster, click on the Edit button and expand "Advanced options". From there, add the following Spark option (derived from the JAR URL's domain name) with your copied Account key as its value and click Confirm: spark.hadoop.fs.azure.account.key.databrickslibraries.blob.core.windows.net 👁 Apply Account Key
   
5. In the Libraries tab of your Databricks cluster, click on "Install new", and select the ADLS option. Specify the ABFSS URL for the driver JAR (also derived from the JAR URL's domain name), and click Install. The ABFSS URL should resemble the below:

   abfss://[email protected]/cdata.jdbc.salesforce.jar

   👁 Install ADLS Library
   

Connect to Bitbucket from Databricks

With the JAR file installed, we are ready to work with live Bitbucket 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).

👁 Attaching to an existing compute resource

Configure the Connection to Bitbucket

Connect to Bitbucket 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.bitbucket.BitbucketDriver"
url = "jdbc:bitbucket:RTK=5246...;Workspace=myworkspaceslug;Schema=Information;InitiateOAuth=GETANDREFRESH;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Bitbucket JDBC Driver. Either double-click the JAR file or execute the JAR file from the command-line.

java -jar cdata.jdbc.bitbucket.jar

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

For most queries, you must set the Workspace. The only exception to this is the Workspaces table, which does not require this property to be set, as querying it provides a list of workspace slugs that can be used to set Workspace. To query this table, you must set Schema to 'Information' and execute the query SELECT * FROM Workspaces>.

Setting Schema to 'Information' displays general information. To connect to Bitbucket, set these parameters:

• Schema: To show general information about a workspace, such as its users, repositories, and projects, set this to Information. Otherwise, set this to the schema of the repository or project you are querying. To get a full set of available schemas, query the sys_schemas table.
• Workspace: Required if you are not querying the Workspaces table. This property is not required for querying the Workspaces table, as that query only returns a list of workspace slugs that can be used to set Workspace.

Authenticating to Bitbucket

Bitbucket supports OAuth authentication only. To enable this authentication from all OAuth flows, you must create a custom OAuth application, and set AuthScheme to OAuth.

Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

Creating a custom OAuth application

From your Bitbucket account:

1. Go to Settings (the gear icon) and select Workspace Settings.
2. In the Apps and Features section, select OAuth Consumers.
3. Click Add Consumer.
4. Enter a name and description for your custom application.
5. Set the callback URL:
   • For desktop applications and headless machines, use http://localhost:33333 or another port number of your choice. The URI you set here becomes the CallbackURL property.
   • For web applications, set the callback URL to a trusted redirect URL. This URL is the web location the user returns to with the token that verifies that your application has been granted access.
6. If you plan to use client credentials to authenticate, you must select This is a private consumer. In the driver, you must set AuthScheme to client.
7. Select which permissions to give your OAuth application. These determine what data you can read and write with it.
8. To save the new custom application, click Save.
9. After the application has been saved, you can select it to view its settings. The application's Key and Secret are displayed. Record these for future use. You will use the Key to set the OAuthClientId and the Secret to set the OAuthClientSecret.

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

Load Bitbucket Data

Once the connection is configured, you can load Bitbucket 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" , "Issues") \
	.load ()

Display Bitbucket Data

Check the loaded Bitbucket data by calling the display function.

display (remote_table.select ("Title"))

👁 Displaying Bitbucket Data

Analyze Bitbucket Data in Azure Databricks

If 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 Bitbucket data for analysis.

result = spark.sql("SELECT Title, ContentRaw FROM SAMPLE_VIEW WHERE Id = '1'")

The data from Bitbucket 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" )

👁 Displaying Bitbucket Data

Download a free, 30-day trial of the CData JDBC Driver for Bitbucket and start working with your live Bitbucket data in Azure Databricks. Reach out to our Support Team if you have any questions.