Process & Analyze Cosmos DB Data in Databricks (AWS)

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

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

Install the CData JDBC Driver in Databricks

To work with live Cosmos DB data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.cosmosdb.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

👁 Loading the JDBC JAR File into AWS

Access Cosmos DB Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Cosmos DB 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 Cosmos DB, and create a basic report.

Configure the Connection to Cosmos DB

Connect to Cosmos DB 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.

Step 1: Connection Information

driver = "cdata.jdbc.cosmosdb.CosmosDBDriver"
url = "jdbc:cosmosdb:RTK=5246...;AccountEndpoint=myAccountEndpoint;AccountKey=myAccountKey;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.cosmosdb.jar

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

To obtain the connection string needed to connect to a Cosmos DB account using the SQL API, log in to the Azure Portal, select Azure Cosmos DB, and select your account. In the Settings section, click Connection String and set the following values:

• AccountEndpoint: The Cosmos DB account URL from the Keys blade of the Cosmos DB account
• AccountKey: In the Azure portal, navigate to the Cosmos DB service and select your Azure Cosmos DB account. From the resource menu, go to the Keys page. Find the PRIMARY KEY value and set AccountKey to this value.

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

Load Cosmos DB Data

Once you configure the connection, you can load Cosmos DB data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Customers") \
	.load ()

Display Cosmos DB Data

Check the loaded Cosmos DB data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("City"))

👁 Displaying Cosmos DB Data

Analyze Cosmos DB Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Cosmos DB data for reporting, visualization, and analysis.

% sql

SELECT City, CompanyName FROM SAMPLE_VIEW ORDER BY CompanyName DESC LIMIT 5

👁 Displaying Cosmos DB Data

The data from Cosmos DB 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 Cosmos DB and start working with your live Cosmos DB data in Databricks. Reach out to our Support Team if you have any questions.