Process & Analyze AWS Management Data in Databricks (AWS)

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AWS Management JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with live AWS Management data!



Host the CData JDBC Driver for AWS Management in AWS and use Databricks to perform data engineering and data science on live AWS Management data.

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 AWS Management data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live AWS Management data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live AWS Management 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.awsdatamanagement.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for AWS Management\lib).

Access AWS Management Data in your Notebook: Python

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

Configure the Connection to AWS Management

Connect to AWS Management by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.awsdatamanagement.AWSDataManagementDriver"
url = "jdbc:awsdatamanagement:AccessKey=myAccessKey;Account=myAccountName;Region=us-east-1;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.awsdatamanagement.jar

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

To authorize AWSDataManagement requests, provide the credentials for an administrator account or for an IAM user with custom permissions:

  1. Set AccessKey to the access key Id.
  2. Set SecretKey to the secret access key.
  3. Set Region to the region where your AWSDataManagement data is hosted.

Note: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

Load AWS Management Data

Once you configure the connection, you can load AWS Management 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" , "NorthwingProducts") \
	.load ()

Display AWS Management Data

Check the loaded AWS Management data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("PartitionKey"))

Analyze AWS Management 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 AWS Management data for reporting, visualization, and analysis.

% sql

SELECT PartitionKey, Name FROM SAMPLE_VIEW ORDER BY Name DESC LIMIT 5

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