Process & Analyze Amazon DynamoDB Data in Databricks (AWS)

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Amazon DynamoDB JDBC Driver

Connect Java applications with the DynamoDB real-time NoSQL cloud database service. Use Amazon DynamoDB as the big data backend that powers your Java/J2EE applications.



Host the CData JDBC Driver for Amazon DynamoDB in AWS and use Databricks to perform data engineering and data science on live Amazon DynamoDB 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 Amazon DynamoDB data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Amazon DynamoDB data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live Amazon DynamoDB 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.amazondynamodb.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Amazon DynamoDB\lib).

Access Amazon DynamoDB Data in your Notebook: Python

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

Configure the Connection to Amazon DynamoDB

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

Step 1: Connection Information

driver = "cdata.jdbc.amazondynamodb.AmazonDynamoDBDriver"
url = "jdbc:amazondynamodb:Access Key=xxx;Secret Key=xxx;Domain=amazonaws.com;Region=OREGON;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.amazondynamodb.jar

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

The connection to Amazon DynamoDB is made using your AccessKey, SecretKey, and optionally your Domain and Region. Your AccessKey and SecretKey can be obtained on the security credentials page for your Amazon Web Services account. Your Region will be displayed in the upper left-hand corner when you are logged into DynamoDB.

Load Amazon DynamoDB Data

Once you configure the connection, you can load Amazon DynamoDB 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" , "Lead") \
	.load ()

Display Amazon DynamoDB Data

Check the loaded Amazon DynamoDB data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Industry"))

Analyze Amazon DynamoDB 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 Amazon DynamoDB data for reporting, visualization, and analysis.

% sql

SELECT Industry, Revenue FROM SAMPLE_VIEW ORDER BY Revenue DESC LIMIT 5

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