Amazon DynamoDB, a serverless NoSQL database, has been a go-to solution for over one million customers to build low-latency and high-scale applications. As data grows, organizations are constantly seeking ways to extract valuable insights from operational data, which is often stored in DynamoDB. However, to make the most of this data in Amazon DynamoDB for analytics and machine learning (ML) use cases, customers often build custom data pipelines—a time-consuming infrastructure task that adds little unique value to their core business.

Starting today, you can use Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse to run analytics and ML workloads in just a few clicks without consuming your DynamoDB table capacity. Amazon SageMaker Lakehouse unifies all your data across Amazon S3 data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data.

Zero-ETL is a set of integrations that eliminates or minimizes the need to build ETL data pipelines. This zero-ETL integration reduces the complexity of engineering efforts required to build and maintain data pipelines, benefiting users running analytics and ML workloads on operational data in Amazon DynamoDB without impacting production workflows.

Let’s get started
For the following demo, I need to set up zero-ETL integration for my data in Amazon DynamoDB with an Amazon Simple Storage Service data lake managed by Amazon SageMaker Lakehouse. Before setting up the zero-ETL integration, there are prerequisites to complete. If you want to learn more on how to set up, refer to this Amazon DynamoDB documentation page.

With all the prerequisites completed, I can get started with this integration. I navigate to the AWS Glue console and select Zero-ETL integrations under Data Integration and ETL. Then, I choose Create zero-ETL integration.

Here, I have options to select my data source. I choose Amazon DynamoDB and choose Next.

Next, I need to configure the source and target details. In the Source details section, I select my Amazon DynamoDB table. In the Target details section, I specify the S3 bucket that I’ve set up in the AWS Glue Data Catalog.

To set up this integration, I need an IAM role that grants AWS Glue the necessary permissions. For guidance on configuring IAM permissions, visit the Amazon DynamoDB documentation page. Also, if I haven’t configured a resource policy for my AWS Glue Data Catalog, I can select Fix it for me to automatically add the required resource policies.

Here, I have options to configure the output. Under Data partitioning, I can either use DynamoDB table keys for partitioning or specify custom partition keys. After completing the configuration, I choose Next.

Because I select the Fix it for me checkbox, I need to review the required changes and choose Continue before I can proceed to the next step.

On the next page, I have the flexibility to configure data encryption. I can use AWS Key Management Service (AWS KMS) or a custom encryption key. Then, I assign a name to the integration and choose Next.

On the last step, I need to review the configurations. When I’m happy, I choose Next to create the zero-ETL integration.

After the initial data ingestion completes, my zero-ETL integration will be ready for use. The completion time varies depending on the size of my source DynamoDB table.

If I navigate to Tables under Data Catalog in the left navigation panel, I can observe more details including Schema. Under the hood, this zero-ETL integration uses Apache Iceberg to transform related to data format and structure in my DynamoDB data into Amazon S3.

Lastly, I can tell that all my data is available in my S3 bucket. 

This zero-ETL integration significantly reduces the complexity and operational burden of data movement, and I can therefore focus on extracting insights rather than managing pipelines.

Available now
This new zero-ETL capability is available in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Hong Kong, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, Stockholm).

Explore how to streamline your data analytics workflows using Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse. Learn more how to get started on the Amazon DynamoDB documentation page.

Happy building!
— Donnie