DynamoDB to S3 using Kinesis and Firehose: a step by step guide

Moving data between different AWS services like DynamoDB and S3 can be challenging, especially when dealing with large amounts of data or real-time data streams. In this guide, we’ll walk through how to build a serverless data pipeline to seamlessly move data from DynamoDB tables to S3 buckets using Kinesis Data Streams and Kinesis Data Firehose.

Prerequisites:

• An AWS account
• A DynamoDB table with data
• An S3 bucket to store the data

Step 1: Create a Kinesis Data Stream

1. Open the AWS Management Console and navigate to the Kinesis service.
2. Click “Create data stream” and enter a name for your stream.
3. Choose the number of open shards you need based on the expected data throughput.
4. Leave the rest as defaults and create the stream.

Step 2: Enable DynamoDB Stream

1. Head to the DynamoDB service and open your table.
2. Go to the “Exports and Streams” tab.
3. Click “Enable” for the DynamoDB stream and choose “New and old images”.
4. Leave the view type as the default and enable the stream.

Step 3: Create a Kinesis Data Firehose Delivery Stream

1. Navigate to the Kinesis Data Firehose service.
2. Click “Create delivery stream” and enter a name.
3. For source, choose “Kinesis Data Stream” and select the stream you created earlier.
4. For destination, select “Amazon S3” and configure your S3 bucket details.
5. For buffer conditions, increase the buffer size and buffer interval for better throughput.
6. For data transformation, leave it disabled for now.
7. Review and create the delivery stream.

Step 4: Test the Pipeline

1. Head back to your DynamoDB table and insert/update some data.
2. The changes will trigger the DynamoDB stream to send data to Kinesis.
3. Kinesis Data Firehose will then batch and deliver the data to your S3 bucket.
4. Check your S3 bucket and you should see new data files delivered!

And there you have it – a seamless pipeline to move data from DynamoDB to S3 using Kinesis streams and firehose. You can monitor and control data flows, transform data in-flight, create backups, run analytics and much more.

This pipeline is highly scalable and serves as the backbone for advanced big data pipelines on AWS. Customize it based on your throughput needs, data formats, transformations and more. AWS offers rich monitoring, logging and error handling capabilities to manage these pipelines effectively.