Part 4: Solving Burst Traffic with Kinesis Data Streams

Introducing Kinesis Data Streams as a Buffer Layer

While the code-level optimizations reduced the volume of data written to DynamoDB, they did not fully address the challenge of sudden traffic spikes caused by user-triggered analytics jobs.

To solve this, we introduced Amazon Kinesis Data Streams as a flow-control layer between the analytics workloads and DynamoDB.

Instead of writing directly to DynamoDB, producers publish events to Kinesis Data Streams. Consumer applications then process records from the stream and persist them to DynamoDB at a controlled rate.

This architecture provided several benefits:

• Absorbed sudden bursts of write traffic generated by complex analytics jobs.

• Protected DynamoDB from short-lived throughput spikes and throttling.

• Reduced dependence on aggressive DynamoDB auto-scaling.

• Improved workload isolation between data producers and consumers.

• Enabled predictable throughput and operational costs.

By combining event aggregation, compression, and Kinesis-based flow control, we transformed the platform from a write-intensive architecture into a highly scalable event-processing pipeline capable of supporting more than 500 jobs per day, including 30% more complex workloads, while maintaining a maximum data availability time of less than five minutes.

Overall we have evolved from a Legacy RDBMS --> DynamoDB --> DynamoDB + Kinesis Solution and here are some metrics captured: