Batch to Streaming with Amazon Kinesis Blueprint
A blueprint to convert batch pipelines to Amazon Kinesis Data Streams using shard/partition-key design, Lambda or Managed Flink processing, and Firehose delivery, validated by parallel run.
Overview
Amazon Kinesis Data Streams is a managed, serverless-friendly streaming service that ingests and processes data in real time without operating brokers. For teams already on AWS, it replaces batch pipelines with low-latency streams while avoiding the operational load of running Kafka. This blueprint converts scheduled batch ingestion into Kinesis streaming.
Phases
Assessment. Identify batch jobs that need freshness and map their sources and sinks. Estimate record rate and size to size shards (or choose on-demand capacity mode). Decide between Provisioned and On-Demand modes based on traffic predictability.
Stream design. Define streams and choose partition keys that distribute records evenly to avoid hot shards. Define record schemas and a contract for consumers. Plan retention (up to 365 days with extended retention).
Pipeline build. Produce records with the Kinesis Producer Library or SDK. Consume with the Kinesis Client Library, AWS Lambda event-source mapping, or Amazon Managed Service for Apache Flink. Land curated data in S3, Redshift, or OpenSearch via Kinesis Data Firehose.
Parallel run. Run streaming next to batch and reconcile to verify completeness and correctness.
Cutover. Move consumers to the stream and retire batch jobs.
Key Risks and Mitigations
- Data consistency: Use idempotent consumers and checkpointing (KCL/Flink); reconcile against batch during parallel run.
- Shard hot-partitioning: Choose high-cardinality partition keys; monitor
IteratorAgeand per-shard throughput, and reshard or use on-demand mode. - Cost overrun: Model shard-hours and PUT payload units; use On-Demand for spiky traffic and Firehose buffering to reduce calls.
Recommended Tooling
Kinesis Data Streams with Producer/Client Libraries; AWS Lambda or Managed Service for Apache Flink for processing; Kinesis Data Firehose for delivery to S3/Redshift/OpenSearch; CloudWatch for IteratorAge and throughput.
Success Metrics
Lower end-to-end latency, sustained throughput within shard limits, and improved data freshness for downstream systems.
Prerequisites
An AWS account with appropriate IAM, sized stream capacity, defined record schemas/contracts, and a reconciliation plan against the existing batch outputs.