Overview. This pipeline demonstrates a fully serverless streaming analytics architecture on AWS. High‑volume events flow into Amazon Kinesis Data Streams. An AWS Lambda function batches and transforms records in real time before writing to Amazon S3 for durable storage. Downstream analytics are performed by Amazon Redshift or Athena, and business users explore insights through Amazon QuickSight. Because there are no long‑running servers, the solution automatically scales with traffic and charges only for consumed resources.
Why serverless for streaming? Streaming patterns typically follow the loop of “stream, collect, process, store, and analyze”. Traditional architectures rely on self‑managed Apache Kafka clusters, but AWS Kinesis and Lambda provide a managed alternative. Processing logic runs in small, stateless Lambda functions, eliminating the need to provision or patch servers. S3 acts as a data lake, decoupling compute from storage so different analytics engines (Redshift Spectrum, Athena, EMR) can query the same data set. This approach reduces operational overhead and scales seamlessly when event throughput surges.
Benefits. A serverless streaming pipeline supports low‑latency analytics with minimal maintenance. Combined with Kinesis Data Firehose (not shown), it can deliver records directly into OpenSearch or Redshift. Using Amazon EventBridge or SNS as an alert layer allows you to trigger downstream workflows when anomalies occur, enabling real‑time monitoring and fast business reaction.
