Streaming & Specialized Data
Explore purpose-built AWS services for streaming and specialized data solutions. Understand how to design continuous, stateful stream processing with Apache Flink, integrate third-party data using AWS Data Exchange, and build cost-efficient time-series analytics pipelines with Amazon Timestream. This lesson guides you through selecting the right tools for latency-sensitive workloads and resilient, scalable data architectures.
Modern enterprise systems increasingly demand sub-second decision-making from continuous data flows rather than periodic batch processing. Fraud detection engines must evaluate transactions as they occur, IoT platforms must correlate sensor readings across millions of devices in real time, and operational dashboards must reflect fleet positions within seconds of GPS emission. Traditional batch ETL pipelines that run on hourly or daily schedules cannot satisfy these latency requirements. AWS addresses these architectural demands through purpose-built services that each solve a distinct layer of the streaming data problem. This lesson examines three services that are often tested as alternatives to general-purpose defaults: Amazon Managed Service for Apache Flink for stateful stream processing, AWS Data Exchange for governed third-party data acquisition, and Amazon Timestream for native time-series analytics. Architects get the best outcomes when they choose purpose-built services that match workload characteristics, rather than forcing familiar tools into unfamiliar patterns.
The following reference architecture illustrates how these services compose a unified streaming pipeline.
Processing streams with Apache Flink
Flink enables real-time, stateful stream processing by maintaining durable application state and processing events continuously, without relying on per-invocation execution boundaries.
Why Flink over Lambda for stream processing
Amazon Managed Service for Apache Flink is a strong fit for continuous, stateful stream-processing workloads in which applications must maintain context across large volumes of incoming events.
The key architectural distinction vs. AWS Lambda is state management. Lambda executes stateless, short-lived functions in which each invocation is independent. That forces external state storage when workloads require aggregation, session tracking, or time-window computations. This adds latency and complexity and typically results in at-least-once processing guarantees.
Exactly-once processing semantics refer to the guarantee that each event is processed a single time, without duplication or loss, even in the presence of failures.
Amazon Managed Service for Apache Flink achieves this by maintaining internal application state and periodically checkpointing it to durable storage such as Amazon S3. If a failure occurs, Flink restores the application from the most recent checkpoint and replays events from the source streams retention window, ensuring consistent recovery and end-to-end exactly-once processing semantics for stateful streaming workloads.