Leveraging AWS Lambda for Big Data Processing
Instruction: Describe how AWS Lambda can be used in big data processing, including integration with other AWS services.
Updated
Official answer
Use Lambda for bounded event-driven processing or orchestration within a larger data platform, rather than treating one invocation as an unlimited distributed analytics engine.
For example, an S3 upload can trigger a function that validates metadata and starts a Glue job. Record the input object version, job identity and processing status durably. Starting the job is not the same as completing it; observe completion and reconcile output counts before publishing results. Step Functions can coordinate supported job integrations and failure paths without keeping a Lambda invocation waiting for the entire job.
For streaming, a Kinesis event source mapping polls records and invokes the handler in batches. Partitioning, batch settings, concurrency quotas and sink capacity bound throughput. Handle duplicates and poison records, monitor IteratorAge, and preserve replayable input. A successful handler return must reflect the required output commitment, not merely an attempted write.
Read large objects in bounded chunks where the format permits it, and budget memory, temporary storage and execution time. The default Lambda Event Function execution model has a 15-minute maximum; other Lambda compute modes have different limits. Use Glue, EMR, Batch or suitable containers for workloads that require longer execution, distributed joins or sustained heavy computation.
Measure end-to-end freshness, completeness, replay behavior and cost per processed dataset, including storage, transfer and downstream services. An event trigger alone does not establish real-time delivery or exactly-once business processing.