What is the difference between batch processing and stream processing in AWS?
💡 Model Answer
Batch processing in AWS processes large volumes of data in discrete chunks at scheduled intervals. Services such as AWS Batch, Amazon EMR, AWS Glue, and Redshift Spectrum are commonly used. Batch jobs are ideal for ETL, data lake ingestion, and periodic reporting where low latency is not critical. Stream processing handles data continuously as it arrives, providing near‑real‑time analytics. AWS Kinesis Data Streams, Kinesis Data Analytics, Amazon MSK (Kafka), and Lambda functions are typical tools. Stream processing is suited for use cases like real‑time dashboards, anomaly detection, and event‑driven architectures. Key differences include latency (batch: minutes to hours vs stream: milliseconds to seconds), data freshness, and the need for stateful vs stateless processing. Choosing between them depends on business requirements for timeliness, throughput, and complexity of transformations.
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