How do you determine the best AWS service to use for a specific use case pattern, such as batch versus event‑driven processing?

Choosing the right AWS service starts with a clear understanding of the workload’s characteristics: latency, throughput, data volume, and integration needs. For batch workloads that can tolerate higher latency and require large‑scale data transformation, services like AWS Batch, Amazon EMR, or AWS Glue are ideal because they provide managed cluster provisioning, job scheduling, and cost control. If the workload is event‑driven, low‑latency, and scales with the number of events, serverless options such as AWS Lambda, Amazon EventBridge, or Amazon Kinesis Data Streams are preferable. Lambda is great for short, stateless functions triggered by events, while EventBridge handles complex event routing across services. Kinesis is suited for high‑throughput streaming data that needs real‑time processing. When deciding, consider factors such as: 1) the size of the data set and compute time; 2) the required response time; 3) the need for stateful processing; 4) cost implications of compute and storage; and 5) how the service integrates with other AWS components. A simple decision matrix that scores each service against these criteria can help you pick the most appropriate one for the specific pattern.