Athena for Analytics
Explore how to use Amazon Athena to analyze log data effectively during incidents. Understand how to write precise queries that count request outcomes, identify error sources, and validate data integrity. This lesson helps you develop reliable techniques to correlate Athena query results with real-world incidents and ensures accurate operational conclusions.
An incident question often starts simple, but "simple" is exactly what makes it dangerous to answer with a hunch. Between 09:00 and 09:15, what changed in request outcomes, and does it match the spike seen in a load balancer 5xx metric or a CloudWatch alarm? Answering that with Amazon Athena requires a minimal, consistent event shape, so an aggregate means what it's supposed to mean rather than an artifact of how the query was written.
The smallest useful log shaped table usually has timestamp, request_path, status_code, latency_ms, and source_ip. Those fields let one query explain three operational signals. Errors come from status_code, latency regressions come from latency_ms, and top talkers usually show up as a skew in source_ip.
Before writing queries, scanning the sample rows and mapping each column to the operational question it can support is worth ... ...