Key points

• Athena reads cataloged data in Amazon S3 without first loading it into a warehouse.
• Use Athena SQL for declarative analysis and Athena Spark notebooks for interactive distributed exploration.

Best-known use cases

• Run ad hoc SQL queries over data stored in Amazon S3.
• Investigate application and access logs without loading them into a warehouse.
• Query governed tables registered in the AWS Glue Data Catalog.

What candidates often confuse it with

• Athena is the serverless S3 query surface; Redshift is the warehouse-local option for repeated optimized processing.

Key takeaway

Choose Athena for ad hoc analysis of governed S3 data when a provisioned warehouse is unnecessary.

Relevant exam tasks

• D2.1 — Task 2.1: Choose a data store
• 2.1.1 — Implement the appropriate storage services for specific cost and performance requirements (for example, Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, Amazon DynamoDB, Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka [Amazon MSK]).
• D3.1 — Task 3.1: Automate data processing by using AWS services
• 3.1.7 — Query data (for example, Amazon Athena).
• D3.2 — Task 3.2: Analyze data by using AWS services
• 3.2.3 — Use SQL in Amazon Redshift and Athena to query data or to create views.
• 3.2.4 — Use Athena notebooks that use Apache Spark to explore data.
• D3.3 — Task 3.3: Maintain and monitor data pipelines
• 3.3.8 — Analyze logs with AWS services (for example, Athena, Amazon EMR, Amazon OpenSearch Service, CloudWatch Logs Insights, big data application logs).
• D4.4 — Task 4.4: Prepare logs for audit
• 4.4.4 — Analyze logs by using AWS services (for example, Athena, CloudWatch Logs Insights, Amazon OpenSearch Service).

Learn more

• DEA-C01 in-scope services
• DEA-C01 Domain 2: Data Store Management
• DEA-C01 Domain 3: Data Operations and Support
• DEA-C01 Domain 4: Data Security and Governance