Analytics services overlap at the edges, so classify the job before the product.
| Job | Start with |
|---|---|
| Serverless ad hoc SQL over S3 | Athena |
| Managed analytical warehouse | Redshift |
| ETL and shared metadata catalog | Glue |
| Govern a multi-source data lake | Lake Formation |
| Managed Hadoop/Spark cluster | EMR |
| Search and log analytics | OpenSearch |
| BI dashboards | QuickSight |
| Stateful stream processing | Managed Service for Apache Flink |
| Managed Kafka compatibility | MSK |
Athena cost and speed depend on bytes scanned: columnar Parquet/ORC, compression, partitioning, and selecting only needed columns matter. Redshift is optimized for repeated analytical workloads and can query S3 through Spectrum. Glue crawlers/catalog describe data; Glue jobs transform it. Lake Formation centralizes data-lake permissions across services.
For ingestion, Kinesis Data Streams retains records for custom consumers and replay; Data Firehose buffers and delivers to supported destinations with minimal operations. MSK fits Kafka APIs and ecosystem requirements. OpenSearch is for search, not a system-of-record replacement for DynamoDB; streams can feed an index.
ML service questions are mostly capability mapping: Rekognition images/video, Textract documents, Transcribe speech-to-text, Polly text-to-speech, Comprehend language, Translate translation, Lex conversational interfaces, and SageMaker custom ML workflows.
Exam lens: “serverless and query S3 occasionally” points to Athena; “warehouse with repeated BI joins” points to Redshift. Both can read S3, but the operating model differs.
Review card
- Optimize Athena by reducing bytes scanned.
- Glue catalogs/transforms; Lake Formation governs.
- Redshift is a warehouse; Spectrum extends queries to S3.
- Data Streams retains/replays; Firehose delivers.
- Match managed AI services by input and output modality.