Data Store Management

Choosing the store, cataloging it, moving it through its lifecycle, and evolving its schema — 26%

• Choosing a Data Store — The decision the exam asks most often. Access pattern before product: S3 and the lake, Redshift and the warehouse, DynamoDB for key-value at scale, RDS and Aurora for relational workloads, and the specialised stores — with the constraints (latency, query shape, cardinality, cost at rest) that eliminate the wrong three. (5 lessons, 70 min)
• Cataloging and Discovery — Data nobody can find is data nobody uses. The Glue Data Catalog as the shared metadata layer, crawlers and classifiers and where they get schema wrong, partition indexes and why a query gets slower as the table grows, Lake Formation as the permissions layer over the catalog, and DataZone for discovery across teams. (4 lessons, 60 min)
• Lifecycle and Tiering — Moving data through its useful life without a script: S3 storage classes and the retrieval bargain each one strikes, lifecycle configuration and the traps in transition rules, Intelligent-Tiering versus explicit policy, Redshift and DynamoDB retention mechanics, and backup and recovery as a design input rather than an afterthought. (4 lessons, 55 min)
• Data Modeling and Schema Evolution — Designing a model that survives contact with change: star and snowflake schemas and when normalising costs more than it saves, DynamoDB single-table design driven by access patterns, the open table formats (Iceberg, Hudi, Delta Lake) and what they actually buy, and evolving a schema without breaking every reader downstream. (5 lessons, 65 min)