Key points

• Aurora provides MySQL- or PostgreSQL-compatible relational processing with managed availability and scaling features.
• For PostgreSQL-compatible vector search, distinguish producing embeddings from organizing them in a vector index.

Best-known use cases

• Run MySQL- or PostgreSQL-compatible transactional workloads with managed scaling and availability.
• Store embeddings alongside relational data with PostgreSQL-compatible vector search.

What candidates often confuse it with

• Aurora is a transactional relational service; Redshift is an analytical warehouse and DynamoDB is a nonrelational key store.

Key takeaway

Choose Aurora when relational compatibility is required and its managed scaling or PostgreSQL vector-index capability fits.

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]).
• 2.1.2 — Configure the appropriate storage services for specific access patterns and requirements (for example, Amazon Redshift, Amazon EMR, Lake Formation, Amazon RDS, DynamoDB).
• 2.1.3 — Apply storage services to appropriate use cases (for example, using indexing algorithms like Hierarchical Navigable Small Worlds [HNSW] with Amazon Aurora PostgreSQL and using Amazon MemoryDB for fast key/value pair access).
• 2.1.8 — Describe vector index types (for example, HNSW, IVF).

Learn more

• DEA-C01 in-scope services
• DEA-C01 Domain 2: Data Store Management