What's the Difference Between Amazon EC2 and AWS Lambda?

Both are fully managed relational database services on AWS, but Aurora is a cloud-native engine built for higher performance and availability, while RDS gives you familiar open-source and commercial engines with less operational overhead than self-managed.

DynamoDB is a serverless NoSQL database for applications with well-defined access patterns that need consistent single-digit millisecond performance. RDS is a managed relational database for applications that need SQL, joins, and transactional integrity across multiple engines.

Both are fully managed, serverless-capable NoSQL databases on AWS. DynamoDB is AWS-native with its own API and data model; Keyspaces is Apache Cassandra-compatible, letting you use existing Cassandra drivers, tools, and expertise.

Both are fully managed NoSQL databases on AWS, but they serve different data models. DynamoDB is a key-value store optimized for known access patterns at massive scale; DocumentDB is a document database for flexible JSON schemas with MongoDB compatibility.

Both handle semi-structured data, but they're optimized for different query patterns. DocumentDB stores and queries JSON documents; Neptune stores and traverses relationships between entities.

Both are fully managed relational database services on AWS, but Aurora is a cloud-native engine built for higher performance and availability, while RDS gives you familiar open-source and commercial engines with less operational overhead than self-managed.

Both are purpose-built, fully managed AWS databases, but they solve fundamentally different problems. Aurora handles structured relational data with SQL; Neptune handles highly connected data where relationships between entities are the primary query pattern.

Both are fully managed AWS database services, but they serve fundamentally different data models and access patterns. Aurora is a relational database for structured data with relationships; DynamoDB is a NoSQL key-value store built for single-digit millisecond performance at any scale.

Both run SQL queries, but they're optimized for fundamentally different workloads. RDS handles transactional operations (inserts, updates, lookups) for your application. Redshift handles analytical queries (aggregations, scans, joins across billions of rows) for your data team.