Future-proofing your applications with AWS databases
summary
Roadmap of concrete features and numbers: zero-ETL integrations push operational data into Redshift and OpenSearch without custom pipelines, Amazon Q/ Q for SQL and Amazon Bedrock agents enable retrieval-augmented workflows, vector capabilities land in seven engines (Aurora Postgres with PGVector, DynamoDB vector path), Aurora Limitless shards to handle roughly 10 million concurrent writes, Neptune Analytics claims up to 80x faster global graph scans, Elasticache serverless targets 100M TPS per shard (500M TPS per cluster), Graviton3 shows ~25% runtime gain. DMS and Fleet Advisor automate migration steps.
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chapters (7)
- 0:00Introduction and welcome
- 1:27Embracing change in databases
- 6:01Zero ETL and real-time data integration
- 14:11Purpose-built databases and innovations
- 20:56Databases for generative AI applications
- 31:50Vector databases and RAG in practice
- 41:24Future-proofing with AWS databases
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