Overview
A modernization path using S3 lakehouse layers, Redshift Serverless, EMR Spark, Managed Flink, QuickSight, Bedrock Claude, and EKS.
- How to phase migration without disrupting business reports.
- Where to place AI orchestration in the platform architecture.
- How security and observability should be designed before production rollout.
Migration
Phase the platform around business continuity
Migration should protect existing reporting workflows while introducing lakehouse governance, modern compute, and AI-ready services in controlled steps.
- Run current BI outputs in parallel with new governed datasets.
- Move high-value domains first instead of attempting a big-bang rebuild.
Architecture
Place AI orchestration beside governed data services
AI workflows should retrieve schemas, metric definitions, historical reports, model outputs, and permission context from the same governed platform path.
- Keep retrieval context and query execution under the same security boundary.
- Use application services to expose AI capabilities instead of raw infrastructure endpoints.
Operations
Design observability before rollout
Production AI data platforms need monitoring for pipelines, query cost, model usage, tenant traffic, output quality, and access control events.
- Instrument data freshness, failed jobs, latency, and tenant-level usage.
- Audit sensitive data access and AI workflow decisions from the beginning.