AWS Data Platform Modernisation Guide

Migrating a legacy data platform to AWS is one of the most impactful modernisation decisions an enterprise can make. But it is also one of the most commonly mishandled — leading to cost overruns, architectural debt, and platforms that are no better than what they replaced.

When to modernise

Consider AWS modernisation when you are experiencing:

• On-premises infrastructure that is expensive to maintain and scale
• Legacy ETL tools (Informatica, SSIS, Talend) that are brittle and slow to modify
• Reporting latency measured in days rather than minutes
• Data quality issues with no automated detection
• Inability to support new analytics or AI workloads on current architecture

Key architecture decisions

1. Storage layer — S3 as the foundational data lake with appropriate partitioning, formats (Parquet/Iceberg), and lifecycle policies
2. Catalogue and governance — AWS Lake Formation or Glue Data Catalog for centralised metadata, access control, and lineage
3. Processing — Glue for batch ETL, Step Functions for orchestration, Lambda for event-driven processing, EMR/Spark for heavy transformations
4. Query and analytics — Athena for ad-hoc queries, Redshift for high-performance analytics, QuickSight or third-party BI tools for visualisation
5. Real-time — Kinesis or MSK (Kafka) for streaming ingestion, Flink for stream processing
6. Infrastructure as Code — Terraform or CDK for reproducible, version-controlled infrastructure

Common mistakes

• Lift and shift — moving legacy patterns to the cloud without rearchitecting. You end up paying cloud prices for on-premises architecture.
• Over-engineering — building for scale you don't need yet. Start simple, evolve as requirements emerge.
• Ignoring governance — the data lake becomes a data swamp without cataloguing, quality checks, and ownership.
• No cost controls — AWS spend grows unchecked without budgets, alerts, and resource tagging.

Our approach

We start every AWS modernisation with a technical assessment that maps current state, identifies quick wins, and designs a target architecture that balances simplicity, cost, and future scalability. The output is a phased delivery plan — not a 100-page document that sits on a shelf.