Modern Data Foundation on AWS

A governed lakehouse foundation enabling unified, AI-ready enterprise data consumption.
Design Intent

This design assumes a centralised data platform ownership model where data is treated as a governed enterprise asset rather than a domain-specific by-product. It enforces controlled ingestion, unified metadata discipline, and structured consumption boundaries, with accountability anchored in platform governance rather than individual teams. Consumption is mediated through shared semantic services to prevent fragmentation and duplication. AWS provides the execution context, with governance anchored through AWS IAM Identity Center and AWS Glue Data Catalog to maintain identity discipline and metadata consistency.

Design
Design Walkthrough
  • Separating governance before ingestion ensures that data enters the platform under enforced identity, access, and compliance controls, preventing unmanaged data sprawl and untraceable lineage (AWS IAM Identity Center, AWS Config).
  • Centralising storage and metadata into a lakehouse model establishes a single source of truth, preventing siloed datasets and inconsistent transformations across domains (Amazon S3, AWS Glue Data Catalog, Amazon Redshift).
  • Introducing governed domain boundaries within the lakehouse allows decentralised ownership without losing central oversight, enabling scale while maintaining control (AWS Lake Formation, AWS Glue ETL).
  • Introducing governed domain boundaries within the lakehouse allows decentralised ownership without losing central oversight, enabling scale while maintaining control (AWS Lake Formation, AWS Glue ETL).
  • Abstracting consumption through a semantic layer decouples users from raw data structures, preventing direct uncontrolled access and enabling consistent cross-domain analytics and AI reuse (Amazon OpenSearch Service, Amazon Athena).
  • Embedding continuous monitoring and audit across layers ensures traceability and rapid anomaly detection, preventing silent data issues and governance blind spots (Amazon CloudWatch, AWS CloudTrail).
Operational Outcomes
Enables
  • Consistent and governed data ingestion across enterprise domains
  • A single, traceable source of truth for analytics and AI consumption
  • Controlled data access aligned to enterprise policies
  • Reduced duplication through shared semantic consumption layers
  • Continuous visibility into data platform usage and health
Good fit when
  • Data exists across multiple disconnected systems and domains
  • Governance and lineage visibility are mandatory requirements
  • Analytics and AI initiatives depend on consistent, shared datasets
  • Data duplication and inconsistency are impacting business decisions
  • There is a need to scale data access without losing control
This reference architecture reflects patterns we see when enterprises attempt to standardise platforms while still allowing teams to move at different speeds.

A practical way to understand whether our approach fits your operating reality.

© 2026 Chavan. All rights reserved
© 2026 Chavan. All rights reserved