What This Guide Covers
The definitive production-ready AWS reference for designing, building, and operating an enterprise-scale Lakehouse Data Platform — 17 interconnected architecture domains, 70 AWS services, and a 24–28 week implementation roadmap across three parts. Every architectural decision is evaluated against the AWS Well-Architected Framework AND the seven lakehouse principles, with AWS-specific configuration guidance documented throughout.
A production-grade GDPR compliance engine built on Lake Formation, Amazon Macie, and Step Functions covers crypto-shredding of the immutable Bronze S3 layer within 72 hours, a DynamoDB-based Data Subject Registry, and query-time consent enforcement. Five Amazon Bedrock Agents are deployed across all platform domains with Action Groups, Knowledge Bases, Guardrails, and CI/CD promotion gates.
Part 1 — Platform Vision, Network, Security and IAM
Part 1 establishes the security and identity foundation. The VPC four-zone architecture implements hub-and-spoke networking with AWS Transit Gateway connecting spoke VPCs, AWS Network Firewall providing stateful deep packet inspection, and VPC endpoints eliminating internet-routed traffic for all AWS service calls — S3, Glue, Athena, KMS, Secrets Manager, and every other data plane service communicate privately within the VPC fabric.
AWS Lake Formation with LF-Tag ABAC provides the centralised access control layer. LF-Tags like sensitivity=PII or domain=finance are assigned to tables and columns in the Glue Data Catalog; permission grants assign tag-level access to IAM principals. When a new PII column is added to a table, no individual IAM policy update is required — the existing tag-based grant automatically covers the new column, dramatically reducing access control maintenance overhead at scale.
Part 2 — Data Engineering, Quality, Catalogue, BI and ML/AI
The Bronze-to-Gold medallion stack on S3 with Apache Iceberg uses AWS Glue for ETL across all three layers, with Glue DataBrew and Amazon Deequ for automated data quality profiling and constraint validation. AWS Schema Registry enforces schema evolution governance for Kinesis and MSK event streams. Amazon DataZone provides the enterprise data catalogue with business glossary, domain-based data products, and subscription workflow for self-service data discovery.
The ML/AI platform combines Amazon SageMaker Feature Store for centralised feature management with Amazon Bedrock for foundation model access and agent deployment. Five Bedrock Agents operate across the platform — each with defined Action Groups invoking Lambda functions for remediation, Knowledge Bases grounded in CloudWatch and Glue metrics via RAG, Guardrails for output safety filtering, and CI/CD promotion pipelines via Prompt Flow evaluation.
Part 3 — Streaming, APIs, FinOps, SRE and Implementation Roadmap
The streaming architecture combines Amazon Kinesis Data Streams for sub-second ingestion with Amazon MSK for Kafka-compatible high-throughput event streaming, and Amazon Managed Service for Apache Flink for stateful exactly-once processing — writing directly to S3 Iceberg Bronze tables. This unified streaming and batch architecture eliminates Lambda architecture complexity: streaming data is immediately queryable through Athena alongside historical batch data in the same Iceberg tables.
The 70-service master implementation table maps every AWS service to its delivery phase (1–5), configuration approach (CDK / Terraform / Console / CLI), team owner, and headcount — the single reference needed to plan and track the entire programme delivery.
Topics Covered in This Guide
- Network & Security — VPC four-zone topology, Transit Gateway, WAF/Shield Advanced, GuardDuty, KMS/CloudHSM, Security Hub & SOC integration
- IAM & Governance — Identity Center SAML/OIDC federation, Lake Formation LF-Tag RBAC/ABAC, SCP, IRSA, PAM/JIT via Systems Manager, GDPR compliance engine
- Medallion Architecture — S3 Bronze/Silver/Gold on Apache Iceberg, AWS Glue ETL vs EMR, schema evolution, Schema Registry, data contracts
- Data Integration & Quality — DMS CDC, AppFlow, Kinesis, MSK Connect, Glue DataBrew, Deequ, CloudWatch Anomaly Detection, OpenLineage
- ML/AI & Streaming — SageMaker Feature Store, Bedrock Agents with RAG, Managed Flink to Iceberg, Kinesis vs MSK decision guide, LLMOps Guardrails
- Operations & Roadmap — CDK GitOps, CloudWatch SLO/SLI budgets, Resilience Hub DR tiers, FinOps Savings Plans, 70-service master implementation table
Frequently Asked Questions
Brief Summary
The definitive production-ready AWS reference for designing, building, and operating an enterprise-scale Lakehouse Data Platform — 17 interconnected architecture domains, 70 AWS services, and a 24–28 week implementation roadmap across three parts.
Every architectural decision is evaluated against the AWS Well-Architected Framework AND the seven lakehouse principles — with explicit trade-offs and AWS-specific configuration guidance documented throughout.
A production-grade GDPR compliance engine built on Lake Formation, Amazon Macie, and Step Functions covers crypto-shredding of the immutable Bronze S3 layer within 72 hours, a DynamoDB-based Data Subject Registry, and query-time consent enforcement.
The guide closes with a dependency-sequenced five-phase delivery plan, a 70-service master implementation table, a complete 85–150 role staffing matrix, a risk register, and a definitive IaC configuration guide.
Extended Summary
What if your entire enterprise data platform — petabyte-scale raw ingestion, real-time streaming, AI-powered analytics, and autonomous data operations — could run entirely on AWS managed services, with built-in governance, zero-trust security, and a 30% cost reduction versus on-premises infrastructure?
This three-part guide is the definitive AWS-native reference for all 17 architecture domains: VPC four-zone network design with Transit Gateway and Network Firewall, zero-trust IAM with Identity Center federation and Lake Formation LF-Tag ABAC, federated data governance with GDPR compliance engine, the full medallion lakehouse stack (Bronze/Silver/Gold on S3 Iceberg), real-time streaming with Kinesis and MSK, ML/AI platform with SageMaker Feature Store and Bedrock Agents, Amazon QuickSight BI semantic layer, AWS FinOps discipline, and CDK-based SRE operations — every domain with AWS service selection rationale, configuration decision points, design alternatives with trade-offs, team requirements, and cross-domain dependencies.
Five Bedrock Agents are deployed across the platform — Pipeline Repair Agent, Quality Triage Agent, Cost Optimisation Agent, Catalog Enrichment Agent, and GDPR Compliance Agent — each with defined Action Groups, Knowledge Bases with RAG, Guardrails, and human approval gates. LLMOps infrastructure with Bedrock Knowledge Bases, text-to-SQL via Athena, and hallucination-resistant Guardrails rounds out the intelligence layer.
A dependency-sequenced five-phase roadmap (24–28 weeks, 85–150 people) includes the 70-service master table with implementation phase, configuration approach (CDK / Console / Terraform / CLI), team owner, and headcount for every AWS service.
