What This Guide Covers
A cloud-provider-independent blueprint for designing, building, and operating an enterprise-scale Lakehouse Data Platform — covering 17 interconnected architecture domains from network security zones to AI agents, with a full three-phase migration strategy from on-premise to final cloud. Every design decision is evaluated against seven pillars: Security, Reliability, Performance, Cost Efficiency, Operational Excellence, Scalability, and Governance.
The guide closes with a dependency-sequenced, five-phase 24–28 week implementation roadmap, a complete headcount matrix (85–150 roles across 17 domains), and a risk register — everything needed to take an enterprise data platform from concept to production.
The 17 Architecture Domains
Medallion Architecture — Bronze, Silver and Gold Layers
The medallion architecture is the organisational backbone of the lakehouse. Bronze is the raw ingestion layer — immutable, append-only, preserving data exactly as received from every source system. This is the audit baseline that can regenerate any downstream layer if transformations need correction. Silver applies standardisation, deduplication, null handling, type casting, and conformance rules to produce reliable, schema-consistent datasets suitable for cross-domain analytics. Gold applies business logic, aggregations, and domain-specific models to produce datasets optimised for specific consumers — BI dashboards, ML feature stores, API endpoints, and operational reporting.
Open table format selection — Apache Iceberg, Delta Lake, or Apache Hudi — is evaluated against five criteria: cloud provider native support, streaming write performance, schema evolution capabilities, time travel requirements, and ecosystem integration depth. The guide provides a decision matrix for all three formats against these criteria.
Zero Trust Security Architecture — Four-Zone Network Design
The four-zone network topology implements defence-in-depth from internet edge to data storage: Zone 1 (Public) hosts WAF, DDoS protection, and load balancers; Zone 2 (DMZ) hosts API gateways and reverse proxies; Zone 3 (Application) hosts data processing workloads with no direct internet access; Zone 4 (Data) hosts storage, databases, and secret management with no outbound internet routes. All inter-zone traffic traverses explicit security controls — no lateral movement is possible between zones without passing through an inspection layer.
AI Agents Across the Platform
Six autonomous AI agents are deployed across the platform with defined guardrails, escalation rules, and approval gates: Pipeline Repair Agent monitors health metrics, diagnoses failures using log analysis, and applies automated fixes for known failure patterns. Quality Triage Agent routes data quality alerts to appropriate resolution paths based on severity and downstream impact. Cost Optimisation Agent analyses cloud spend, identifies waste, and implements reductions within pre-approved bounds. Catalog Enrichment Agent automatically generates metadata, tags, and quality assessments for new data assets. Security Anomaly Agent monitors access patterns for anomalous behaviour. Compliance Agent monitors GDPR and ISO 27001 obligations and generates audit evidence automatically.
Topics Covered in This Guide
- Network & Security — four-zone topology, defence-in-depth (6 layers), mTLS, DDoS, IDS/IPS, SOC integration
- IAM & Governance — Zero-trust IAM, RBAC/ABAC policy-as-code, PAM/JIT, federated identity, GDPR compliance engine
- Medallion Architecture — Bronze/Silver/Gold layers, Iceberg vs Delta vs Hudi, schema evolution, data contracts
- Data Integration & Quality — Batch/CDC/streaming ingestion, 6-dimension DQ framework, AI anomaly detection, lineage
- ML/AI & Streaming — Feature store, model lifecycle, autonomous AI agents, LLMOps/RAG, exactly-once streaming
- Operations & Roadmap — FinOps, SRE/SLO framework, tiered DR, GitOps CI/CD, 28-week implementation roadmap
Frequently Asked Questions
Brief Summary
A cloud-provider-independent blueprint for designing, building, and operating an enterprise-scale Lakehouse Data Platform — covering 17 interconnected architecture domains from network security zones to AI agents, with a full three-phase migration strategy from on-premise to final cloud.
Every design decision is evaluated against seven pillars — Security, Reliability, Performance, Cost Efficiency, Operational Excellence, Scalability, and Governance — with explicit trade-offs documented throughout.
A production-grade GDPR compliance engine covers the Data Subject Registry, Right-to-Erasure within 72 hours, crypto-shredding for the immutable Bronze layer, and query-time consent enforcement.
The guide closes with a dependency-sequenced, five-phase 24–28 week implementation roadmap, a complete headcount matrix (85–150 roles across 17 domains), and a risk register.
Extended Summary
What if a single platform could unify petabyte-scale data from 200+ heterogeneous sources — on-premise databases, SaaS feeds, IoT streams, images, video, audio, encrypted payloads — and serve every tier of the organisation simultaneously, from the CEO consuming executive dashboards to AI agents autonomously executing data operations, all under one governed, GDPR-compliant umbrella?
This guide is the definitive cloud-provider-independent reference for 17 interconnected architecture domains: network security zones with four-zone defence-in-depth, zero-trust IAM with RBAC/ABAC policy-as-code, federated data governance, the full medallion lakehouse stack (Bronze/Silver/Gold), real-time streaming with exactly-once semantics, ML/AI platform with feature stores and autonomous agents, BI semantic layer, FinOps cost management, and SRE operations — each domain with component descriptions, design choices, team requirements, and cross-domain dependencies.
Follow the complete three-phase migration: from legacy on-premise infrastructure through an initial cloud platform to a final target cloud, with dual-write coexistence patterns, continuous data reconciliation, and rollback playbooks that minimise risk at every cutover boundary.
The GDPR compliance engine goes far beyond policy documentation: a Data Subject Registry maps every natural person to all their records across Bronze, Silver, and Gold; a Right-to-Erasure pipeline completes crypto-shredding of the immutable Bronze layer within 72 hours; and query-time consent enforcement blocks unauthorised processing without breaking the application layer.
Autonomous AI agents are deployed across every domain — Pipeline Repair Agent, Quality Triage Agent, Cost Optimisation Agent, Catalog Enrichment Agent, Security Anomaly Agent, and Compliance Agent — each with defined guardrails, escalation rules, and approval gates. A dependency-sequenced five-phase roadmap (24–28 weeks, 85–150 people) maps every domain to a delivery phase with team size, duration, and inter-domain dependency.
