⚖️ Architecture Comparison

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Key Differentiators

Architecture Data Storage Schema Approach Governance Model Primary Use Case Complexity
Data Lake Object Store (S3, ADLS) Schema-on-Read Centralized Catalog Raw Data Archive & ML Low
Data Warehouse Relational/Columnar Schema-on-Write Centralized DBA BI & Reporting Medium
Data Lakehouse Object + Table Formats Schema Evolution Unified Catalog Unified Analytics Medium
Data Fabric Virtualized Views Runtime Schema Policy-Driven Real-time Integration High
Data Mesh Domain-Distributed Contract-Based Federated Domain Autonomy Very High

Performance & Cost Analysis

Architecture Query Performance Storage Cost Compute Cost Time to Value Scalability
Data Lake Variable Very Low Pay-per-use Fast Unlimited
Data Warehouse Optimized High Always-on Slow Vertical
Data Lakehouse Good Low Elastic Medium Horizontal
Data Fabric Latency No Duplication Virtualization Instant Federated
Data Mesh Domain-specific Distributed Per Domain Long-term Infinite

Organizational Fit

Team Size & Maturity

  • Small Teams (5-20): Data Lake → Lakehouse
  • Medium Teams (20-100): Modern Data Warehouse
  • Large Teams (100+): Data Mesh or Fabric
  • High Maturity: Lakehouse, Mesh
  • Low Maturity: Data Lake, Warehouse

Business Requirements

  • Real-time Decisions: Data Fabric
  • Regulatory Compliance: Data Warehouse
  • ML/AI Innovation: Data Lake/Lakehouse
  • Domain Autonomy: Data Mesh
  • Cost Optimization: Data Lake

Evolution Path

Typical Migration Journey

Phase 1: Data Warehouse → Data Lake (cost reduction)
Phase 2: Data Lake → Lakehouse (ACID + performance)
Phase 3: Lakehouse → Mesh (organizational scale)
Overlay: Data Fabric for real-time integration

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