🎯 Solution Selection Guide

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Decision Framework

Choose the right data architecture based on your business requirements, team maturity, and technical constraints.

Quick Decision Tree

Start Here: What's Your Primary Goal?

📊 Business Intelligence & Reporting

→ Modern Data Warehouse

  • Predictable performance
  • Strong governance
  • SQL-first approach
🤖 Machine Learning & Analytics

→ Data Lake or Lakehouse

  • Schema flexibility
  • Cost-effective storage
  • Support for all data types
⚡ Real-Time Decision Making

→ Data Fabric + Streaming

  • Low latency access
  • Virtualized integration
  • Real-time processing
🏢 Enterprise Scale & Autonomy

→ Data Mesh

  • Domain ownership
  • Federated governance
  • Organizational scalability

Maturity-Based Recommendations

🌱 Starting Out

Team: 5-15 people
Data: <10TB
Budget: Limited

Recommended Path:
  1. Start with Data Lake
  2. Use cloud-managed services
  3. Focus on Bronze→Silver layers
  4. Simple ETL with visual tools
🚀 Growing

Team: 15-50 people
Data: 10TB-1PB
Budget: Moderate

Recommended Path:
  1. Evolve to Lakehouse
  2. Implement table formats
  3. Add streaming capabilities
  4. Establish data governance
🏢 Enterprise

Team: 50+ people
Data: >1PB
Budget: Substantial

Recommended Path:
  1. Consider Data Mesh
  2. Implement federated governance
  3. Domain-specific platforms
  4. Self-service infrastructure

Industry-Specific Recommendations

Industry Primary Architecture Key Requirements Secondary Options
Financial Services Modern Data Warehouse Compliance, audit trails, performance + Data Fabric for real-time
Healthcare Data Fabric Privacy, interoperability, real-time + Lakehouse for research
Retail/E-commerce Lakehouse ML/AI, personalization, scale + Streaming for real-time
Manufacturing Data Lake IoT data, predictive maintenance + Real-time for monitoring
Technology Data Mesh Scale, innovation, autonomy + Multiple architectures

Migration Strategies

From Legacy Data Warehouse

Phase 1: Hybrid Approach
  • Keep existing warehouse for critical reports
  • Build data lake for new use cases
  • Establish data pipelines
Phase 2: Modernization
  • Migrate to cloud data warehouse
  • Implement lakehouse patterns
  • Add streaming capabilities
Phase 3: Optimization
  • Consolidate architectures
  • Implement advanced governance
  • Consider mesh patterns

Greenfield Implementation

Recommended Approach
  • Start with cloud-native lakehouse
  • Use managed services (Databricks, Snowflake)
  • Implement table formats from day one
  • Build with governance in mind
Technology Stack
  • Storage: S3/ADLS + Delta/Iceberg
  • Compute: Spark + SQL engines
  • Orchestration: Airflow/ADF
  • Governance: Unity Catalog/Purview

Cost Optimization Guide

💰 Low Cost
  • • Data Lake: Object storage
  • • Serverless compute
  • • Pay-per-query model
  • • Minimal always-on resources
💸 Medium Cost
  • • Lakehouse: Managed platforms
  • • Auto-scaling clusters
  • • Reserved capacity
  • • Optimized storage formats
💎 High Cost
  • • Data Warehouse: Always-on
  • • Data Fabric: Virtualization
  • • Premium features
  • • 24/7 availability

Success Metrics

Key Performance Indicators
Technical Metrics
  • Query performance (p95 latency)
  • Data freshness (SLA compliance)
  • System availability (99.9%+)
  • Cost per TB processed
Business Metrics
  • Time to insight (days → hours)
  • Self-service adoption rate
  • Data quality scores
  • Developer productivity
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