Overview
A Modern Data Warehouse combines data lake and relational store capabilities with schema-on-write governance. It delivers optimized BI and reporting performance.
Data Organization
- Staging Layer: ETL via SSIS, Informatica
- Raw Zone: Archive in data lake
- Curated Zone: Star/snowflake schemas
- Data Marts: Departmental subsets
- Semantic Layer: Business-friendly BI models
Key Components & Patterns
Components
- ETL Pipelines: Azure Data Factory, Airflow
- SQL Pools: Azure Synapse, Snowflake, Redshift
- Dimensional Modeling: Star/snowflake schemas
- Security: Row-/column-level access controls
- BI Integration: Power BI, Tableau
Patterns
- ETL Orchestration scheduled workflows
- Delta Loads incremental data refresh
- Data Partitioning by date, region
- Slowly Changing Dimensions history tracking
- Semantic Views abstract complexity
Use Cases
- Financial Consolidation: Multi-country ERP → GAAP reporting
- Supply-Chain KPIs: POS, inventory, shipping → executive dashboards
- HR Analytics: Employee data integration for workforce planning
Pros & Cons
Pros
- ✅ Predictable performance for BI
- ✅ Strong governance & compliance support
Cons
- ⚠️ Upfront schema design slows onboarding
- ⚠️ Scaling costs grow with data volume
Day-to-Day Operations
- Maintenance Windows: Off-peak ETL runs
- Performance Tuning: Indexes & partitions
- Workload Management: Reporting vs loading queues
- Lineage Tracking: End-to-end audit trails
- Self-Service BI: Semantic layers for users
- Access Control: Single security boundary