Data with Soumya
Data Engineering Mentor
Data WarehousingRoadmap
Learn Data Warehousing step-by-step including ETL pipelines, warehouse architecture, medallion architecture, lakehouse concepts, and enterprise analytics systems.
Why Data Warehousing is Important
Data Warehousing is one of the core foundations behind enterprise reporting, analytics, business intelligence, and decision-making systems.
Strong warehousing knowledge helps Data Engineers build scalable ETL systems, optimize analytical queries, organize enterprise data effectively, and support modern analytics platforms.
Structured Data WarehousingLearning Path
Follow this step-by-step roadmap to build strong Data Warehousing and analytics architecture foundations.
Phase 1 — Data Warehousing Fundamentals
Warehouse Basics
- ✓What is a Data Warehouse
- ✓Operational vs analytical systems
- ✓Business intelligence concepts
- ✓Analytical reporting basics
OLTP vs OLAP
- ✓Transactional systems
- ✓Analytical systems
- ✓Query workload differences
- ✓Warehouse optimization basics
Warehouse Architecture Basics
- ✓Source systems
- ✓Staging layer
- ✓Warehouse layer
- ✓Reporting layer
Phase 2 — ETL & ELT Concepts
ETL Fundamentals
- ✓Extract phase
- ✓Transform phase
- ✓Load phase
- ✓Batch processing concepts
ELT Fundamentals
- ✓Modern ELT workflows
- ✓Cloud warehouse transformations
- ✓Data processing layers
- ✓Scalable architectures
Pipeline Design
- ✓Incremental loading
- ✓Full load vs delta load
- ✓Error handling
- ✓Data validation
Phase 3 — Dimensional Modeling & Schemas
Fact & Dimension Tables
- ✓Fact tables
- ✓Dimension tables
- ✓Measures & metrics
- ✓Grain concepts
Schema Design
- ✓Star schema
- ✓Snowflake schema
- ✓Denormalization
- ✓Reporting optimization
SCD Concepts
- ✓SCD Type 1
- ✓SCD Type 2
- ✓History tracking
- ✓Audit columns
Phase 4 — Modern Data Warehousing
Lakehouse Concepts
- ✓What is a Lakehouse
- ✓Warehouse vs Lakehouse
- ✓Unified analytics
- ✓Modern architecture evolution
Medallion Architecture
- ✓Bronze layer
- ✓Silver layer
- ✓Gold layer
- ✓Curated data concepts
Cloud Warehousing
- ✓Snowflake basics
- ✓BigQuery overview
- ✓Redshift concepts
- ✓Synapse warehouse basics
Phase 5 — Data Integration & Processing
Data Integration
- ✓Data ingestion concepts
- ✓Batch pipelines
- ✓Streaming basics
- ✓Data synchronization
Processing Concepts
- ✓Data transformation
- ✓Data enrichment
- ✓Aggregation workflows
- ✓Data quality checks
Performance Optimization
- ✓Partitioning basics
- ✓Compression concepts
- ✓Query optimization
- ✓Storage optimization
Phase 6 — Real-World Warehouse Architecture
Enterprise Architecture
- ✓Enterprise warehouse design
- ✓Department-level marts
- ✓Analytics ecosystems
- ✓Governance basics
Modern Analytics Stack
- ✓dbt workflows
- ✓Databricks integration
- ✓Fabric lakehouse concepts
- ✓Cloud-native architecture
Project Building
- ✓Design warehouse projects
- ✓Build ETL workflows
- ✓Create medallion architecture
- ✓Practice real-world scenarios
How to Practice Effectively
Learning Data Warehousing requires understanding both architecture concepts and practical ETL workflows. Focus on warehouse design and real-world analytics scenarios.
Daily Practice
- • Study warehouse architectures regularly
- • Practice ETL & ELT workflows
- • Understand medallion architecture deeply
- • Explore cloud warehouse concepts
- • Analyze enterprise reporting structures
Build Projects
- • Build sample warehouse pipelines
- • Create Bronze/Silver/Gold workflows
- • Design analytics warehouse models
- • Build reporting-oriented datasets
- • Practice real-world architecture scenarios
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