Learn Data Warehousing

1. Introduction to Data Warehousing

• Definition and Overview
• Importance and Benefits
• Data Warehouse vs. Database

2. Data Warehouse Basics

Key Concepts

• ETL (Extract, Transform, Load)
• Dimensional Modeling
• Fact Tables
• Dimension Tables
• OLAP (Online Analytical Processing)
• Data Mart
• Data Warehouse Schema
• Star Schema
• Snowflake Schema
• Components of a Data Warehouse
• Types of Data Warehouses (Enterprise DW, Operational Data Stores, Data Marts)

3. Data Warehouse Architecture

• Single-tier, Two-tier, and Three-tier Architecture
• Lambda and Kappa Architectures
• Data Warehouse and Data Lake Integration

4. Data Warehouse Infrastructure

• Hardware Considerations
• Software Components
• Network Requirements
• Security and Compliance

5. Data Modeling

• Conceptual, Logical, and Physical Data Models
• ER Diagrams and Star Schema
• Snowflake Schema and Galaxy Schema
• Data Vault Modeling

6. Setting up an ETL Process

• ETL Process Overview
• Data Extraction Techniques
• Data Transformation Logic
• Data Loading Strategies

7. Dimensional Modeling: Facts & Dimensions

• Dimensional Modeling Concepts
• Designing Fact Tables
• Designing Dimension Tables
• Hierarchies and SCDs

8. Implementing a Complete Data Warehouse Hands-on

• Project Planning and Requirements Analysis
• Data Warehouse Design and Implementation
• Testing and Deployment
• Maintenance and Monitoring

9. Slowly Changing Dimensions (SCDs)

• Types of SCDs (Type 1, Type 2, Type 3)
• Implementing SCDs in ETL Process
• Best Practices for Managing SCDs

10. Understanding ETL Tools

• Overview of Popular ETL Tools (Informatica, Talend, SSIS)
• Open Source vs. Commercial ETL Tools
• Evaluating and Selecting ETL Tools
• ETL Tool Implementation Case Studies

11. ELT vs. ETL

• Differences and Use Cases
• Benefits and Limitations
• Choosing Between ELT and ETL

12. Advanced Topics

• Columnar Storage and Its Benefits
• OLAP Cubes: Design and Implementation
• In-memory Databases: Concepts and Use Cases
• Massive Parallel Processing (MPP) Architectures
• Cloud Data Warehouses (AWS Redshift, Google BigQuery, Snowflake)

13. Optimizing a Data Warehouse

• Using Indexes (B-tree indexes & Bitmap indexes)
• Query Performance Tuning
• Data Partitioning Strategies
• Caching and In-memory Processing

14. Practically Using and Connecting to a Data Warehouse

• Data Warehouse Access Tools (SQL, BI Tools)
• Integrating Data Warehouses with Applications
• Data Warehousing Services and APIs
• Security Best Practices

15. Future Trends and Innovations in Data Warehousing

• Big Data and Data Warehousing
• Machine Learning and AI in Data Warehousing
• Real-time Data Warehousing
• Ethical Considerations and Data Governance

• Common Pitfalls and How to Avoid Them