Master Data Warehousing: Complete SQL Server, Oracle, MySQL & PostgreSQL Course

 

🏢 Data Warehousing Course Outline – SQL Server, Oracle, MySQL, PostgreSQL (Beginner to Advanced 2025)

📌 Introduction

Data Warehousing is the backbone of enterprise analytics and business intelligence, enabling organizations to store, consolidate, and analyze large volumes of data from multiple sources. This course roadmap teaches learners to design, develop, and manage modern data warehouses using SQL Server, Oracle, MySQL, and PostgreSQL, including latest 2025 features, data modeling, ETL integration, performance optimization, and BI-ready reporting.

---

📘 Detailed Course Outline

Module 1: Introduction to Data Warehousing

• What is a Data Warehouse (DW) and its benefits

• OLTP vs OLAP systems

• DW architecture: Staging, Data Integration, Data Storage, Presentation Layer

• Key concepts: Fact tables, Dimension tables, Granularity, Measures, Attributes

• Latest trends in data warehousing (cloud, real-time DW, automation)

---

Module 2: Data Warehousing Concepts & Modeling

• Star schema, Snowflake schema, Galaxy schema

• Slowly Changing Dimensions (SCD Types 1, 2, 3)

• Fact tables: transaction, snapshot, and aggregate facts

• Surrogate keys vs natural keys

• Data marts vs enterprise DW

• Best practices for schema design in SQL Server, Oracle, MySQL, PostgreSQL

---

Module 3: Data Extraction & ETL Integration

• Extracting data from multiple sources: relational, flat files, APIs

• Transforming data for DW: cleaning, validation, aggregation

• Loading strategies: full load, incremental load, change data capture

• ETL tools integration: SSIS, Oracle Data Integrator (ODI), Python ETL scripts

• Latest ETL features for SQL Server 2022/2025, Oracle 23c, MySQL 8.1+, PostgreSQL 16+

---

Module 4: Data Warehousing with SQL Server

• SQL Server DW architecture and components

• Creating and managing DW databases

• Partitioning, indexing, and performance optimization

• Using SSIS for ETL integration with DW

• Incremental data loading and ETL automation

• Advanced features in SQL Server 2022/2025: columnstore indexes, in-memory analytics

---

Module 5: Data Warehousing with Oracle

• Oracle DW concepts and architecture

• Oracle Data Integrator (ODI) for ETL

• Materialized views and advanced indexing

• Data partitioning and parallel execution

• Data quality, auditing, and error handling

• Latest Oracle 21c/23c DW features

---

Module 6: Data Warehousing with MySQL & PostgreSQL

• MySQL DW setup and best practices

• Partitioning, indexing, and query optimization

• Using PostgreSQL for large-scale DW

• Foreign data wrappers and cross-database integration

• Advanced features in MySQL 8.1+ and PostgreSQL 16+ for data warehousing

• Automating ETL workflows and incremental loads

---

Module 7: Advanced Data Warehousing Techniques

• Data aggregation and pre-computation for reporting

• Handling historical data and slowly changing dimensions

• Real-time data warehousing basics

• Data warehouse optimization and tuning

• Metadata management and lineage tracking

• Data validation and quality monitoring

---

Module 8: Reporting & Analytics Integration

• Creating BI-ready datasets

• Integration with Power BI, Tableau, and other BI tools

• Using views and materialized views for reporting

• Building dashboards and KPIs from DW

• Data security, access control, and auditing

---

Module 9: Cloud & Modern Data Warehousing

• Cloud-based DW concepts (Azure Synapse, Amazon Redshift, Google BigQuery)

• Hybrid DW architecture: on-premise + cloud

• Data lake integration with DW

• Automation, scheduling, and orchestration in cloud DW

• Latest trends: AI-driven DW insights, real-time analytics

---

Module 10: Best Practices & Conclusion

• Designing scalable and maintainable DW solutions

• Performance tuning: indexing, partitioning, compression

• Documentation, version control, and governance

• Security, compliance, and auditing best practices

• Capstone idea: combining multiple sources into an enterprise-ready DW

• Preparing for real-world BI and analytics projects

---

📌 Conclusion

Mastering Data Warehousing empowers professionals to design, manage, and optimize large-scale databases for analytics and business intelligence. This roadmap covers SQL Server, Oracle, MySQL, PostgreSQL, ETL integration, cloud deployment, reporting, and modern 2025 DW features, preparing learners to handle enterprise-level analytics projects efficiently.