Master ETL: Complete Course Outline (SQL Server, Oracle, MySQL, PostgreSQL)

🔄 ETL (Extract, Transform, Load) Course Outline – SQL Server, Oracle, MySQL, PostgreSQL (Beginner to Advanced 2025)

📌 Introduction

ETL (Extract, Transform, Load) is a core process in data warehousing and analytics that moves data from multiple sources into centralized databases or data warehouses. This course equips learners with hands-on skills in SQL Server, Oracle, MySQL, and PostgreSQL, covering data extraction, transformation, loading, optimization, and modern ETL best practices for 2025-ready data solutions.

📘 Detailed Course Outline

Module 1: Introduction to ETL

What is ETL and its role in Data Warehousing
ETL vs ELT: differences and use cases
Overview of ETL tools vs manual ETL with SQL
ETL workflow: Extraction → Transformation → Loading
Key ETL concepts: data sources, staging, data quality, and scheduling

Module 2: Data Sources & Extraction

Connecting to relational databases: SQL Server, Oracle, MySQL, PostgreSQL
Extraction from flat files (CSV, Excel, JSON, XML)
Extraction from APIs and cloud sources (Azure, AWS, Web APIs)
Incremental vs full data extraction
Handling large datasets and batch processing
Latest connector updates for 2025 databases

Module 3: Data Transformation Basics

Cleaning and standardizing data
Handling missing values, duplicates, and nulls
Data type conversions and normalization
String, date, and numeric transformations
Conditional transformations and computed columns
Using SQL functions for ETL transformations

Module 4: Advanced Data Transformation

Aggregation, grouping, and summarization
Joins, unions, and merges across multiple sources
Lookup tables and reference data integration
Slowly Changing Dimensions (SCD Type 1, 2, 3)
Pivoting and unpivoting data
Using stored procedures and scripts for transformation automation

Module 5: Data Loading & Performance Optimization

Loading data into staging, intermediate, and target tables
Bulk inserts and batch loading
Indexing and partitioning for performance
Error handling and logging during ETL loads
Data validation and reconciliation
ETL automation scheduling using SQL Server Agent, Oracle Scheduler, Cron Jobs

Module 6: ETL with SQL Server

SQL Server Integration Services (SSIS) overview
Creating and configuring packages
Data flow and control flow tasks
Handling transformations and lookups in SSIS
Logging, error handling, and package deployment
Integration with SQL Server 2022/2025 features

Module 7: ETL with Oracle

Oracle Data Integrator (ODI) introduction
Connecting to Oracle databases for ETL
Using ETL mappings and transformations
Error handling, auditing, and logging
Advanced ETL features in Oracle 21c and 23c

Module 8: ETL with MySQL & PostgreSQL

Using MySQL Workbench and PostgreSQL tools for ETL
Data extraction and transformation using SQL scripts
Using stored procedures and functions for ETL
Handling incremental loads and replication
Latest features in MySQL 8.1+ and PostgreSQL 16+ for ETL

Module 9: Modern ETL Practices & Automation

Automating ETL workflows using Python, Airflow, or SSIS
Real-time ETL and streaming data
Cloud ETL: Azure Data Factory, AWS Glue
Data validation and monitoring best practices
ETL for analytics, BI, and machine learning pipelines
Security and compliance considerations

Module 10: Best Practices, Optimization & Conclusion

Designing efficient and maintainable ETL pipelines
Data quality checks and reconciliation
Version control and deployment strategies
Performance tuning and optimization tips
Documentation and governance in ETL
Capstone idea: end-to-end ETL workflow combining multiple databases into a centralized data warehouse

📌 Conclusion

Mastering ETL enables professionals to efficiently extract, transform, and load data from diverse sources into central systems. This roadmap covers SQL Server, Oracle, MySQL, PostgreSQL, and modern ETL tools, with automation, performance optimization, and cloud integration, preparing learners for data-driven enterprise projects in 2025.