Master ChatGPT ETL scripts, SSIS packages, dimensional modeling & Streamlit BI dashboards for enterprise analytics
Build a complete, real-world data engineering and analytics solution from scratch using SQL Server 2025, SSIS, Python, ChatGPT, and Streamlit — even if you’re starting as a beginner.
What you’ll learn
- Build a data driven data warehouse.
- Setup SQL Server and Python Environment.
- Restore SQL Server databases from backup files and validate that the restore completed correctly..
- Create, activate, and manage Python virtual environments to isolate dependencies per project..
- Use ChatGPT to generate ETL scripts and data engineering logic, accelerating development while maintaining control..
- Build a staging table and create a working SSIS package to load data into SQL Server as part of an ETL pipeline..
- Design a dimensional data warehouse model (star schema) and build a Python Streamlit analytics dashboard using SQL KPI queries.
Course Content
- SQL Server Environment Setup –> 10 lectures • 34min.
- Python Environment Setup –> 6 lectures • 29min.
- AI ETL Pipeline with ChatGPT –> 4 lectures • 24min.
- Dimensional Modeling Fundamentals Dimensional Modelling Concepts –> 3 lectures • 27min.
- Analytics & BI Dashboard with Python –> 13 lectures • 54min.
Requirements
Build a complete, real-world data engineering and analytics solution from scratch using SQL Server 2025, SSIS, Python, ChatGPT, and Streamlit — even if you’re starting as a beginner.
In this course, you won’t just learn isolated tools. You will build an end-to-end pipeline the same way modern companies build reporting systems: from environment setup, to ETL, to dimensional modelling, to a working analytics dashboard.
We start by setting up your SQL Server environment properly. You’ll learn how to prepare Windows for installation, understand SQL Server 2025 requirements, install SQL Server, verify your setup, install SSMS, connect successfully, explore database types, and restore databases. This ensures your foundation is solid before you build anything.
Next, you will set up a clean Python development environment on Windows. You’ll install Python, learn what virtual environments are (and why they matter in real projects), create and activate a venv, update pip, install Visual Studio Code, and install the key Python libraries used in data analytics and dashboards.
Then we take it to the next level with an AI-assisted ETL workflow. You’ll create a ChatGPT account and learn how to use AI to generate ETL scripts, speed up development, and reduce errors — while still understanding exactly what the code is doing. You’ll build a staging table and create a working SSIS package to load data into SQL Server.
After that, we cover Dimensional Modelling Fundamentals, including star schema concepts, fact tables, dimension tables, and best practices. You’ll also learn how AI can support dimensional modelling design decisions.
Finally, you’ll build a complete Analytics & BI Dashboard with Python. You’ll write real SQL queries for KPI metrics, engagement levels, channel performance, top customers, and activity trends by day of week. Then you’ll develop a multi-part Streamlit dashboard and run it locally as a working BI application.
By the end of this course, you’ll have a practical portfolio project that demonstrates SQL Server setup, SSIS ETL, data warehouse modelling, AI-assisted development, and Python dashboard delivery.