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Build AI-Driven Data Warehouses: ChatGPT ETL, & Python BI
1 students

Build AI-Driven Data Warehouses: ChatGPT ETL, & Python BI

Master ChatGPT ETL scripts, SSIS packages, dimensional modeling & Streamlit BI dashboards for enterprise analytics
Created byBluelime Learning Solutions
Last updated 2/2026
English

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

5 sections • 36 lectures • 2h 48m total length

  • Introduction3:21
  • Pre-Installation Preparation: Installing Windows Updates2:50
  • SQL SERVER 2025 HARDWARE AND SOFTWARE REQUIREMENTS4:26
  • Download SQL SERVER3:13
  • SQL SERVER INSTALLATION6:24
  • Verify SQL Server Installation1:01
  • Installing SSMS2:20
  • Connecting to SQL Server with SSMS2:09
  • Database Types in SQL Server5:21
  • Restore databases3:07

Requirements

  • Basic computer skills (installing software, creating folders, downloading files)
  • Beginner-level understanding of databases is helpful (but not required)
  • Basic familiarity with SQL is recommended (SELECT, WHERE, JOIN), but the course will guide step-by-step
  • No prior SSIS experience is required (you will learn from scratch)
  • No prior Python experience is required, but basic programming familiarity is a plus
  • Internet connection required.

Description

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.


Who this course is for:

  • Beginners in data engineering or analytics
  • SQL learners who want to go beyond basic queries and learn how real-world ETL pipelines and data warehouses are built
  • Data analysts who want to learn dimensional modelling fundamentals and how to turn SQL data into KPI dashboards
  • BI and reporting professionals who want to modernize their skills using Python and Streamlit for interactive analytics
  • Developers and software engineers who want to understand data warehouse architecture and build practical ETL workflows
  • IT professionals and database administrators who want hands-on experience setting up SQL Server 2025, SSMS, and database restore processes
  • Students and career switchers looking for an end-to-end portfolio project that demonstrates SQL, ETL, dimensional modelling, and dashboard development
  • Professionals curious about AI in data engineering who want to learn how ChatGPT can accelerate ETL scripting and warehouse design in a controlled, practical way

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