Build a complete, hands-on Analytics Engineering project from start to finish using Python, SQL, DuckDB, dbt, Streamlit, Plotly, Git, and GitHub. This course is designed for learners who want to understand how modern analytics pipelines work by actually building one. Rather than focusing only on theory, you will create a practical e-commerce ELT pipeline that takes data from its raw form through transformation and testing to business-ready analytics and an interactive executive dashboard.
You will begin by using Python, pandas, and Faker to generate realistic synthetic datasets containing customers, products, and orders. You will then use DuckDB as a fast, lightweight analytical data warehouse for the project. Next, you will work with dbt — data build tool — to transform your data using a structured Bronze, Silver, and Gold architecture. You will create staging models to clean and standardise the data before building a business-ready fact table containing important calculations such as gross revenue, total cost, and gross profit.
Data quality is an essential part of Analytics Engineering, so you will also implement automated dbt tests to validate important rules including unique values, not-null values, and accepted values. Once the transformation pipeline is complete, you will query the analytical warehouse using Python and calculate executive KPIs including completed orders, gross revenue, gross profit, and profit margin.
