End-to-End Sales Forecasting Data Warehouse
An end-to-end analytics solution that cleans raw sales data with Python, stores it in a SQL Server data warehouse, and forecasts future sales with machine learning. It includes automated ETL with SSIS, predictive modelling with evaluation metrics, and interactive Power BI dashboards for actual vs forecasted sales and stock planning.
- Year
- 2024
- Engagement
- Data engineering & analytics
- Focus
- Data Engineering, AI/ML
- Status
- Open source
01 — The problem
Sales history sits in messy operational exports. Answering "how much will we sell next month, and what should we stock" means cleaning, modelling and forecasting — and repeating all three every time new data lands.
02 — Our solution
A full analytics stack rather than a notebook. Python handles cleaning, a dimensional SQL Server warehouse holds the clean history, SSIS automates the load, machine learning produces the forecast, and Power BI puts actual against forecast for the people who make the stocking decisions.
How it works
The pipeline, step by step — from the first input to the final output.
- 01
Cleaning
Python normalises raw sales exports, resolving types, duplicates and missing values.
- 02
Warehouse
Clean data lands in a SQL Server data warehouse modelled for analytical querying.
- 03
Automated ETL
SSIS packages schedule and automate the load so refreshes are not manual.
- 04
Forecasting
Machine learning models predict future sales and are scored with evaluation metrics.
- 05
Dashboards
Power BI presents actual vs forecast alongside stock planning views.
What we built
The outcomes
01
One automated path from raw export to executive dashboard
02
Forecasts evaluated with explicit metrics
03
Stock planning driven by the same pipeline that reports history
Challenges we solved
Data quality upstream
Forecasts inherit every upstream error. Most of the effort sat in cleaning and validation rather than modelling.
Forecasts need context
A predicted number without its error is not decision-ready. Dashboards present forecast against actual so the model stays continuously visible.
Technology stack
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