In many organizations, capturing and analyzing changes in data over time is crucial. Whether it’s tracking inventory, customer behavior, or sales trends, having historical snapshots can unlock powerful insights. Recently, I worked on a challenge that involved exactly that: storing weekly data from SQL Server across multiple tables and visualizing trends through Power BI. Here’s how I tackled it using Python and SQLite.
The Challenge
Most transactional databases (like SQL Server) only store the current state of the data. But what if you need to:
• Track changes week by week?
• Monitor data growth over time?
• Compare historical vs current values in a dashboard?
The goal was to create a lightweight, automated system that:
• Pulls data weekly from SQL Server,
• Stores each snapshot with minimal overhead,
• Connects easily to Power BI for analysis.
The Tech Stack
• SQL Server: Source of truth.
• Python: ETL (Extract, Transform, Load) scripting.
• SQLite (.db): Lightweight local database to store snapshots.
• Power BI: Dashboard and visualization layer.
