Building Weekly Historical Insights with SQL Server, Python, SQLite, and Power BI

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.

Apr 7, 2025 - 17:34

Building Weekly Historical Insights with SQL Server, Python, SQLite, and Power BI

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.