Super UserEDA on AMAZON ANLYSIS
Purpose & Scenario 🚀
This notebook showcases the power of Microsoft Fabric Lakehouse to transform raw e-commerce data into actionable business intelligence.
Using the Amazon Sales Data from Kaggle, we simulate a real-world retail analytics scenario where decision-makers need clear, data-driven insights to stay competitive in a fast-moving marketplace.
Why this matters
E-commerce businesses generate huge volumes of sales, pricing, and customer feedback data every day. The challenge isn’t just storing this data—it’s turning it into insights fast enough to adapt pricing strategies, optimize discounts, and improve product positioning.
Our approach
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Data ingestion: Imported raw CSV into a Fabric Lakehouse table, ensuring it’s ready for scalable analytics.
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Exploratory Data Analysis (EDA): Used PySpark for distributed data processing and Pandas for quick tabular exploration.
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Interactive visual storytelling: Leveraged Plotly to create dynamic, drill-down visualizations that reveal trends, anomalies, and correlations instantly.
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Business-ready metrics: Identified top-performing product categories, mapped discount–rating relationships, and highlighted potential pricing opportunities.
Outcome
This end-to-end workflow demonstrates how Fabric unifies storage, processing, and visualization—reducing friction between raw data and strategic action.
The result is an interactive analytics experience that empowers business teams to act today, not next quarter.
Link of Kaggle Notebook- Amazon Analysis
