Day 14: Building a Real Retail Analytics Pipeline Using Spark Window Functions

Welcome to Day 14 of the Spark Mastery Series.
Today we stop learning concepts and start building a real Spark solution.

This post demonstrates how window functions solve real business problems like:

• Deduplication
• Running totals
• Ranking

📌 Business Requirements
Retail company needs:

• Latest transaction per customer
• Running spend per customer
• Top customers per day

🧠 Solution Design

We use:

• DataFrames
• groupBy for aggregation
• Window functions for analytics
• dense_rank for top-N

🔹 Latest Transaction Logic

Use row_number() partitioned by customer ordered by date DESC.

This pattern is commonly used in:

• SCD2
• CDC pipelines
• Deduplication logic

🔹 Running Total Logic

Use window frame:

rowsBetween(unboundedPreceding, currentRow)

This preserves row-level detail while adding cumulative metrics.

🔹 Top N Customers Per Day

Aggregate daily spend first → apply dense_rank().

This is far more efficient than windowing raw transactions.

🚀 Why This Project Matters

✔ Interview-ready
✔ Real-world logic
✔ Blog-worthy
✔ Production-style coding
✔ Performance-aware

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