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)
Th…
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
Follow for more such content. Let me know if I missed anything in comments. Thank you!!