Advanced DW Concepts for DE Interviews — SCD, ETL, Partitioning & Clustering| DE Roadmap #15

🏛️ Data Warehouse for Data Engineering — Session 2!

Continuing from Session 13, today we go DEEPER into the
advanced DWH concepts that are asked repeatedly in Data
Engineering interviews. Every topic is explained with
real examples and interview-ready answers.

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📌 WHAT WE COVER TODAY:
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⇄ ETL vs ELT:
✅ What is ETL — Extract → Transform → Load
✅ What is ELT — Extract → Load → Transform
✅ Why modern cloud DWHs (Snowflake, BigQuery) prefer ELT
✅ Interview Q: "When would you choose ETL over ELT?"
✅ Interview Q: "Explain the difference with a real pipeline"

◈ SLOWLY CHANGING DIMENSIONS (SCD):
✅ What are Dimensions and why they change over time
✅ SCD Type 1 — Overwrite (no history kept)
✅ SCD Type 2 — Add new row (full history preserved)
✅ SCD Type 3 — Add new column (limited history)
✅ When to use Type 1 vs Type 2 vs Type 3
✅ Interview Q: "A customer changes their address —
which SCD type do you use and why?"
✅ Real implementation example in SQL

⚡ PERFORMANCE OPTIMIZATION:
✅ INDEXING
— What is an index and how it speeds up queries
— B-tree vs Bitmap indexes — when to use which
— Interview Q: "Why can too many indexes hurt performance?"

✅ PARTITIONING
— Range, List & Hash partitioning explained
— Partition pruning — how it reduces query scan
— Interview Q: "How would you partition a 10TB orders table?"

✅ CLUSTERING
— What is clustering in Snowflake / BigQuery
— Difference between partitioning and clustering
— Interview Q: "When do you cluster vs partition a table?"

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💬 Which SCD type is used most at your company?
Comment below!

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#DataWarehouse #DataEngineering #ETL #ELT #SCD
#SlowlyChangingDimensions #Partitioning #Clustering
#Indexing #DataEngineer #Snowflake #BigQuery
#InterviewPrep #DEJobs #DataRoadmap #dbt

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