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Snowflake Schema is not about performance… It’s about structure and consistency. Use it when: ✔ Data has deep hierarchies ✔ Frequent updates happen ✔ Redundancy becomes a problem Avoid it when spee
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Most candidates design only orders… But real systems include: ✔ Menu changes ✔ Inventory tracking ✔ Multi-outlet complexity That’s what makes this a real data modeling problem. If you can explain t
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This is how you answer data modeling questions in interviews 👇 Not just tables… but: ✔ Requirements ✔ Schema ✔ Performance ✔ Trade-offs If you can explain like this, you’re already ahead of most ca
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SCD Type 2 is one of the most asked topics in data engineering interviews. But most people only know theory… not implementation. Here’s the real approach: 👉 Expire old record 👉 Insert new record �
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Most candidates fail data modeling interviews not because they don’t know concepts… But because they can’t explain trade-offs. Real-world systems are all about: 👉 Performance vs Cost 👉 Simplicity
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If your queries are slow… this might be the reason 👇 👉 You’re scanning too much data Partitioning fixes that. Instead of scanning entire tables: ✔ Query only relevant chunks ✔ Faster performance
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Understanding relationships is the foundation of data modeling. Without it, your data is just disconnected tables. 3 types you must know: • One-to-One → Rare but useful • One-to-Many → Most common
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Data changes… but do you track it? That’s where SCD comes in. 👉 Slowly Changing Dimensions help you manage data changes over time Types you must know: • Type 1 → Overwrite • Type 2 → Full histor
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Metrics alone don’t tell the full story… Dimensions give them meaning. 👉 Who bought? 👉 What was sold? 👉 When did it happen? Types you must know: • Type 1 → No history • Type 2 → Full history (
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If you don’t understand fact tables… you don’t understand analytics. Fact tables are where the real value lives: 👉 Metrics 👉 Business events 👉 KPIs Types you must know: • Transaction → every eve
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If your data keeps changing… Data Vault is your best friend. Unlike Star Schema, Data Vault is built for: ✔ Flexibility ✔ Scalability ✔ Historical tracking Structure: • Hubs → Core entities • Link
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Not all schemas are built for speed. Snowflake Schema focuses on structure and efficiency instead of raw performance. 👉 Dimensions are normalized 👉 Data redundancy is reduced 👉 But queries become
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Data Engineering Notebook

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Data Engineering in simple words. Cloud • ETL • Lakehouse Daily notes from the notebook.

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