the_information_nexus/tech_docs/database/OLTPvsOLAP.md

Great idea! Let’s use the library database example for both normalized (OLTP) and denormalized (OLAP) models so we can compare them directly.

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Library Database Example

1. Normalized (OLTP) – Optimized for Transactions

Goal: Efficiently manage book checkouts, returns, and updates while minimizing redundancy.

Tables (3NF Structure):

Books Table  
+---------+------------+-----------+  
| BookID  | Title      | AuthorID  |  
+---------+------------+-----------+  

Authors Table  
+-----------+------------+  
| AuthorID  | AuthorName |  
+-----------+------------+  

Borrowers Table  
+-------------+--------------+  
| BorrowerID  | BorrowerName |  
+-------------+--------------+  

Loans Table (Transactions)  
+-----------+-------------+---------+------------+------------+  
| LoanID    | BorrowerID  | BookID  | LoanDate   | ReturnDate |  
+-----------+-------------+---------+------------+------------+  

Why Normalized?

• No duplicate data (e.g., author names stored only once).
• Easy to update (e.g., change an author’s name in one place).
• Optimized for fast writes (recording loans, returns).

Problem for Analytics:

• To find "How many books by Author X were borrowed last month?", you must join 4 tables:

  SELECT COUNT(*) 
  FROM Loans  
  JOIN Books ON Loans.BookID = Books.BookID  
  JOIN Authors ON Books.AuthorID = Authors.AuthorID  
  WHERE Authors.AuthorName = 'J.K. Rowling' 
    AND Loans.LoanDate BETWEEN '2023-01-01' AND '2023-01-31';
  

  → Slow for large datasets.

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2. Denormalized (OLAP) – Optimized for Analytics

Goal: Speed up queries for reports like "Top borrowed authors by month."

Star Schema Structure:

Fact_Loans (Central Fact Table)  
+--------+-------------+---------+-----------+------------+------------+  
| LoanID | BorrowerID  | BookID  | AuthorID  | LoanDate   | ReturnDate |  
+--------+-------------+---------+-----------+------------+------------+  

Dim_Books (Dimension Table)  
+---------+------------+-----------------+  
| BookID  | Title      | Genre           |  
+---------+------------+-----------------+  

Dim_Authors (Dimension Table)  
+-----------+------------+----------------+  
| AuthorID  | AuthorName | Nationality    |  
+-----------+------------+----------------+  

Dim_Borrowers (Dimension Table)  
+-------------+--------------+---------------+  
| BorrowerID  | BorrowerName | MembershipTier|  
+-------------+--------------+---------------+  

Dim_Time (Dimension Table)  
+------------+-------+-------+------+  
| LoanDate   | Month | Quarter | Year|  
+------------+-------+-------+------+  

Key Denormalizations:

1. Fact_Loans includes AuthorID (redundant, since it’s also in Books).
2. Dim_Time pre-calculates month/quarter/year for faster filtering.
3. No strict foreign keys (optimized for reads, not updates).

Same Query, But Faster:

SELECT COUNT(*)  
FROM Fact_Loans  
JOIN Dim_Authors ON Fact_Loans.AuthorID = Dim_Authors.AuthorID  
WHERE Dim_Authors.AuthorName = 'J.K. Rowling'  
  AND Fact_Loans.LoanDate BETWEEN '2023-01-01' AND '2023-01-31';

→ Fewer joins (just 2 tables) → Better performance for analytics.

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Side-by-Side Comparison (Library Example)

Feature	Normalized (OLTP)	Denormalized (OLAP)
Structure	4+ tables (Books, Authors, Loans, etc.)	Star schema: 1 fact table + dimensions
Author Data	Only in Authors (linked via Books)	Duplicated in Fact_Loans for speed
Query Complexity	Needs multi-table joins	Fewer joins (optimized for reads)
Update Efficiency	Perfect (no redundancy)	Slow (batch updates, redundant data)
Use Case	Daily operations (checkouts, returns)	Monthly reports, trend analysis

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Key Takeaways

1. Normalized (OLTP):

   • Like a library’s backend system—efficient for daily transactions.
   • "Where is this book? Who borrowed it?" → Fast updates, strict integrity.

2. Denormalized (OLAP):

   • Like a library’s annual report—optimized for questions like:
     • "Which genre is most popular?"
     • "Do VIP members borrow more books?"
   • Redundancy is intentional to avoid joins.

Would you like to see how an ETL pipeline transforms the normalized library OLTP data into a denormalized OLAP star schema?