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## Data Modeling Best Practices – Headings
1. Start with Business Requirements (Not Tables)
2. Define Grain First (Critical Rule)
3. Choose the Right Model Type
4. Use Clear Naming Conventions
5. Design Clean Dimension Tables
6. Keep Fact Tables Lean
7. Enforce Data Integrity
8. Handle Dates Properly
9. Optimize for Query Performance
10. Plan for Data Growth
11. Document Everything
12. Validate with Real Data
13. Version Control Your Models
14. Avoid Over-Normalization in Warehouses
15. Design for BI & Reporting Tools
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## 1. Start with Business Requirements (Not Tables)
**Best Practice**
* Understand **what questions the business wants answered**, not just source schemas.
**Why**
* Prevents over-engineering and unused columns/tables.
**How**
* Identify KPIs, reports, dashboards
* Define **facts**, **dimensions**, and **grain**
* Write requirements in plain language
*Example: “Daily sales by product and region”*
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## 2. Define Grain First (Critical Rule)
**Best Practice**
* Clearly define the **lowest level of detail** for each fact table.
**Why**
* Prevents aggregation errors and duplicate data.
**Example**
* ❌ “Sales table” (too vague)
* ✅ “One row per product, per order, per day”
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## 3. Choose the Right Model Type
### a) OLTP (Transactional Systems)
* Use **3NF (Third Normal Form)**
* Minimize redundancy
* Optimize for **writes**
### b) OLAP / Data Warehouse
* Use **Dimensional Modeling**
* Star schema preferred
* Optimize for **reads & analytics**
**Rule of Thumb**
> Applications → normalized
> Analytics → dimensional
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## 4. Use Clear Naming Conventions
**Best Practice**
* Use **business-friendly, consistent names**
**Examples**
* `customer_id` (not `custid`)
* `order_date` (not `dt1`)
* Tables: singular or plural—choose one and stick to it
**Why**
* Improves usability for analysts and BI tools.
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## 5. Design Clean Dimension Tables
**Best Practices**
* Use **surrogate keys** (not natural keys)
* Keep dimensions **wide, not deep**
* Handle Slowly Changing Dimensions (SCD)
**SCD Types**
* Type 1 – overwrite (no history)
* Type 2 – add new row (full history) ⭐ most common
* Type 3 – limited history
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## 6. Keep Fact Tables Lean
**Best Practices**
* Store only:
* Foreign keys
* Numeric measures
* Avoid text fields in facts
**Example**
* ✔ `sales_amount`, `quantity`
* ❌ `customer_name`, `product_description`
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## 7. Enforce Data Integrity
**Best Practice**
* Use constraints where possible
**Examples**
* Primary keys
* Foreign keys
* NOT NULL for required fields
**Why**
* Prevents silent data corruption.
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## 8. Handle Dates Properly
**Best Practice**
* Use a **Date Dimension**, not raw dates everywhere
**Benefits**
* Easy filtering by:
* Year
* Month
* Quarter
* Fiscal periods
* Holidays
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## 9. Optimize for Query Performance
**Best Practices**
* Index:
* Foreign keys
* Frequently filtered columns
* Use partitioning for large fact tables
* Avoid unnecessary joins
**Cloud Tip (AWS)**
* Redshift / Athena → sort keys & distribution keys
* RDS → proper indexing and query plans
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## 10. Plan for Data Growth
**Best Practice**
* Design for **scale from day one**
**How**
* Avoid hard-coding values
* Use surrogate keys
* Anticipate new dimensions & measures
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## 11. Document Everything
**Best Practice**
* Maintain data model documentation
**Include**
* Table purpose
* Column definitions
* Grain
* Business rules
**Why**
* Reduces dependency on individuals.
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## 12. Validate with Real Data
**Best Practice**
* Test models using **actual data volumes**
**Checks**
* Row counts
* Duplicates
* Aggregation accuracy
* Edge cases
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## 13. Version Control Your Models
**Best Practice**
* Treat data models like code
**How**
* Store DDL in Git
* Track schema changes
* Use migration scripts
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## 14. Avoid Over-Normalization in Warehouses
**Best Practice**
* Prefer **simplicity over theoretical purity**
**Why**
* BI tools and users prefer fewer joins.
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## 15. Design for BI & Reporting Tools
**Best Practice**
* Models should be **self-service friendly**
**How**
* Meaningful column names
* Pre-calculated metrics where useful
* Consistent dimensions across facts (conformed dimensions)
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## Golden Rule Summary
> **A good data model is easy to understand, hard to misuse, and flexible to change.**
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