This makes time-based analysis much easier.

🔹 12. Why Month Number Is Important

If you display:

January

February

March

April

Power BI may sort month names alphabetically depending on the setup.

You need a:

Month Number

January → 1

February → 2

March → 3

Then sort Month by Month Number.

🔹 13. Understand the Grain

Before creating relationships, ask:

"What does one row represent?"

For example:

Sales table

→ One row = One order

or:

Sales table

→ One row = One order item

These are different grains.

If you don't understand the grain, you can accidentally double-count sales.

🔹 14. Example of a Grain Problem

Suppose one order contains:

Order 1001

Laptop → ₹60,000

Mouse → ₹2,000

The order-item table has two rows.

If you join this with another table incorrectly, the ₹62,000 order value could potentially be repeated.

So before creating relationships or calculations:

Always understand the grain of your tables.

🔹 15. Active and Inactive Relationships

Sometimes two tables can have more than one possible relationship.

For example, Sales may contain:

Order_Date

Ship_Date

Both could connect to the Date table.

But Power BI generally allows only one active relationship between the same pair of tables at a time.

The other relationship can be inactive and activated when needed using DAX.

This becomes important when building advanced date analysis.

🔹 16. Filter Direction

Relationships control how filters move between tables.

In a simple star schema:

Customer

↓

Sales

filters usually flow from the dimension toward the fact table.

Avoid using bi-directional filtering everywhere.

It can create:

• Ambiguous relationships

• Unexpected results

• Difficult-to-debug models

• Performance issues

🔹 17. Don't Create Relationships Just Because Column Names Match

For example:

Customer_ID

appearing in two tables doesn't automatically mean they should be connected.

Check:

✔ Same business meaning

✔ Compatible data type

✔ Correct grain

✔ Unique values on the "one" side

✔ Correct cardinality

🎯 Interview Question

What is the difference between a Fact Table and a Dimension Table?

Fact Table

Contains business transactions and measurable values.

Example:

"Sales, Quantity, Cost"

Dimension Table

Contains descriptive information used to analyze those transactions.

Example:

"Customer, Product, Date, Region"

Easy way to remember:

Fact = What happened

Dimension = Describe what happened

💡 Key Lesson

Don't build your Power BI visuals before understanding your data model.

A good model makes your calculations easier, your reports more reliable, and your analysis much easier to maintain.

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