Welcome back to Zero2DataEngineer!
Today, we go deeper into how schema design can either streamline your data pipeline… or turn it into a spaghetti monster. we unpack how your database schema shapes everything — speed, cost, logic, scalability.
And no, it’s not just theory.
Good schema = fast queries, fewer bugs, and easier handovers.
Bad schema = JOIN hell, duplication drama, and angry dashboards.
What’s the Difference?
Normalization Forms (Real-World Style)
1NF – First Normal Form
Rule: Atomic columns, no repeating groups
Think of it like: Every drawer in your closet holds only one type of item
Example:
Bad:Phone Numbers = 123, 456
Good: Separate rows per number
2NF – Second Normal Form
Rule: 1NF + all columns depend on entire primary key
Think of it like: No room for half-relevant details
Example: Don't include
Student Name
in a table where the primary key is (Student_ID, Course_ID)
3NF – Third Normal Form
Rule: 2NF + no transitive dependencies
Think of it like: No gossip. Columns shouldn't depend on other non-key columns.
Example: Move
Department_Location
to a separateDepartments
table instead of placing it inEmployees
.
Real Life Scenarios
Fintech Use Case: Normalized Schema (OLTP)
Let’s say you're working at a fintech startup on a banking system. You separate:
Customer Table → updated rarely
Accounts Table → moderate frequency
Transactions Table → updated constantly
Why this works: Different update rates + data integrity + modular ETL pipelinesYour transactional system handles:
Customers
Accounts
Transactions
Instead of storing everything in one big table, you normalize:
One table for customer details
Another for account info
A third for transactions
Why?
Because each table changes at different speeds and scales. Transactions update constantly. Customers, rarely. This improves storage, indexing, and update reliability.