Lesson Description
Keep only rows that match a condition.
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WHERE: filtering rows
Easy Project
Mini project: Create a hiring shortlist query that filters by role, salary range, and hire date with deterministic ordering.
WHERE — filter rows
WHERE keeps only the rows that satisfy a condition. Without WHERE, every row in the table is returned.
Basic syntax
SELECT columns
FROM table_name
WHERE condition;
Comparison operators
| Operator | Meaning |
|---|---|
= | Equal |
!= or <> | Not equal |
<, > | Less / greater than |
<=, >= | Less / greater than or equal |
Examples
-- Employees earning above 10 000
SELECT first_name, last_name, salary
FROM employees
WHERE salary > 10000;
-- Only the IT department (id = 60)
SELECT first_name, last_name
FROM employees
WHERE department_id = 60;
-- A specific employee by name
SELECT *
FROM employees
WHERE last_name = 'King';
String comparisons
String comparisons are case-sensitive by default in PostgreSQL. Use ILIKE for case-insensitive matching (covered in the next course).
Date comparisons
Dates can be compared directly using ISO format:
SELECT first_name, hire_date
FROM employees
WHERE hire_date > '2005-01-01';
Tips
WHEREis evaluated beforeSELECT, so you can filter on columns you are not selecting.- One
WHEREclause per query; combine multiple conditions withAND/OR.
Learning context
This lesson is part of SQL Fundamentals. Focus on the core idea in WHERE: filtering rows, then validate with deliberate practice.
What to master
- Understand clause order and intent
- Write deterministic result queries
- Use clear aliases and readable structure
Common mistakes
- Skipping ORDER BY in final outputs
- Using joins before validating base rows
- Not checking row counts after changes
High-level strategy
- Start simple, then layer complexity
- Run often and compare expected shape
- Refactor with CTEs when logic grows
Task ladder
- Baseline query: get a correct first output.
- Add one complication: edge case, join, filter, or ranking rule.
- Re-run and refine until output is stable and explainable.
Transparent data checks
- Check row count before and after each major clause.
- Validate one manual sample row for correctness.
- Confirm final ordering and column naming for trust.
Retention loop
- Run the solution once.
- Rewrite from memory with one variation.
- Explain the logic in plain English in one sentence.
Logical reasoning for commands
WHERE
Why: Limits rows to only the business-relevant subset.
Memory cue: Filter early to reduce noise.
ORDER BY
Why: Makes output deterministic and reviewable.
Memory cue: No ORDER BY means no guaranteed row order.
AS alias
Why: Makes output columns readable for teams and reports.
Memory cue: If the name is clear, the query is easier to trust.
Concept check
Quick check: WHERE: filtering rows
3 quick questions. One at a time. Instant score at the end.
Exercise 1
Employees earning more than $10,000
Enterprise Workforce Analyticseasy
List the first_name, last_name, and salary of every employee with a salary above 10000.
Run a query to see results here.
Exercise 2
Everyone in IT
Enterprise Workforce Analyticseasy
Return the first_name and last_name of employees in department 60 (IT).
Run a query to see results here.
Exercise 3
Employees hired after 2005
Enterprise Workforce Analyticseasy
Return first_name, last_name, and hire_date for employees hired after 2005-01-01, ordered by hire_date ascending.
Run a query to see results here.
Exercise 4
In-stock products under $50
Commerce Operations Intelligenceeasy
From the store sandbox, return name, price, and stock for products with price < 50 AND stock > 0, ordered by price ascending.
Run a query to see results here.
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