Mastering SQL Logical Operators for Data Analytics: A Beginner’s Guide

Mastering SQL Logical Operators for Data Analytics: A Beginner’s Guide
🎓 Who Is This For?
This guide is designed for fresh graduates and aspiring data analysts who are just starting with SQL. If you have basic SQL knowledge and want to harness the power of logical operators to enhance your queries, you’re in the right place!
🔍 What Are Logical Operators in SQL?
Logical operators in SQL help us filter records by applying logic to the conditions in a query. They are essential when you want to retrieve data that satisfies multiple conditions.
Common Logical Operators in SQL:
- AND – Returns records that satisfy all specified conditions.
- OR – Returns records that satisfy at least one condition.
- NOT – Excludes records that meet a specified condition.
- BETWEEN, IN, LIKE – These also fall under logical filtering but aren’t boolean operators strictly.
📘 Why Logical Operators Matter in Analytics
Imagine you’re analyzing customer data, and you want to filter only those customers who:
- Have made a purchase in the last month AND
- Are located in New York OR California
Without logical operators, writing such conditional queries would be impossible. These operators bring in the power of decision-making to your SQL statements.
📌 The SQL WHERE Clause Revisited
Logical operators are primarily used inside the WHERE
clause. Here’s a basic example:
SELECT * FROM customers WHERE state = 'California' AND last_purchase_date > '2024-05-01';
🔗 Operator #1: AND
The AND operator ensures that all conditions in the WHERE clause must be true for a record to be included.
Retrieve employees who work in the ‘Sales’ department and earn more than $50,000.
SELECT * FROM employees WHERE department = 'Sales' AND salary > 50000;
🧠 How It Works
The query returns only those rows where both conditions are satisfied. If even one is false, the row is excluded.
SELECT * FROM employees WHERE department = 'Sales' AND salary > 50000 AND experience_years > 5;
⚖️ Truth Table for AND
Condition A | Condition B | A AND B |
---|---|---|
TRUE | TRUE | TRUE ✅ |
TRUE | FALSE | FALSE ❌ |
FALSE | TRUE | FALSE ❌ |
FALSE | FALSE | FALSE ❌ |
💡 Real-world Example
As a junior data analyst, your team may ask you to generate a report of all products that:
- Have inventory count less than 100 AND
- Were last restocked more than 30 days ago
You can use AND to solve this efficiently!
👉
🔗 Operator #2: OR
The OR operator returns records where at least one of the specified conditions is true. This allows for broader data retrieval.
Get customers who are either from ‘California’ or ‘Texas’.
SELECT * FROM customers WHERE state = 'California' OR state = 'Texas';
🧠 Why Use OR?
Unlike AND, which restricts your result set, OR expands it. You use OR when any of the conditions are acceptable. It’s great for offering users multiple filters like “show me people in either New York or LA.”
⚖️ Truth Table for OR
Condition A | Condition B | A OR B |
---|---|---|
TRUE | TRUE | TRUE ✅ |
TRUE | FALSE | TRUE ✅ |
FALSE | TRUE | TRUE ✅ |
FALSE | FALSE | FALSE ❌ |
🎯 Combining AND & OR with Parentheses
It’s crucial to understand the order in which SQL evaluates conditions. That’s where parentheses come into play, helping you control precedence and prevent unexpected results.
Get customers from ‘California’ OR ‘Texas’ AND their purchase total is over $1,000.
SELECT * FROM customers WHERE (state = 'California' OR state = 'Texas') AND purchase_total > 1000;
This query ensures the condition for purchase_total applies to both California and Texas.
SQL might interpret it as:
SELECT * FROM customers WHERE state = 'California' OR (state = 'Texas' AND purchase_total > 1000);
Result: All Californians will show regardless of their purchase total, which might not be wha
🔄 Operator #3: NOT
The NOT operator is used to reverse a condition’s logical value. It returns rows that do not meet the specified condition.
Get all employees who are not in the ‘HR’ department.
SELECT * FROM employees WHERE NOT department = 'HR';
You can also combine NOT
with IN
, BETWEEN
, or LIKE
for more refined control.
📍 Operator #4: IN
The IN operator simplifies multiple OR conditions by checking if a value is in a given list.
Fetch all orders from states: ‘New York’, ‘California’, and ‘Texas’.
SELECT * FROM orders WHERE state IN ('New York', 'California', 'Texas');
Using IN
is cleaner than chaining multiple OR
statements.
📏 Operator #5: BETWEEN
The BETWEEN operator checks whether a value falls within a specified range, inclusive of boundary values.
Get all products priced between $100 and $500.
SELECT * FROM products WHERE price BETWEEN 100 AND 500;
This is widely used in sales and financial reporting queries.
🔎 Operator #6: LIKE
LIKE is used for pattern matching using wildcards:
%
– matches zero or more characters_
– matches exactly one character
Get all customers whose name starts with ‘A’.
SELECT * FROM customers WHERE name LIKE 'A%';
💬 Combining with NOT
You can use NOT with LIKE to exclude patterns.
SELECT * FROM customers WHERE name NOT LIKE 'A%';
📊 Analytics Use Case: Marketing Campaign Filters
Your team needs to send an email campaign to customers who:
- Are not located in ‘California’
- Registered between Jan 1 and Mar 31
- Have email addresses ending with ‘@gmail.com’
Here’s how your SQL query might look:
SELECT * FROM customers WHERE state != 'California' AND signup_date BETWEEN '2024-01-01' AND '2024-03-31' AND email LIKE '%@gmail.com';
🧠 Summary: Choose the Right Tool
- NOT – exclude rows
- IN – match against a list
- BETWEEN – filter by a range
- LIKE – pattern search (with wildcards)
🧪 Practice Makes Perfect
The more you use these operators, the more intuitive they become. Try combining them with real datasets or sandbox tools like SQLZoo, Mode Analytics, or DB Fiddle.
Next up in Part 4: Advanced use cases with logical operators, nested conditions, performance tips, and common mistakes to avoid.
⚙️ Advanced Filtering with Nested Conditions
Complex business rules often require nested conditions. These allow more control and help in writing queries that mimic real-world logic closely.
📌 Example: Multi-layered Campaign Filter
Retrieve users who:
- Live in ‘New York’ or ‘Chicago’
- AND have either spent more than $1000 or registered before 2024
SELECT * FROM users WHERE (city = 'New York' OR city = 'Chicago') AND (total_spent > 1000 OR signup_date < '2024-01-01');
Parentheses help us separate the logic so SQL evaluates it correctly.
📈 Performance Tips
- Use indexed columns in logical expressions for faster query performance.
- Avoid NOT with LIKE or BETWEEN on large tables—it can hurt performance.
- Use IN instead of multiple ORs where possible.
- Use EXPLAIN (MySQL/PostgreSQL) to check how your query performs.
🚫 Common Mistakes
- ❌ Missing parentheses: can cause incorrect logic.
- ❌ Confusing AND/OR precedence.
- ❌ Overusing NOT without understanding implications.
- ❌ Comparing NULL with = instead of using
IS NULL
.
🧪 Bonus: Mini Project for Practice
Dataset: E-commerce customer table
Columns: customer_id, name, email, city, state, total_spent, signup_date, status
Task: Write a query to find customers who:
- Are not from ‘Texas’
- Spent between $500 and $2000
- Signed up in 2023
- Email ends with ‘@yahoo.com’
SELECT * FROM customers WHERE state != 'Texas' AND total_spent BETWEEN 500 AND 2000 AND signup_date BETWEEN '2023-01-01' AND '2023-12-31' AND email LIKE '%@yahoo.com';
🧠 Final Thoughts
SQL logical operators are the backbone of data filtering. Whether you’re building dashboards, writing reports, or running quick data audits, mastering these tools makes you a more effective and employable data analyst.
📚 What Next?
- Practice filtering with real datasets (Kaggle, Data.gov)
- Use SQL challenges like StrataScratch or LeetCode SQL
- Explore advanced concepts: joins, window functions, CTEs
🎉 Thanks for following the full blog series! You now have a solid foundation in logical operators in SQL—one of the most valuable tools for a career in data analytics.
🏁 End of Blog Series — Happy Querying!