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Mastering the DISTINCT Keyword in SQL: When, Why, and What If You Don’t Use It

DISTINCT Keyword in SQL: When to Use It, Why It Matters, and What Happens If You Don’t

Introduction

If you’ve been learning SQL, chances are you’ve come across the DISTINCT keyword. At first glance, it seems simple: it removes duplicates from your query results. But the truth is, the decision to use DISTINCT (or not) can affect your data analysis, query performance, and even the insights you present to your manager or client.

I still remember my first encounter with DISTINCT. Back when I was asked to pull a quick report of customers who raised support tickets in the last month. I happily wrote a query, hit run, and boom — the count looked suspiciously high. My manager frowned:

“Why are we seeing more customers than we actually have in the system?”

That was my first lesson: duplicates can make or break your results, and DISTINCT was the lifesaver.

In this blog, we’ll explore when to use DISTINCT, why it matters, and what really happens if you skip it. Along the way, I’ll share real-life examples, SQL queries you can try yourself, and practical reflections that will help you build trust as a data professional.

What Is DISTINCT in SQL?

In SQL, the DISTINCT keyword ensures that the result set contains only unique values. It eliminates duplicates and helps you see the true diversity of your data.

Syntax:

SELECT DISTINCT column1, column2, ...
FROM table_name;

It can be applied to one or multiple columns.

  • Single column DISTINCT → removes duplicate values from that one column.

  • Multiple columns DISTINCT → treats the combination of values across columns as unique.

Why Duplicates Exist in the First Place

Before diving deeper, let’s ask: Why do duplicates even exist in databases?

  • Joins gone wrong: When you join tables without proper conditions, you may get duplicate rows.

  • Data entry errors: Manual entries can result in repeating values.

  • Many-to-many relationships: For example, one customer may buy multiple products, but when reporting, you only want unique customers.

  • ETL pipelines: Data pulled from different sources might create unintentional duplicates.

In short: duplicates are not always “bad,” but they’re often “context-dependent.” Sometimes duplicates represent reality (e.g., multiple purchases by the same customer), but other times they distort your numbers (e.g., counting the same customer multiple times).

When to Use DISTINCT

Here are the most common scenarios where DISTINCT becomes your best friend:

1. Listing Unique Values

If you want to know which products your company sells, or which cities your customers come from, DISTINCT is a quick win.

SELECT DISTINCT City
FROM Customers;

This ensures you don’t get “New York” listed 500 times just because 500 customers live there.

2. Counting Unique Records

Your manager asks: “How many unique customers purchased last month?” If you just use COUNT(CustomerID), you may overcount customers who bought multiple times.

SELECT COUNT(DISTINCT CustomerID) AS UniqueCustomers
FROM Orders
WHERE OrderDate BETWEEN '2025-07-01' AND '2025-07-31';

Without DISTINCT, you’d be reporting number of purchases, not number of unique customers.

3. Removing Duplicates from Joins

Let’s say you join Customers with Orders. A customer with 10 orders will appear 10 times. But maybe you just want a list of unique customers who have ever ordered.

SELECT DISTINCT C.CustomerID, C.ContactName
FROM Customers C
JOIN Orders O ON C.CustomerID = O.CustomerID;

Now each customer appears once, no matter how many orders they made.

4. Data Exploration & Cleaning

When exploring new datasets, DISTINCT helps identify unexpected duplicates or surprising values.

For example:

SELECT DISTINCT Gender
FROM Employees;

If the result includes Male, Female, and male, you’ve spotted a data inconsistency that needs cleaning.

5. Reporting & Dashboards

As a Power BI Developer (or any BI role), you’ll often need to deliver clear metrics. DISTINCT ensures your dashboards reflect unique entities (customers, accounts, policies, etc.) and don’t inflate KPIs.

Why to Use DISTINCT: The “Why That Matters” Layer

Here’s the real-world importance of DISTINCT:

  • Accuracy of Reports: Business leaders rely on your numbers. Duplicate inflation can mislead strategy.

  • Better Customer Insights: Knowing you have 5,000 unique customers (vs. 20,000 transactions) makes all the difference.

  • Data Cleaning Clues: Spotting unexpected duplicates can highlight deeper issues in your system.

  • Trustworthiness: As a data professional, you’re not just writing queries — you’re building trust. A wrong number can break credibility fast.

What Happens If You Don’t Use DISTINCT?

This is where things get interesting. Let’s imagine scenarios:

1. Inflated Numbers in Reports

You present a report saying:

  • “We served 10,000 customers this month.”
    But in reality, those were 10,000 orders from 3,000 unique customers. That’s a big credibility gap.

2. Wrong Business Decisions

A marketing manager thinks:

  • “Wow, we reached 10,000 people, let’s scale this campaign.”
    But if it’s only 3,000 unique customers, the ROI calculation is way off.

3. Duplicate Work in Data Cleaning

Without DISTINCT, you might mistake duplicates for valid records and waste time fixing what’s not broken.

4. Query Misinterpretation in Joins

For analysts learning SQL, the most common frustration is:

  • “Why is my count double?”
    The answer often lies in improper joins that weren’t corrected with DISTINCT.

When NOT to Use DISTINCT

Interestingly, DISTINCT isn’t always the answer. Overusing it can backfire.

  • Performance Issues: DISTINCT forces the database to scan, sort, and remove duplicates. On millions of rows, this can slow queries.

  • Masking Real Data: Sometimes duplicates are real (e.g., a customer ordering the same product twice). Using DISTINCT here hides useful insights.

  • Better Alternatives: Instead of DISTINCT, sometimes GROUP BY or window functions (ROW_NUMBER()) give more control.

Example:

SELECT CustomerID, COUNT(OrderID) AS OrderCount
FROM Orders
GROUP BY CustomerID;

This gives you both unique customers and their order counts — something DISTINCT alone can’t.

DISTINCT vs GROUP BY: The Common Confusion

Many beginners wonder: “Why use DISTINCT if I can use GROUP BY?”

  • DISTINCT → Just removes duplicates.

  • GROUP BY → Groups data and allows aggregation.

Example with DISTINCT:

SELECT DISTINCT Country
FROM Customers;

Example with GROUP BY:

SELECT Country
FROM Customers
GROUP BY Country;

Both give the same result here. But only GROUP BY lets you do this:

SELECT Country, COUNT(CustomerID) AS CustomersPerCountry
FROM Customers
GROUP BY Country;

So, think of DISTINCT as a quick de-duplication tool, while GROUP BY is for analysis and aggregation.

Real-Life Reflection: A Lesson from Broadband Data

I often had to report which areas had the highest number of complaints. The raw support data showed every complaint record, but if I forgot to use DISTINCT, the same customer raising three tickets in one day would blow up the count.

One day, I reported:

  • “100 customers faced issues yesterday.”

My manager cross-checked and said:

  • “Actually, it’s 40 customers. Some of them called multiple times.”

That experience taught me something deeper:
Data is not just numbers — it’s real people.
Misrepresenting duplicates meant misrepresenting people’s frustrations. From that day, I used DISTINCT mindfully.

Best Practices with DISTINCT

  1. Use only when necessary — Don’t throw it in every query.

  2. Check the source of duplicates — Sometimes fixing joins is better than masking with DISTINCT.

  3. Test with and without DISTINCT — Compare results to understand the data.

  4. Educate stakeholders — Explain the difference between “total transactions” vs. “unique customers.”

  5. Optimize for performance — On large datasets, consider indexing or alternative methods (GROUP BY, EXISTS, window functions).

Quick Quiz

Let’s see if you got it!

  1. Your boss asks: “How many products do we sell?” → DISTINCT or not?

  2. You need to know “How many times each product was sold?” → DISTINCT or GROUP BY?

  3. You want to find “Unique customers who raised support tickets.” → Use?

(Take a moment before scrolling up to answers!)

Conclusion

The DISTINCT keyword in SQL may look simple, but its impact is anything but. It ensures accuracy, credibility, and trustworthiness in your reporting. Use it when you need unique values, but avoid it when duplicates represent meaningful business realities.

If you don’t use DISTINCT in the right situations, you risk inflated numbers, wrong decisions, and loss of trust. And in the world of data, trust is everything.

So next time you write a query, pause and ask yourself:

  • Do I want all records, or just unique ones?

That single decision can change the story your data tells.

Location: India

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