If you’ve worked with SQL for data analysis, you’ve probably used the SELECT DISTINCT keyword to remove duplicate rows. But at some point, you might come across another term — SELECT UNIQUE.

Are they the same thing? Is one better or faster than the other?

In this article, we’ll explore the difference between SELECT DISTINCT and SELECT UNIQUE in SQL, with examples, practical use cases, and best practices for analysts who use SQL daily in tools like Power BI, Tableau, or direct database querying.

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Why Understanding DISTINCT vs UNIQUE Matters

Business and data analysts rely on SQL for data extraction, cleaning, and aggregation. One of the most common tasks is removing duplicates — ensuring that the dataset you’re analyzing reflects unique entities such as customers, transactions, or products.

While both UNIQUE and DISTINCT can appear to do the same thing, there are subtle differences in meaning and compatibility that can affect how your query behaves — especially when you’re working with enterprise databases like Oracle, Snowflake, or SQL Server.

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Quick Summary

Keyword	Function	Supported In	Recommended Use
DISTINCT	Removes duplicate rows from result set	All major SQL databases (Oracle, SQL Server, MySQL, PostgreSQL, Snowflake, BigQuery)	✅ Use this for standard SQL queries
UNIQUE	Legacy synonym for DISTINCT (mainly Oracle)	Oracle and some older DB systems	⚠️ Use only for backward compatibility

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The Purpose of DISTINCT

DISTINCT ensures that your query returns only unique combinations of columns in the result set.

Syntax

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

Example

Suppose you’re analyzing customer data from a Sales table:

CustomerID	Region	Sales
101	East	500
102	West	300
101	East	700

If you want to list all unique customers (regardless of multiple transactions):

SELECT DISTINCT CustomerID
FROM Sales;

✅ Result:

CustomerID
101
102

If you include more than one column, DISTINCT applies to the combination of those columns:

SELECT DISTINCT CustomerID, Region
FROM Sales;

✅ Result:

CustomerID	Region
101	East
102	West

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What About UNIQUE?

UNIQUE is an older keyword that some databases — particularly Oracle — support as a synonym for DISTINCT.

Example in Oracle SQL

SELECT UNIQUE Department_ID
FROM Employees;

This behaves exactly like:

SELECT DISTINCT Department_ID
FROM Employees;

However, the UNIQUE keyword is not part of the ANSI SQL standard, which means it may not work in databases like:

• Microsoft SQL Server
• PostgreSQL
• MySQL
• Snowflake

If you try SELECT UNIQUE in these systems, you’ll often get a syntax error.

Attention — UNIQUE vs. UNIQUE Constraint (Don’t Confuse!)

Be careful not to mix up the UNIQUE keyword in a SELECT statement with the UNIQUE constraint in table definitions:

CREATE TABLE Employees (
    EmployeeID INT PRIMARY KEY,
    Email VARCHAR(100) UNIQUE
);

Here, UNIQUE enforces that each email address must be unique — it’s a data integrity constraint, not a query keyword.

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Key Difference — Portability and Standards

Aspect	SELECT DISTINCT	SELECT UNIQUE
SQL Standard	✅ Yes (ANSI SQL compliant)	❌ No
Compatibility	Works in all major databases	Works mostly in Oracle (and older DB2, Informix versions)
Future-proof	Recommended for modern SQL	Deprecated in most systems
Performance	Same behavior as UNIQUE	Same behavior (in Oracle only)

So, in modern analytics environments — especially if you’re using SQL inside BI tools, data warehouses, or cloud platforms — DISTINCT is always the correct choice.

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Common Use Cases for DISTINCT

1. Removing Duplicate Customers

When your dataset has multiple transactions per customer:

SELECT DISTINCT CustomerID, CustomerName
FROM Orders;

2. Counting Unique Values

You can combine DISTINCT with aggregation:

SELECT COUNT(DISTINCT CustomerID) AS UniqueCustomers
FROM Orders;

This tells you how many unique customers placed orders — a common KPI in business analysis.

3. Finding Unique Categories or Regions

SELECT DISTINCT Category, Region
FROM Sales;

This helps you quickly check which product categories were sold in which regions.

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Performance Considerations

DISTINCT is a set operation, meaning the database engine must evaluate all selected rows and remove duplicates.

For small datasets, this is negligible. But for very large tables (hundreds of millions of rows), it can impact query time.

Tips to improve performance:

1. ✅ Only use DISTINCT when needed — avoid selecting all columns (SELECT DISTINCT *).
2. ✅ Use proper indexes on the columns you’re filtering.
3. ✅ Filter the dataset first with a WHERE clause before applying DISTINCT.
4. ✅ Use GROUP BY if you need aggregations — it can be more efficient in some engines.

Example (better approach):

Instead of:

SELECT DISTINCT CustomerID, Region
FROM Sales
WHERE OrderDate >= '2024-01-01';

Try:

SELECT CustomerID, Region
FROM Sales
WHERE OrderDate >= '2024-01-01'
GROUP BY CustomerID, Region;

The GROUP BY version can be optimized more efficiently by the SQL engine in many cases.

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Using DISTINCT Inside Aggregates

You can also use DISTINCT inside aggregate functions:

SELECT COUNT(DISTINCT ProductID) AS UniqueProducts,
       COUNT(ProductID) AS TotalProducts
FROM Sales;

This pattern is very common in Power BI data models, Tableau Prep, or SQL-based dashboards — where analysts need both total and unique counts.

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Common Mistakes to Avoid

❌ Using DISTINCT when you really need GROUP BY

DISTINCT removes duplicates; it doesn’t aggregate data. If you need totals or averages by group, use GROUP BY.

❌ Selecting all columns with DISTINCT

SELECT DISTINCT * forces the engine to compare every column — a heavy operation for large datasets.

❌ Assuming UNIQUE works in all databases

It doesn’t. Always prefer DISTINCT for portability and compliance.

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Best Practice Summary

Recommendation	Why
✅ Always use DISTINCT (not UNIQUE)	ANSI standard and widely supported
✅ Combine with COUNT() for unique metrics	Ideal for dashboards and KPIs
⚙️ Use GROUP BY for aggregations	More efficient for grouped summaries
🧠 Avoid DISTINCT *	Costly and unnecessary
💡 Optimize with filters and indexes	Improves query performance

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Real-World Example (for Analysts)

You’re analyzing customer orders in an e-commerce dataset. Management wants to know:

“How many unique customers placed an order in Q1 2025?”

Here’s the query:

SELECT COUNT(DISTINCT CustomerID) AS UniqueCustomers
FROM Orders
WHERE OrderDate BETWEEN '2025-01-01' AND '2025-03-31';

Result:

UniqueCustomers
8,524

You can now confidently report that your platform had 8,524 unique customers in Q1.

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Conclusion

Both SELECT DISTINCT and SELECT UNIQUE can filter duplicate records, but only DISTINCT is the modern, portable, and SQL-standard approach.

For data analysts and BI professionals, using DISTINCT ensures your queries will run consistently across SQL Server, PostgreSQL, MySQL, Snowflake, BigQuery, and Oracle without breaking.

So remember:

✅ Always use DISTINCT.
❌ Avoid UNIQUE except in legacy Oracle scripts.

With this understanding, you’ll write cleaner, faster, and more reliable SQL queries for all your analytical projects.