SQL Fundamentals: DISTINCT & Advanced Filtering
Learn how to remove duplicates and apply precise filtering using DISTINCT, WHERE, and HAVING — essential for clean and reliable data analysis.
Series Note:
This blog is part of the SQL Fundamentals for Data Analysts series — 4 by Shahnaz Insights.
- In Part 1, we retrieved and filtered data using
SELECT,FROM,WHERE - In Part 2, we sorted data using
ORDER BY,LIMIT - In Part 3, we created insights using aggregation and GROUP BY
👉 Now in Part 4, we focus on data cleaning and refinement
Understanding:
Raw data is often messy.
It may contain:
- Duplicate values
- Repeated categories
- Irrelevant records
Stakeholders don’t want duplicates —
they want clear and accurate insights
👉 To achieve this:
- DISTINCT → removes duplicates
- WHERE → filters raw data
- HAVING → filters grouped insights
What You’ll Learn:
- Remove duplicate values using DISTINCT
- Identify unique categories and combinations
- Apply WHERE and HAVING correctly
- Clean data before analysis
📊 Sample Dataset: employees:
This dataset uses appropriate data types to store different kinds of information:
- INT → numeric values (id, salary, sales)
- VARCHAR(25) → text values (name, department, up to 25 characters)
By using the right data types, the dataset remains well-organized, supports filtering and grouping, and ensures reliable results when applying operations like HAVING, DISTINCT, and aggregations.
We insert employee records across IT, HR, and Finance.
This dataset simulates real-world scenarios with different salaries and sales values, making it useful for applying HAVING, DISTINCT, and aggregation.
Result:
The table shows all records in raw form.
Using aggregation and GROUP BY, we can turn this data into meaningful insights.
🔍 Examples with Observation & Analyst Interpretation:
✅ Step 1: DISTINCT — Removing Duplicates:
Result:
What you’ll observe:
Duplicate department values are removed, leaving only unique categories
Analyst Interpretation:
Helps understand the structure of data before analysis
✅ Step 2: DISTINCT with Multiple Columns:
Result:
What you’ll observe:
Only unique combinations of department and salary are returned
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Analyst Interpretation:
Useful for identifying unique patterns in data
✅Step 3: DISTINCT vs GROUP BY:
Result:
Result:
What you’ll observe:
Both return unique departments, but their purpose is different
Analyst Interpretation:
- DISTINCT → removes duplicates
- GROUP BY → prepares data for aggregation
✅Step 4: WHERE vs HAVING:
Result:
What you’ll observe:
WHERE filters rows before grouping
HAVING filters results after grouping
Analyst Interpretation:
WHERE → controls input data
HAVING → controls final output
✅Step 5: Combining DISTINCT with Filtering:
Result:
What you’ll observe:
Only unique departments meeting the salary condition are returned
Analyst Interpretation:
Removes duplicates while focusing only on relevant data
❌ Common Mistakes (Must Avoid):
- Using DISTINCT instead of GROUP BY for calculations
- Confusing DISTINCT with GROUP BY
- Using HAVING without GROUP BY
- Filtering with HAVING instead of WHERE
Analyst Insight:
Clean data = reliable insights
Before analysis:
Remove duplicates
Filter unnecessary data
After that:
👉 Apply aggregation (Part 3)
Conclusion:
Data becomes analysis-ready when you:
- Remove duplicates (DISTINCT)
- Filter correctly (WHERE)
- Refine results (HAVING)
This ensures your analysis is accurate and trustworthy
➡️ What’s Next — Series 5 (Advanced SQL):
Now we move into real-world data relationships:
✅ Part 1: JOINs & Data Relationships
Learn how to combine multiple tables for deeper insights
Final Insight:
Correct data leads to correct decisions.
DISTINCT doesn’t create insights —
it ensures the insights you create are clean and accurate.
Follow for More:
Follow to master real-world data analysis step by step through the Shahnaz Insights series
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