How do you handle duplicates in SQL?

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As a leading Data Science training institute in Hyderabad, Quality Thought focuses on personalized training with small batch sizes, allowing for greater interaction with instructors. Students gain in-depth knowledge of popular tools and technologies such as Python, R, SQL, Tableau, and more.

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How to Handle Duplicates in SQL: A Student’s Guide for Data Science

Handling duplicates is a crucial skill in SQL, especially for data science students—duplicate records can skew analytics, inflate storage needs, and degrade query performance. For instance, duplicate entries may lead to inaccurate summaries or biased predictive models. DataCamp highlights that duplicates “can compromise data integrity and database performance” , while GeeksforGeeks emphasizes their negative effects on analysis accuracy and storage use.

Here are key techniques you’ll learn in your Data Science Course:

• DISTINCT keyword – Returns unique records in queries.

• GROUP BY + HAVING COUNT(*)>1 – Finds fields with duplicates.

This effectively spots orders repeated in billing datasets.

• ROW_NUMBER() with CTE – Advanced method to rank and remove duplicates.

This approach keeps the first occurrence and removes extras.

• Self-join or DELETE with JOIN – Practical for bulk deletion:

This retains the earliest row per group.

In data warehousing, duplicates commonly stem from redundant source systems, faulty ETL logic, or poorly designed incremental loads. Data Engineer Journey outlines how GROUP BY/HAVING, ROW_NUMBER, and hashing methods help detect and prevent duplicates in pipelines.

Quality Thought: Cultivating a mindset of quality from the start—such as using primary keys or unique constraints—prevents many duplicates from entering your datasets. Enforcing schema design and practicing data quality checks are pillars of Quality Thought in data science.

In our Data Science Course, we help Educational Students master these techniques step by step: from recognizing why duplicates matter, to profiling data quality, to writing robust SQL that ensures clean, reliable datasets.

Conclusion

Effective duplicate handling in SQL—through methods like DISTINCT, GROUP BY + HAVING, window functions, and proper schema constraints—not only enhances data integrity but also aligns with the Quality Thought philosophy our courses promote. As an Educational Student training in data science, how will you apply these principles to elevate the quality of your analyses?

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