What is Data Cleaning?
Data cleaning is the process of detecting and correcting or removing inaccurate, incomplete, inconsistent or irrelevant data from a database or dataset. The goal is to improve data quality, ensure data integrity, and make dataset reliable for analysis, reporting, or machine learning.
Why Clean data in SQL?
SQL is widely used for storing, querying, and managing structured data in relational databases.
Cleaning data directly in SQL offers several advantages: • Efficiency: SQL operates directly on the data in the database, reducing the need for exporting/importing. • Scalability: Ideal for cleaning large datasets. • Integration: Seamlessly integrates with BI tools, data pipelines, and ETL processes. • Reproducibility: SQL scripts are reusable and version - controllable.
Key Data Cleaning techniques in SQL
- Removing Duplicates
- Standardize Data
- Trimming white Spaces
- Correcting Data Types
- Identifying Nulls
- Removing Unwanted Columns