DATA CLEANING IN SQL

Data cleaning in SQL involves identifying and correcting errors, inconsistencies, and inaccuracies in a database. The process of data cleaning is critical to ensuring the accuracy and reliability of data, which in turn helps to improve the quality of analysis and decision-making.

The following are some common techniques used for data cleaning in SQL:

Removing duplicates: Duplicate records in a database can cause errors in analysis and decision-making. To remove duplicates in SQL, we can use the DISTINCT keyword or GROUP BY clause to eliminate redundant rows.

Filtering out null values: Null values can distort analysis and cause errors. We can use the WHERE clause to filter out null values from a table.

Correcting data types: Inconsistencies in data types can lead to errors and cause problems in analysis. We can use the CAST or CONVERT functions to convert data types in SQL.

Removing outliers: Outliers are values that fall outside of the expected range and can distort analysis. We can use the HAVING clause or subqueries to identify and remove outliers from a table.

Standardizing data: Standardizing data involves converting data to a consistent format. We can use the REPLACE or SUBSTRING functions to standardize data in SQL.

Merging and splitting data: When data is stored in different formats or in multiple tables, we can use SQL to merge or split data. The JOIN and UNION clauses are commonly used for merging data, while the SUBSTRING and CONCAT functions are used for splitting data.

Overall, data cleaning in SQL is a critical process for ensuring data accuracy and reliability. By identifying and correcting errors and inconsistencies, we can ensure that our data is consistent, accurate, and reliable, which in turn leads to better analysis and decision-making.