CarouselSQL & Databases
Your Data is a Mess. Let SQL Fix It
Dirty data leads to inaccurate reports and analysis Ensures consistency across your entire dataset
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01Why Clean Data?
- Dirty data leads to inaccurate reports and analysis
- Ensures consistency across your entire dataset
- Builds a reliable foundation for machine learning models
- Saves time in the long run by preventing rework
- Essential for making confident business decisions
- Improves the performance of your database queries
02Find Missing Values
- Use IS NULL to identify empty fields
- Use IS NOT NULL to find complete records
- Count nulls with COUNT(*) - COUNT(column_name)
- Use COALESCE to replace nulls with a default value
- Investigate why data is missing for critical fields
- Decide if rows with nulls should be kept or removed
03Tame Text Data
- Use TRIM to remove extra spaces from strings
- Standardize case with UPPER() or LOWER() functions
- Replace substrings using the REPLACE function
- Check for inconsistent entries (e.g., 'USA', 'U.S.A.', 'US')
- Use LENGTH() to find abnormally long or short entries
- CONCAT strings to merge first and last names
04Fix Data Types
- Ensure numbers are stored as INT or DECIMAL types
- Convert strings to proper DATE or DATETIME types
- Use CAST or CONVERT functions to change types
- Check for numbers mistakenly stored as text
- Validate that booleans are represented consistently
- Correct mismatched types before mathematical operations
05Remove Duplicates
- Use GROUP BY and HAVING to find duplicate rows
- Identify duplicates based on key columns
- Use ROW_NUMBER() to rank and delete duplicates
- Keep the most recent or complete record
- Use SELECT DISTINCT for quick duplicate-free results
- Always validate counts before and after deduplication
06Handle Outliers
- Use MIN() and MAX() to spot extreme values
- Calculate averages and standard deviations
- Filter data using WHERE and comparison operators
- Decide if outliers are errors or valid data points
- Use percentiles to identify top and bottom extremes
- Cap extreme values if they skew analysis
07Standardize Formats
- Enforce consistent date formats (YYYY-MM-DD)
- Standardize phone numbers and country codes
- Apply a single currency format across financial data
- Create categories for free-text fields
- Use CASE statements to map values to a standard
- Ensure units of measurement are consistent
08Validate Data
- Use CHECK constraints to prevent future errors
- Ensure foreign keys match primary keys in other tables
- Verify that numeric values fall within expected ranges
- Check that dates are logical (e.g., no future birth dates)
- Use LIKE with wildcards to find pattern violations
- Create data quality rules for ongoing checks
09Your Cleaning Toolkit
- UPDATE to modify existing records
- ALTER TABLE to add, drop, or change columns
- DELETE to remove incorrect rows (use with caution)
- CASE statements for conditional logic and categorizing
- String functions (TRIM, REPLACE, SUBSTRING) for text
- Temporary tables to test changes before applying them
- Always backup your data before starting any cleaning process
