Tame Your Text Data with SQL.

• Tame Your Text Data with SQL

Why Text Analytics?

• Unlock insights hidden in free-text survey responses
• Understand customer sentiment from reviews and feedback
• Categorize support tickets automatically for better routing
• Identify emerging trends and topics in large datasets
• Clean messy data for more accurate analysis
• Move beyond simple numerical metrics

The LIKE Operator

• Use for simple pattern matching in text
• The percent sign (%) matches any sequence of characters
• The underscore (_) matches any single character
• Example: Find all comments containing the word 'excellent'
• Query: SELECT * FROM feedback WHERE comment LIKE '%excellent%'
• Ideal for basic keyword searches
• Case-sensitive in some database systems

CHARINDEX Function

• Finds the starting position of a substring within a string
• Returns an integer value for the character position
• Returns 0 if the substring is not found
• Syntax: CHARINDEX('search_text', 'your_string')
• Useful for conditional logic based on text presence
• Often used with other functions to extract parts of text

PATINDEX Power

• Similar to CHARINDEX but uses more powerful patterns
• Allows for wildcard pattern matching like the LIKE operator
• Returns the starting position of the first pattern occurrence
• Syntax: PATINDEX('%pattern%', 'your_string')
• Example: Find the position of the first number in a string
• More flexible for complex search criteria

SUBSTRING & LEFT/RIGHT

• Extract specific parts of a text string
• SUBSTRING lets you pull text from any position
• LEFT returns a specified number of characters from the start
• RIGHT returns characters from the end of the string
• Often combined with CHARINDEX or PATINDEX for dynamic extraction
• Essential for parsing structured text within free-form fields

REPLACE & STUFF

• REPLACE swaps all instances of one substring with another
• Great for correcting common typos or standardizing terms
• STUFF deletes a specified length and inserts another string
• Useful for masking sensitive information within text
• Both functions help clean and standardize messy data

TRIM, LTRIM, RTRIM

• Remove unwanted spaces from your text data
• TRIM removes leading and trailing spaces
• LTRIM removes only leading spaces
• RTRIM removes only trailing spaces
• Critical for ensuring data consistency before analysis
• Prevents duplicate records due to spacing differences

LOWER, UPPER, PROPER

• Standardize the case of your text data
• LOWER converts a string to all lowercase letters
• UPPER converts a string to all uppercase letters
• Some SQL dialects have PROPER for title case
• Ensures matching is not case-sensitive
• Makes your data look clean and professional

LEN & DATALENGTH

• LEN returns the number of characters in a string
• DATALENGTH returns the number of bytes used
• Useful for validating input length or filtering short responses
• Helpful for data quality checks and constraints
• Note: LEN does not count trailing spaces

CONCAT & Concatenation

• Combine multiple strings into one
• Use the + operator or the CONCAT function
• CONCAT handles NULL values by treating them as empty strings
• Create full names from separate first and last name fields
• Build dynamic messages or labels from data

Real Regex Power

• Some databases offer full regular expression support
• Functions like REGEXP_SUBSTR or REGEXP_REPLACE
• Provide extremely powerful and flexible pattern matching
• Match complex patterns like email addresses or phone numbers
• Currently available in PostgreSQL, Oracle, and BigQuery
• The ultimate tool for sophisticated text parsing

Keyword Counting Strategy

• Use a combination of functions to count keyword frequency
• Create a temporary table or CTE of your target keywords
• Join or cross apply to your main text data
• Use LOWER on both sides for case-insensitive matching
• Count occurrences using string functions
• Aggregate and summarize to see trending topics

Your Action Plan

• Start with basic LIKE searches for quick wins
• Use TRIM and LOWER to standardize your text first
• Practice extracting parts of strings with SUBSTRING
• Build a dictionary of common keywords for your domain
• Automate cleaning steps into your data pipeline
• Explore regex functions if your database supports them
• Transform messy text into your most valuable asset