1) Why Data Types Matter in SQL

For SQL to analyze and sort data correctly, each column must have the correct data type.
When data is imported from external sources, values that look like numbers or dates are often stored as strings (text). This can lead to incorrect sorting, filtering, and calculations.

Ensuring correct data types is a core step in data cleaning.

2) What Is Typecasting?

Typecasting is the process of converting data from one data type to another.
In SQL, this is commonly done using the CAST function.

Typecasting is necessary when:

  • Numeric values are stored as text
  • Dates and times are stored as strings
  • Calculations or sorting behave incorrectly

3) The CAST Function

CAST converts a value from one data type into another so SQL can interpret it correctly.

Conceptual Syntax

CAST(value AS data_type)

CAST does not permanently change the table unless used in an update; it can also be applied temporarily within a query.

4) Common Problem: Numbers Stored as Strings

A frequent issue occurs when numeric values are stored as text.

Example Symptom

  • Sorting prices in descending order
  • A smaller number (e.g., 89.85) appears before a larger number (e.g., 799.99)

Why This Happens

  • SQL sorts strings lexicographically (character by character)
  • The database compares the first character instead of the numeric value
  • "89.85" is treated as text starting with "8"
  • "799.99" is treated as text starting with "7"

As a result, sorting is incorrect.

5) Using CAST to Fix Numeric Sorting

To correct this behavior:

  • Convert the string value into a numeric data type
  • Sort using the converted value

Conceptual Approach

  • Replace the string column with CAST(column AS FLOAT)
  • Use the casted value in ORDER BY

This allows SQL to recognize the values as numbers and sort them correctly.

6) Understanding FLOAT and FLOAT64

A FLOAT is a numeric data type that supports decimal values.
Some SQL systems (such as BigQuery) use FLOAT64, which refers to a 64-bit floating-point number.

Key idea:

  • FLOAT / FLOAT64 = numeric values with decimals
  • The exact name may vary slightly by SQL platform

7) Why CAST Is Important for Data Cleaning

Using CAST:

  • Ensures correct sorting and comparisons
  • Enables accurate calculations
  • Prevents subtle logic errors
  • Makes data usable for analysis

Without proper typecasting, even clean-looking data can produce incorrect results.

8) CAST Beyond Numbers

CAST is not limited to numeric conversions.

It can also convert:

  • Strings → dates
  • Strings → timestamps
  • Integers → strings
  • Dates → strings

This is especially useful when:

  • Combining data from multiple sources
  • Standardizing formats across datasets
  • Preparing data for time-based analysis

9) When to Use CAST

CAST is commonly used when:

  • Imported data has incorrect data types
  • Sorting or calculations behave unexpectedly
  • Data comes from external systems with inconsistent schemas

Typecasting early in the workflow prevents problems later in analysis.

10) Key Takeaways

  • SQL relies on correct data types to function properly.
  • Imported data is often misclassified as strings.
  • CAST converts values from one data type to another.
  • Typecasting fixes incorrect sorting and calculations.
  • CAST is essential for cleaning numeric, date, and time data.
  • Mastering CAST improves accuracy, reliability, and efficiency in SQL analysis.