Understanding Data Type Mapping

Data type mapping is crucial when transferring data between different systems, as each platform has its own type system with specific behaviors, sizes, and constraints.

Common Data Types Across Systems

String Types

• SQL: VARCHAR, CHAR, TEXT - Fixed or variable-length character strings
• Python: str - Unicode strings, immutable
• JSON: string - UTF-8 encoded text
• Java: String - Immutable object reference

Integer Types

• SQL: INT (4 bytes), BIGINT (8 bytes), SMALLINT (2 bytes), TINYINT (1 byte)
• Python: int - Arbitrary precision integer
• JSON: number - No distinction between int and float
• Pandas: int64, int32, int16, int8 - Sized integers

Floating Point Types

• SQL: FLOAT (4 bytes), DOUBLE (8 bytes), DECIMAL (exact precision)
• Python: float (64-bit), Decimal (arbitrary precision)
• JSON: number - IEEE 754 double precision

Boolean Types

• SQL: BOOLEAN or BIT
• Python: bool (True/False)
• JSON: boolean (true/false)

Date/Time Types

• SQL: DATE, DATETIME, TIMESTAMP, TIME
• Python: date, datetime, time (from datetime module)
• JSON: string (ISO 8601 format)
• Pandas: datetime64 - Numpy datetime type

Important Considerations

Precision Loss

Null Handling

Different systems handle null/missing values differently:

• SQL: NULL - Special marker for missing data
• Python: None - Singleton object representing absence
• JSON: null - Explicit null value
• Pandas: NaN, NaT, None - Multiple representations

String Encoding

Always be aware of character encoding when working with strings:

• Python strings are Unicode by default
• SQL VARCHAR may have different encodings (UTF-8, Latin-1, etc.)
• JSON requires UTF-8 encoding