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SQL data types express how values are represented and what operations are appropriate. A column that looks numeric may contain identifiers rather than quantities, and a timestamp may need a time-zone convention. Decide meaning before conversion so a technically valid query does not erase leading zeroes or compare incompatible units.
Describe the business meaning of each field
Distinguish identifiers, measured amounts, dates, statuses and free text. A postal code or account reference may need text representation even when it contains digits. Record units and precision for quantitative values. Do not choose a type merely because the first sample happens to fit; inspect the intended domain and documented constraints.
Review engine-specific representation and conversion
Database types and implicit conversions vary. Check the actual engine’s documentation for precision, range and date-time behavior. Use explicit conversions where appropriate, while preserving an approved source and exception process. A failed conversion can reveal a malformed record or incorrect assumption that should not simply be replaced with zero.
Test boundaries and downstream comparisons
Use controlled fixtures with leading zeroes, large values, missing entries and relevant time boundaries. Check whether joins compare compatible types and whether sorting is numeric or lexical. Document any loss of precision or changed interpretation. A conversion that makes the query run is not automatically a correct transformation of the client’s data.
A practical checklist
- Define meaning, units and identifier rules.
- Use the actual database engine’s type documentation.
- Review implicit and explicit conversions.
- Keep malformed values visible for resolution.
- Test sorting, joins and precision boundaries.
Worked example
Illustrative example: a synthetic product code begins with zero. Treating it as an integer changes the displayed identifier and can break a lookup against another system’s text key. The team keeps the code as a documented identifier and uses numeric types only for the quantities that genuinely need arithmetic.
Common questions
Is every digit-only field a number? No. Are database types identical across engines? No. Does a successful cast prove the business meaning survived? No.
What to do next
Maintain a short data dictionary with important fields and transformation rules. Correct types support reliable queries, but they cannot replace clear meaning and source validation.