Importing CSV Into a Database: A Practical Checklist
A database import that "completes without errors" isn't the same as one that imported your data correctly. Type coercion, truncation, and silent nulls happen quietly, this checklist catches them before they reach production.
Why "No Errors" Doesn't Mean "Correct"
Most database import tools are permissive by default: a text value in a numeric column often becomes NULL rather than an error, a value too long for a column gets silently truncated, and a malformed date can become an unexpected default. Every one of these completes "successfully" while quietly corrupting your data.
Pre-Import Checklist
- ☐Column types match the target schema. Verify each CSV column's actual content (not just its header name) matches the destination column's type before loading.
- ☐No values exceed column length limits. A
VARCHAR(50)destination silently truncating a 60-character value is one of the most common causes of "why does this field look cut off in production." - ☐Dates are in an unambiguous format (ISO 8601,
YYYY-MM-DD) before import, so the database doesn't have to guess between MM/DD and DD/MM. - ☐The intended primary/unique key column has no duplicates, check this before import, not after the database rejects (or worse, silently accepts) a conflicting row.
- ☐Foreign key values actually exist in the referenced table, an orphaned foreign key will either fail the import or, if constraints aren't enforced, create a silently broken relationship.
- ☐Encoding is UTF-8 and matches what the database expects, mismatched encoding corrupts any non-ASCII text on the way in.
Staging Before Production
Import into a staging table (or a throwaway database) first, then run validation queries, row counts, null checks on required fields, distinct-value counts on your key column, before promoting to production. This turns "the import broke something" into a caught bug instead of a live incident.
Validating After Import
- Row count in the database matches the row count in the source CSV (minus any rows you deliberately excluded)
- Spot-check a sample of rows against the original file, especially any row with unusual values (very long text, special characters, edge-case dates)
- Aggregate a numeric column (e.g.
SUM(amount)) and compare it against the same calculation on the raw CSV, a mismatch usually means silent type coercion happened somewhere
Doing This in How To CSV
Clean and validate your CSV before it ever reaches your database: the Data Health Check and Statistics tools surface type mismatches and null patterns, the Schema Detector suggests appropriate column types, and SQL on CSV lets you test queries against the raw file before committing to a schema.
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