importing-data

To reliably import CSV data into SQLite, use a structured 5-phase pipeline covering data profiling, schema design, standardization, execution, and quality assessment. This enforces explicit NULL handling and type inference to eliminate ad-hoc imports and ensure reproducible ingestion.

You profile a CSV file by detecting its encoding, delimiter, and headers, then capturing representative samples. This generates a detailed data profile that informs schema design and type inference for the subsequent database import.

Schema design for CSV imports works by inferring column data types, mapping NULL representations, and proposing a raw table CREATE TABLE statement with justifications. This ensures clean raw data standardization before the actual import execution.

Yes, you can standardize dates and clean numeric values during a CSV import by defining explicit standardization rules. These rules handle date normalization, numeric cleaning, whitespace trimming, and NULL mappings to ensure clean raw data enters the database.

You assess data quality after a CSV import by running validation checks on row counts and using sub-agents to identify NULL patterns, duplicates, outliers, and potential foreign-key relationships, compiling these findings into a comprehensive quality report.

CSV imports often fail due to mismatched NULL representations and encoding issues because ad-hoc processes lack explicit profiling. A structured pipeline detects encoding and maps NULL values during schema design to prevent these import failures.