Prove the delimiter, stop header collisions, validate every record, and safely escape HTML output. A detailed guide with implementation steps, negative tests, verification criteria, and trust boundaries.
Define the decision and success criteria
Before selecting a tool, write down the decision, its owner, and the impact of a wrong result. The practical objective here is: Inspect an unknown CSV while preserving the raw copy, map it to a target column contract, split to NDJSON, and locate bad rows. “Output was produced” is not a success criterion; define measurable thresholds for accuracy, completeness, reversibility, time, and human approval. Keeping assumptions visible from the start reduces post-hoc justification and automation bias.
Prove the delimiter, stop header collisions, validate every record, and safely escape HTML output.
- Inspect an unknown CSV while preserving the raw copy, map it to a target column contract, split to NDJSON, and locate bad rows.
- Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Record input, output, and decision owner.
Prepare the input contract and rights
Begin only with synthetic data, your own data, or material whose reuse rights are explicit. Preserve the raw input read-only and document field names, types, units, language, dates, encoding, missing values, and duplicate rules in a separate dictionary. Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner. Minimise sensitive data and never use values representing real people in shareable examples.
Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Inspect an unknown CSV while preserving the raw copy, map it to a target column contract, split to NDJSON, and locate bad rows.
- Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Record input, output, and decision owner.
Run small, reversible workflow steps
Split the workflow into observable gates: input validation, transformation, structural review, before/after comparison, and export. For csv-ayirac-tespit-araci, csv-sutun-yeniden-adlandirici, ndjson-toplu-dogrulayici, csv-html-tablo-donusturucu, document expected input, output, failure message, and stop condition. Start with one record and do not scale until a small batch reconciles successfully.
Prove the delimiter, stop header collisions, validate every record, and safely escape HTML output.
- Inspect an unknown CSV while preserving the raw copy, map it to a target column contract, split to NDJSON, and locate bad rows.
- Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Record input, output, and decision owner.
Deliberately test failures and edge cases
Alongside the happy path, test empty input, malformed encoding, unexpected Unicode, oversized values, missing required fields, duplicate keys, negative numbers, division by zero, wrong time zones, and deliberate contradictions. Errors should name the invalid field, explain why it failed, and state the next corrective action. Prefer visible assumptions to silent correction.
Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Inspect an unknown CSV while preserving the raw copy, map it to a target column contract, split to NDJSON, and locate bad rows.
- Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Record input, output, and decision owner.
Reconcile output with the source
Reconcile source and output row counts, fields, totals, missing values, unique keys, and checksums. Run a round-trip test when conversion is reversible; otherwise publish a data-loss list. Manually inspect a random sample and trace consequential claims to primary evidence. A visually tidy table is not proof of structural or factual correctness.
Prove the delimiter, stop header collisions, validate every record, and safely escape HTML output.
- Inspect an unknown CSV while preserving the raw copy, map it to a target column contract, split to NDJSON, and locate bad rows.
- Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Record input, output, and decision owner.
Record evidence, limits, and next review
Record date, tool and data version, acceptance threshold, known limits, failure cases, output summary, human approval, and next review. Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner. For legal, security, health, or financial impact, make qualified review against current primary sources a mandatory workflow gate; never present a tool result as conclusive verification.
Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Inspect an unknown CSV while preserving the raw copy, map it to a target column contract, split to NDJSON, and locate bad rows.
- Delimiter scoring does not validate encoding, locale decimals, or dates; confirm the data dictionary with the source owner.
- Record input, output, and decision owner.
Content is checked against visible ByteQuant product behavior and the listed primary sources where available. It is general information, not legal or security advice.