Profile CSV samples in the browser before they hit production tables
Bad loads are cheaper to stop at the laptop than after a weekend warehouse job. A quick profile answers whether dates parsed, whether categorical cardinality exploded and whether null rates match what the supplier promised. This profiler reads CSV entirely on-device so partner extracts with PII never traverse a third-party upload form. Use it in intake meetings, after Excel exports and when you inherit a folder of ad hoc extracts without documentation.
Profiling workflow
- Export or save a CSV sample with headers in the first row.
- Load the file and scan inferred types versus your contract.
- Investigate high null or high cardinality columns before you map types in SQL.
- Convert or clean, then validate again after transforms.
What to look for in each column
Numeric columns with unexpected min/max often hide unit mistakes (cents versus dollars). String columns with low distinct counts may be enums missing from the data dictionary. Datetime columns that inferred as strings mean delimiter or timezone issues upstream. Compare two supplier drops with separate profiles before you merge — silent schema drift is easier to explain with side-by-side stats than with a failed COPY command at 3 a.m.
Adjacent utilities in the load path
After profiling, shape files for import with the CSV → SQL Import Helper and convert wide extracts to JSON when APIs expect objects via the CSV ⇄ JSON Converter. Prototype cleansing logic in the SQLite Playground on a sampled subset before you promote transforms to dbt or stored procedures. When spreadsheets need automated fixes at scale, review the AI Data Cleanup tool separately — it may send rows to a server when you apply AI-assisted rules.
Documentation and handoff
Screenshot or copy notable metrics into your ticket so reviewers see evidence without rerunning the file. Note encoding (UTF-8 versus Latin-1) and delimiter choices in the description — profilers assume sensible defaults that legacy exports violate. When you approve a file for production, attach the profile summary beside the JSON Schema or contract test so the next rotation knows what “normal” looked like on day one.
Sampling large files
Multi-hundred-megabyte CSVs can exhaust browser memory. Profile a filtered extract or the first N thousand rows when you only need a schema sketch, then rerun on a server job for full counts. Document the sample strategy in your ticket so stakeholders know which metrics are approximate versus exhaustive.
Turn profiling into an intake gate
A profile is most useful when it drives a decision, not just a screenshot. Before you accept a feed, write down the thresholds that matter — a null rate under a few percent on required columns, a distinct count that matches the known set of categories, a numeric range that fits the business reality — then reject or escalate the file when the profile breaks them. Capturing those expectations as explicit acceptance criteria turns a manual eyeball into a repeatable contract, and it gives the supplier concrete numbers to fix rather than a vague complaint that the data looks off. Re-profile after every redelivery so a quiet regression cannot slip through on the third drop.