DataEngUtils for Data Analysts
Data Analysts spend their days exploring datasets, writing SQL and reconciling numbers. You might get a Parquet export from a data engineer, a CSV to sanity-check before it reaches a dashboard, or two query outputs that should match but don't. The web tools for this ask you to upload files to a server, which compliance teams dislike and large files make impractical. DataEngUtils gives you the same capabilities in your browser with no uploads and no account. This page highlights the tools that map to an analyst's everyday workflows.
Tools for data analysts
- Parquet InspectorInspect Parquet metadata, row groups, column statistics and encodings without uploading the file to
- Parquet Data ProfilerProfile a Parquet file in your browser and get per-column statistics — null percentage, distinct cou
- JSONL InspectorInspect JSONL (NDJSON) files line by line without uploading them to a converter. DataEngUtils valida
- SQL FormatterFormat messy warehouse SQL in your browser with a formatter tuned for analytics queries — CTEs, wind
- SQL Query ExplainerSQL Query Explainer is built for data engineers who need to inspect, debug, and explain data artifac
- Text Diff CheckerText Diff Checker is built for data engineers who need to inspect, debug, and explain data artifacts
- Unix Timestamp / TimezoneUnix Timestamp / Timezone is built for data engineers who need to convert between file formats and s
- CSV → ParquetTurn CSV exports into columnar Parquet without shipping them to a random web converter. DataEngUtils
- Parquet → CSVConvert Parquet to CSV in your browser without uploading the file to a cloud converter. DataEngUtils
- Data Type InspectorData Type Inspector is built for data engineers who need to inspect, debug, and explain data artifac
Workflow: Explore a dataset without code
Open a Parquet or JSONL file in the browser to inspect schema, column statistics and a row preview — no Python, no notebook, no upload.
1. Open the file
Drop a Parquet file into Parquet Inspector. Schema, row count and metadata load instantly from the footer.
2. Check the quality
Use Parquet Data Profiler for null percentages, distinct counts and distributions per column.
3. Validate JSONL
For event exports, JSONL Inspector flags the exact lines that fail to parse.
Workflow: Format and understand SQL
Clean up messy SQL and understand complex query logic without pasting warehouse queries into a web formatter.
1. Paste the query
Copy from your warehouse or BI tool into the SQL Formatter and pick the dialect.
2. Explain the structure
Use SQL Query Explainer to break down joins, CTEs and aggregations in plain language.
3. Share it
Copy the formatted or explained output into documentation or a ticket.
Workflow: Reconcile two outputs
Compare two CSV or JSON exports and normalise timestamps to find the discrepancy between pipeline stages.
1. Diff the two versions
Paste both outputs into Text Diff Checker to see added, removed and changed lines side by side.
2. Normalise timestamps
Use Unix Timestamp / Timezone to convert epoch values before comparing time-series data.
3. Identify odd values
Use Data Type Inspector to check whether a column really holds UUIDs, IPs, base64 or plain text.