DataEngUtils for Data Analysts
Data Analysts spend their days exploring datasets, writing SQL, and preparing data for dashboards and reports. Whether you are inspecting a Parquet export from Snowflake, validating a CSV before loading it into Tableau, or comparing two query outputs to track down a discrepancy, you need tools that work fast and keep your data private. Online converters and validators ask you to upload files to a server, which is often blocked by compliance policies or simply impractical for large datasets. DataEngUtils gives you the same capabilities directly on your Mac with no uploads, no network requests, and no waiting. This page highlights the tools most useful for Data Analysts, organized by the workflows you tackle every day.
Tools for data analysts
- Parquet InspectorInspect Parquet metadata, row groups, column statistics, and encodings without uploading files to a
- CSV to JSONCSV to JSON is built for data engineers who need to convert between file formats and schema represen
- JSON to CSVJSON to CSV is built for data engineers who need to convert between file formats and schema represen
- CSV ValidatorCSV Validator is built for data engineers who need to validate and format pipeline configs, warehous
- SQL FormatterFormat messy warehouse SQL on your Mac with a formatter tuned for analytics queries — CTEs, window f
- SQL Query ExplainerSQL Query Explainer is built for data engineers who need to inspect, debug, and explain data artifac
- CSV to ParquetTurn CSV exports into columnar Parquet without shipping them to a random web converter. DataEngUtils
- Parquet to CSVConvert Parquet files to CSV on your Mac without uploading them to a cloud converter. DataEngUtils s
- Unix Timestamp ConverterUnix Timestamp Converter is built for data engineers who need to convert between file formats and sc
- Text DiffText Diff is built for data engineers who need to inspect, debug, and explain data artifacts when so
Workflow: Explore Parquet files without code
Open Parquet files directly on your Mac to inspect schema, column statistics, and sample rows — no Python, no notebook, no upload required.
1. Open the Parquet Inspector
Drag a Parquet file from Finder into the Parquet Inspector. The tool immediately loads the schema and row group metadata.
2. Browse column statistics
Click any column to view min, max, null count, and distinct values — useful for quick data quality checks before analysis.
3. Export sample rows to JSON
Select a subset of rows and export them as JSON for sharing with stakeholders or importing into another tool.
Workflow: Validate CSV data before loading
Check CSV files for formatting issues, missing values, and schema inconsistencies before importing them into a database or analytics platform.
1. Upload or paste your CSV
Open the CSV Validator and drop your file or paste a sample. The tool automatically detects delimiters and headers.
2. Review validation issues
The validator flags inconsistent row lengths, encoding problems, and type mismatches across all rows.
3. Convert clean CSV to Parquet
Once validated, use CSV to Parquet to convert the file to columnar format for faster querying in tools like Tableau or DuckDB.
Workflow: Format and understand SQL queries
Clean up messy SQL and understand complex query logic without pasting your warehouse queries into a web-based formatter.
1. Paste SQL into the formatter
Copy a SQL query from your warehouse or BI tool and paste it into the SQL Formatter for instant, consistent formatting.
2. Read the explained structure
Use SQL Query Explainer to break down JOINs, CTEs, window functions, and aggregation steps in plain language.
3. Copy and share
Copy the formatted or explained output back to your notebook, documentation, or team chat.
Workflow: Compare dataset outputs
Quickly compare two CSV or JSON outputs side by side to find discrepancies between query results or pipeline stages.
1. Convert data to comparable format
Use JSON to CSV to normalize both datasets into a consistent row-based format for comparison.
2. Diff the two outputs
Paste both versions into Text Diff to see added, removed, and changed lines highlighted side by side.
3. Normalize timestamps
Use Unix Timestamp Converter to convert epoch values to human-readable dates before comparing time-series data.