DataEngUtils for Data Engineers

Tools for data engineers

• Parquet InspectorInspect Parquet metadata, row groups, column statistics and encodings without uploading the file to
• Parquet QueryQuery a Parquet file in your browser — pick columns, filter rows and limit the result, then export t
• Parquet Data ProfilerProfile a Parquet file in your browser and get per-column statistics — null percentage, distinct cou
• Parquet Schema EvolutionCompare two Parquet schemas and get a compatibility verdict before you ship a pipeline change. DataE
• Parquet Row Group PrunerParquet Row Group Pruner is built for data engineers who need to inspect, debug, and explain data ar
• Parquet File RepairParquet File Repair is built for data engineers who need to inspect, debug, and explain data artifac
• 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
• Merge CSVs → ParquetCombine multiple CSV extracts into one Parquet dataset in your browser — ideal when logs, exports or
• dbt YAML ValidatorValidate dbt schema.yml and model YAML before it breaks parse-time in CI. DataEngUtils checks struct
• 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

Workflow: Inspect and query Parquet

Open a Parquet file in the browser to read its schema, row groups and column statistics, then query and export a slice — all without a warehouse, a notebook, or an upload.

1. 1. Drop the Parquet file

   Open Parquet Inspector and add the file. The Apache Arrow engine reads the footer and schema locally in a Web Worker.

2. 2. Query and profile

   Use Parquet Query to select columns and filter rows, or Parquet Data Profiler for nulls, ranges and distributions per column.

3. 3. Export the result

   Send the slice to CSV, Parquet or JSON, and copy the stats into a ticket.

Workflow: Convert and merge files

Turn CSV exports into columnar Parquet, export Parquet back to CSV, and merge several extracts into one dataset — processed locally in your browser.

1. 1. Add your CSV files

   Open CSV → Parquet and drop files in. Delimiters and preamble rows are auto-detected and adjustable.

2. 2. Choose compression

   Pick Snappy, Zstd or Gzip and preview the schema before writing.

3. 3. Download Parquet

   Save the .parquet output, then re-open it in Parquet Inspector to verify before it lands in the warehouse.

Workflow: Validate dbt and warehouse SQL

Catch dbt YAML mistakes and format or explain warehouse SQL without a full dbt run or a web beautifier.

1. 1. Validate dbt YAML

   Paste a schema.yml excerpt into the dbt YAML Validator to catch structure and indentation errors before CI.

2. 2. Format the SQL

   Drop a CTE-heavy query into the SQL Formatter and pick the dialect for consistent casing and indentation.

3. 3. Explain the query

   Use SQL Query Explainer to list the tables, joins, CTEs and columns before you optimize.

Workflow: Debug schemas, secrets and schedules

Diff two pipeline schema versions for compatibility, split connection strings with the password masked, and read cron schedules in plain language.

1. 1. Check schema evolution

   Load the before and after Parquet files into Parquet Schema Evolution to see added, removed and retyped columns with a compatibility verdict.

2. 2. Split a connection string

   Paste a postgres:// or kafka:// URI into the Connection String Parser to review host, database and parameters with the password masked.

3. 3. Read a cron schedule

   Drop the expression into the Cron Expression Parser for a description, the next ten runs and a heatmap.

Frequently asked questions