DataEngUtils for Data Engineers
Data Engineers build and maintain the infrastructure that moves, transforms, and stores data across an organization. Every day involves working with Parquet files, writing SQL for warehouse transformations, validating dbt YAML configurations, and debugging pipeline logs — often across multiple cloud platforms like Snowflake, BigQuery, and Databricks. Most existing tools are web-based, requiring you to upload production data to third-party servers, which creates compliance risk and slows down debugging when you need to work offline. DataEngUtils runs entirely on your Mac, giving you the same convenience without uploading anything. This page covers the DataEngUtils tools most relevant to Data Engineers, organized by workflow so you can get started fast.
Tools for data engineers
- SQL FormatterFormat messy warehouse SQL on your Mac with a formatter tuned for analytics queries — CTEs, window f
- dbt YAML ValidatorValidate dbt schema.yml and model YAML before it breaks parse-time in CI. DataEngUtils checks struct
- JSON to CREATE TABLEGenerate CREATE TABLE statements from JSON payloads for Postgres, BigQuery, and Snowflake without pa
- SQL Query ExplainerSQL Query Explainer is built for data engineers who need to inspect, debug, and explain data artifac
- Connection String ParserBreak apart database connection strings for Postgres, MySQL, Redis, and other engines without pastin
- 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
- Merge CSVs to ParquetCombine multiple CSV extracts into one Parquet dataset on macOS — ideal when logs, exports, or shard
- Parquet InspectorInspect Parquet metadata, row groups, column statistics, and encodings without uploading files to a
- Cron Expression ParserParse and explain cron expressions in plain language when schedules from Airflow, Kubernetes, or leg
- Cron to EnglishCron to English is built for data engineers who need to convert between file formats and schema repr
- Log ParserLog Parser is built for data engineers who need to inspect, debug, and explain data artifacts when s
Workflow: Validate dbt projects locally
Catch YAML syntax errors and schema misconfigurations in your dbt project before pushing to production. DataEngUtils validates your source definitions and model configs on your machine without uploading them.
1. Open the dbt YAML Validator
Launch the tool from the DataEngUtils sidebar and paste your dbt YAML snippet or source definition.
2. Review validation results
The validator checks for common dbt YAML issues — missing required keys, incorrect indentation, and invalid field types.
3. Fix and re-validate
Update the YAML directly in the tool and re-validate until everything passes, then commit with confidence.
Workflow: Convert CSV to Parquet offline
Transform CSV files into columnar Parquet format for faster queries and smaller storage footprints. All processing happens locally on your Mac — no data ever leaves your machine.
1. Select your CSV file
Open the CSV to Parquet converter and drag in one or more CSV files from Finder.
2. Configure output options
Set compression codec (Snappy, Zstd, or Gzip) and choose whether to merge multiple CSVs into a single Parquet file.
3. Convert and inspect
Run the conversion, then open the result in Parquet Inspector to verify schema, row counts, and column statistics before loading into your warehouse.
Workflow: Debug warehouse DDL and SQL
Quickly translate JSON records into CREATE TABLE statements and format or explain complex SQL queries for your data warehouse.
1. Generate DDL from sample data
Paste a JSON record into JSON to CREATE TABLE and get a ready-to-use DDL statement for Snowflake, BigQuery, or PostgreSQL.
2. Format messy SQL
Copy your unformatted warehouse SQL into the SQL Formatter to get consistent indentation and keyword casing.
3. Explain query structure
Use SQL Query Explainer to understand complex JOINs, CTEs, and aggregation patterns before optimizing.
Workflow: Inspect pipeline logs and schedules
Debug cron schedules and parse application logs directly in DataEngUtils without switching between multiple online tools or text editors.
1. Parse a log file
Paste log output into Log Parser. The tool automatically detects common log formats and extracts timestamps, severity levels, and message patterns.
2. Decode a cron expression
Drop your cron expression into the Cron Expression Parser to see the schedule in human-readable form.
3. Translate cron to plain English
Use Cron to English to get a natural-language explanation of when each pipeline job runs, making handoff easier for non-technical stakeholders.