DataEngUtils for Analytics Engineers
Analytics Engineers sit between data engineering and analysis, applying software engineering best practices to data transformations using tools like dbt, SQL, and data modeling frameworks. Your daily work involves validating dbt YAML source definitions, formatting SQL models, converting between schema formats, and testing data quality before promoting code through staging to production. Many of these tasks currently require switching between multiple web-based tools, each asking you to upload sensitive schema definitions. DataEngUtils brings these utilities together in one native macOS app that works offline. This page covers the tools that map to the core Analytics Engineering workflow, from dbt validation to schema testing.
Tools for analytics engineers
- dbt YAML ValidatorValidate dbt schema.yml and model YAML before it breaks parse-time in CI. DataEngUtils checks struct
- YAML to JSONYAML to JSON is built for data engineers who need to convert between file formats and schema represe
- JSON to YAMLJSON to YAML is built for data engineers who need to convert between file formats and schema represe
- YAML FormatterYAML Formatter is built for data engineers who need to validate and format pipeline configs, warehou
- 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
- JSON to CREATE TABLEGenerate CREATE TABLE statements from JSON payloads for Postgres, BigQuery, and Snowflake without pa
- CSV ValidatorCSV Validator is built for data engineers who need to validate and format pipeline configs, warehous
- JSON ValidatorJSON Validator is built for data engineers who need to validate and format pipeline configs, warehou
- JSON Schema ValidatorJSON Schema Validator is built for data engineers who need to validate and format pipeline configs,
Workflow: Validate dbt YAML source definitions
Catch syntax errors, missing fields, and structural issues in your dbt YAML files before running dbt compile or dbt run in production.
1. Paste dbt YAML
Copy a dbt source YAML or model config and paste it into the dbt YAML Validator. The tool checks for dbt-specific key requirements.
2. Review flagged issues
The validator highlights missing fields (like freshness configs), incorrect types, and indentation problems. Fix them inline.
3. Format and commit
Use YAML Formatter to normalize formatting, then copy the clean YAML back to your dbt project and commit.
Workflow: Convert and debug dbt schemas
Translate between YAML and JSON representations of your dbt schema files for use with external tools and schema registries.
1. Convert dbt YAML to JSON
Use YAML to JSON to convert your dbt schema files into JSON for programmatic validation or integration with external catalog tools.
2. Convert JSON back to YAML
If you receive schema definitions in JSON format, use JSON to YAML to convert them back to dbt-compatible YAML.
3. Review the converted output
Both converters preserve comments and structure. Review the output before copying it into your project.
Workflow: Format and explain warehouse SQL
Standardize SQL formatting across your dbt models and understand complex query logic before code review.
1. Format a dbt model SQL
Paste a CTE-heavy dbt model into SQL Formatter to get consistent indentation, keyword casing, and line breaks.
2. Explain query logic
Use SQL Query Explainer to break down the model into its component CTEs, JOINs, and aggregations in plain language.
3. Generate DDL from sample JSON
When modeling new source data, use JSON to CREATE TABLE to generate DDL directly from a sample JSON record.
Workflow: Test data quality and contracts
Validate CSV files, JSON data, and JSON Schema contracts before loading new sources into your warehouse.
1. Validate a data contract
Use JSON Schema Validator to check that incoming data conforms to your defined contract before accepting it into the warehouse.
2. Check CSV quality
Use CSV Validator to scan raw CSV exports for formatting issues, inconsistent row lengths, and encoding problems.
3. Validate JSON payloads
Use JSON Validator to ensure JSON data from API sources is well-formed before staging it for transformation.