DataEngUtils for Analytics Engineers
Analytics Engineers apply software practices to data transformation — dbt, SQL, data contracts and modelling. The daily loop involves validating dbt YAML, formatting models, converting config between YAML and JSON, and checking that incoming data matches its contract. Each of those usually means a different web tool that uploads your schema definitions. DataEngUtils brings them into one browser app that runs client-side, so private repo contents and column descriptions never touch a third-party server.
Tools for analytics engineers
- dbt YAML ValidatorValidate dbt schema.yml and model YAML before it breaks parse-time in CI. DataEngUtils checks struct
- YAML ↔ JSONYAML ↔ JSON is built for data engineers who need to convert between file formats and schema represen
- 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
- JSON → CREATE TABLEGenerate CREATE TABLE statements from JSON payloads without pasting sample records into a web form.
- JSON Schema ValidatorJSON Schema Validator is built for data engineers who need to validate and format pipeline configs,
- JSON Format / ValidateJSON Format / Validate is built for data engineers who need to validate and format pipeline configs,
- CSV → CREATE TABLECSV → CREATE TABLE is built for data engineers who need to convert between file formats and schema r
- Parquet InspectorInspect Parquet metadata, row groups, column statistics and encodings without uploading the file to
- JSONL InspectorInspect JSONL (NDJSON) files line by line without uploading them to a converter. DataEngUtils valida
Workflow: Validate dbt YAML sources
Catch syntax and structural mistakes in schema.yml before dbt compile or a long CI run.
1. Paste the YAML
Copy a source or model config into the dbt YAML Validator.
2. Review the issues
Fix missing keys, indentation and type problems highlighted inline.
3. Normalise formatting
Use YAML ↔ JSON to convert or normalise the config, then copy it back and commit.
Workflow: Format and explain dbt models
Standardise SQL across models and understand complex logic before code review.
1. Format the model
Paste a CTE-heavy model into SQL Formatter for consistent casing, indentation and line breaks.
2. Explain the logic
Use SQL Query Explainer to break the model into its CTEs, joins and aggregations.
3. Generate DDL
Use JSON → CREATE TABLE to turn a sample record into DDL when modelling a new source.
Workflow: Test data contracts
Validate JSON Schema contracts and inspect incoming data files before loading a new source.
1. Validate the contract
Use JSON Schema Validator to check that incoming data conforms before it enters the warehouse.
2. Inspect the source file
Open a Parquet or JSONL sample in Parquet Inspector or JSONL Inspector to confirm the shape.
3. Document the schema
Use CSV → CREATE TABLE to infer a DDL draft from a raw CSV export.