Build yourData ingestion layer

Give it a sample of data. Get back a project that runs, with the tests, the schema and the documentation already written.

Ingest sources you have no connector for

  • From a sample to a complete pipeline

    Drop a sample file, get a running pipeline back minutes later.

  • Schema inference

    Types, ranges and rules derived from what the source actually sends.

  • Error handling by default

    Retries, malformed record capture, partial failure or stop on failure options that keep the batch alive.

  • Orchestration ready

    Airflow, Prefect and Dagster config files generated alongside the code.

  • data_ingestion/
  • data/
  • lib/
  • tests/
  • quality/
  • config/
  • docs/
  • pipeline.py
  • schema.json
  • requirements.txt

Enforce data quality at ingestion

  • Field transformations

    The model automatically adds the right transformations to your fields to enforce the quality of the data.

  • Tune the transformation to get a preferred output format

    Each transformation can be tuned to reach the correct output format.

  • Metadata for every field

    A boolean beside every enriched field, so failure is readable row by row.

  • Original values preserved

    What arrived sits next to what you use, so a conversion can be contested.

  • Output contract

    A JSON Schema with strict validation and a description on every field.

Feed your systems with data you can trust

  • Typed inputs

    Every field declared and validated before your system ever sees it.

  • Traceable records

    An answer can always be walked back to the value that produced it.

  • Schema drift caught early

    A source that changes shape fails at ingestion, not in a dashboard.

  • Reproducible runs

    Same input, same output, same tests.

{ "siren": { "type": "string", "validated": true }, "updatedAt": { "type": "string", "format": "date-time", "normalized": true }, "isActive": { "type": "boolean", "validated": true }, "validation": { "status": "passed", "errors": [] }}