Operations — how data is queried, observed, governed

Data Quality

3 open standards for Data Quality in a modern data architecture, each with an opinionated judgement: Adopt, Situational, Assess, or Caution.

Nothing here is a safe default yet.

Situational 3 standards

The right answer in some contexts but not others. Pick deliberately based on the constraint.

  • Great Expectations

    Python-first DQ; powerful but heavy.

    GX Labs (vendor-driven open source)

  • dbt tests — dbt schema and data tests

    Right when your transformations already live in dbt; otherwise reach for a standalone DQ tool.

    dbt Labs (vendor-driven open source)

  • SodaCL — Soda Checks Language

    YAML-first DQ; lighter than Great Expectations, smaller ecosystem.

    Soda (vendor-driven open source)

More in Operations

Operations covers how data is queried, observed, governed.

See Data Quality in context

These standards are one panel of the interactive Data Landscape, which maps every open standard a modern data architecture is built on. The underlying data is a single JSON file; disagree with a judgement? Open an issue.