provero: A vendor-neutral, declarative data quality engine

[andreahlert]

Submitting Author: (@andreahlert)
All current maintainers: (@andreahlert)
Package Name: provero
One-Line Description of Package: A vendor-neutral, declarative data quality engine for validating datasets using YAML-based check definitions.
Repository Link: https://github.com/provero-org/provero
Version submitted: 0.1.1
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

Provero is a vendor-neutral, declarative data quality engine. Users define quality checks in a simple provero.yaml file and run them against any supported data source (DuckDB, PostgreSQL, Pandas/Polars DataFrames) with a single CLI command. The package provides 14 built-in check types (not_null, unique, completeness, range, freshness, anomaly detection, custom SQL, and more), a SQL batch optimizer that compiles multiple checks into a single query for performance, data contracts with schema validation and SLA enforcement, statistical anomaly detection using Z-Score, MAD, and IQR methods without requiring scipy, HTML report generation, webhook alerts for Slack/PagerDuty, a SQLite-based result store with time-series history, and a data profiler that auto-generates check suggestions. Provero also ships an Airflow provider and a Flyte plugin for orchestrated pipeline integration.

Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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  • Who is the target audience and what are scientific applications of this package?

    The target audience is data engineers, data scientists, and researchers who need to validate datasets before analysis or model training. Provero is useful in scientific workflows where data integrity is critical, such as verifying that experimental datasets meet expected schema and value constraints before running statistical analyses or ML pipelines.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?

    Great Expectations and Soda Core are the main alternatives. Provero differs by being fully declarative (YAML-only configuration, no Python code required for common checks), vendor-neutral with a plugin architecture, and lightweight with zero heavy dependencies. It also includes a SQL batch optimizer that compiles N checks into a single query, built-in anomaly detection without scipy, data contracts with SLA enforcement, and native Airflow/Flyte integrations as first-class packages.

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Footnotes

1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩