# Ploosh

> Ploosh is an open source YAML-based framework used to automate the testing process in data projects. It lets you declare connections and test cases in plain YAML, compare source datasets against expected outcomes, and export detailed reports (JSON, CSV, TRX) — making it well suited for SQL databases, file systems, Spark, Microsoft Fabric and CI/CD pipelines.

Ploosh is distributed as a Python package on PyPI (`pip install ploosh`). Tests are configured through YAML files and can be executed locally or integrated into pipelines such as Azure DevOps. This file lists the canonical resources useful to an LLM consuming the project.

## Documentation

- [Get Started](https://ploosh.io/): Installation, connection setup, first test case and execution.
- [Docs index](https://ploosh.io/docs): Full documentation entry point.
- [Api — Execute Cases](https://ploosh.io/docs/api/execute-cases)
- [Configuration — Command Line](https://ploosh.io/docs/configuration/command-line)
- [Configuration — Custom Parameters](https://ploosh.io/docs/configuration/custom-parameters)
- [Configuration — Options](https://ploosh.io/docs/configuration/options)
- [Connectors — Native - Analysis Services](https://ploosh.io/docs/connectors/native/analysis-services)
- [Connectors — Native - Big Query](https://ploosh.io/docs/connectors/native/big-query)
- [Connectors — Native - CSV](https://ploosh.io/docs/connectors/native/csv)
- [Connectors — Native - Databricks](https://ploosh.io/docs/connectors/native/databricks)
- [Connectors — Native - Delta](https://ploosh.io/docs/connectors/native/delta)
- [Connectors — Native - Empty](https://ploosh.io/docs/connectors/native/empty)
- [Connectors — Native - Excel](https://ploosh.io/docs/connectors/native/excel)
- [Connectors — Native - JSON](https://ploosh.io/docs/connectors/native/json)
- [Connectors — Native - MySQL](https://ploosh.io/docs/connectors/native/mysql)
- [Connectors — Native - ODBC](https://ploosh.io/docs/connectors/native/odbc)
- [Connectors — Native - Parquet](https://ploosh.io/docs/connectors/native/parquet)
- [Connectors — Native - PostgreSQL](https://ploosh.io/docs/connectors/native/postgresql)
- [Connectors — Native - Semantic Model Xmla](https://ploosh.io/docs/connectors/native/semantic-model-xmla)
- [Connectors — Native - Snowflake](https://ploosh.io/docs/connectors/native/snowflake)
- [Connectors — Native - SQL Server](https://ploosh.io/docs/connectors/native/sqlserver)
- [Connectors — Spark - CSV](https://ploosh.io/docs/connectors/spark/csv)
- [Connectors — Spark - Delta](https://ploosh.io/docs/connectors/spark/delta)
- [Connectors — Spark - Dremio](https://ploosh.io/docs/connectors/spark/dremio)
- [Connectors — Spark - Empty](https://ploosh.io/docs/connectors/spark/empty)
- [Connectors — Spark - Fabric Kql](https://ploosh.io/docs/connectors/spark/fabric-kql)
- [Connectors — Spark - Fabric Semantic Model](https://ploosh.io/docs/connectors/spark/fabric-semantic-model)
- [Connectors — Spark - Fabric Warehouse](https://ploosh.io/docs/connectors/spark/fabric-warehouse)
- [Connectors — Spark - JSON](https://ploosh.io/docs/connectors/spark/json)
- [Connectors — Spark - Parquet](https://ploosh.io/docs/connectors/spark/parquet)
- [Connectors — Spark - SQL](https://ploosh.io/docs/connectors/spark/sql)
- [Developers — Add Connector](https://ploosh.io/docs/developers/add-connector)
- [Exporters — CSV](https://ploosh.io/docs/exporters/csv)
- [Exporters — JSON](https://ploosh.io/docs/exporters/json)
- [Exporters — TRX](https://ploosh.io/docs/exporters/trx)
- [Getting Started — Concepts](https://ploosh.io/docs/getting-started/concepts)
- [Getting Started — Installation](https://ploosh.io/docs/getting-started/installation)
- [Getting Started — Quick Start](https://ploosh.io/docs/getting-started/quick-start)
- [Getting Started — What Is Ploosh](https://ploosh.io/docs/getting-started/what-is-ploosh)
- [Pipelines — Azure Devops](https://ploosh.io/docs/pipelines/azure-devops)
- [Pipelines — Fabric Pipeline](https://ploosh.io/docs/pipelines/fabric-pipeline)
- [Pipelines — Github Actions](https://ploosh.io/docs/pipelines/github-actions)
- [Spark — Databricks](https://ploosh.io/docs/spark/databricks)
- [Spark — Fabric Notebook](https://ploosh.io/docs/spark/fabric-notebook)
- [Spark — Fabric Reporting](https://ploosh.io/docs/spark/fabric-reporting)
- [Spark — Fabric Setup](https://ploosh.io/docs/spark/fabric-setup)
- [Spark — Fabric Shortcuts](https://ploosh.io/docs/spark/fabric-shortcuts)
- [Spark — Local Spark](https://ploosh.io/docs/spark/local-spark)
- [Spark — Overview](https://ploosh.io/docs/spark/overview)
- [Use Cases — Data Quality](https://ploosh.io/docs/use-cases/data-quality)
- [Use Cases — Fabric Data Platform](https://ploosh.io/docs/use-cases/fabric-data-platform)
- [Use Cases — Migration Testing](https://ploosh.io/docs/use-cases/migration-testing)
- [Use Cases — Regression Testing](https://ploosh.io/docs/use-cases/regression-testing)
- [Use Cases — Testing Approaches](https://ploosh.io/docs/use-cases/testing-approaches)

## Blog

- [Ploosh + Microsoft Fabric: The Easy Way to Test Your Data Platform](https://ploosh.io/blog/ploosh-microsoft-fabric-the-easy-way-to-test-your-data-platform): Learn how to integrate Ploosh into Microsoft Fabric to automate data quality testing at scale using Spark distributed processing across multiple workspaces.
- [ploosh: three key approaches to automating tests in data projects](https://ploosh.io/blog/ploosh-three-key-approaches-to-automating-tests-in-data-projects): Introduction In previous articles, we introduced Ploosh as an automated testing framework, highlighting its role in preventing regressions and improving the quality of deliveries in complex data projects.
- [ploosh: how to simplify your migration testing?](https://ploosh.io/blog/ploosh-how-to-simplify-your-migration-testing): In a previous article, I introduced Ploosh, a tool I developed to facilitate testing in the data domain. Today, I will show you a use case where Ploosh was used to improve efficiency during testing phases.
- [ploosh: a framework to automatize tests in data project](https://ploosh.io/blog/ploosh-a-framework-to-automatize-tests-in-data-project): In this article, I will present the issues related to testing in data projects and introduce one of my tools to address them.

## Optional

- [Changelog](https://ploosh.io/changelog): Release history for the Ploosh package.
- [Version 0.5.6 (2026-06-09)](https://ploosh.io/changelog/0.5.6)
- [Version 0.4.11 (2026-04-16)](https://ploosh.io/changelog/0.4.11)
- [Version 0.4.8 (2026-04-15)](https://ploosh.io/changelog/0.4.8)
- [Version 0.4.7 (2026-01-20)](https://ploosh.io/changelog/0.4.7)
- [Version 0.3.8 (2025-01-15)](https://ploosh.io/changelog/0.3.8)
- [Version 0.2.32 (2024-12-22)](https://ploosh.io/changelog/0.2.32)
- [Version 0.2.31 (2024-12-21)](https://ploosh.io/changelog/0.2.31)
- [Version 0.2.29 (2024-12-19)](https://ploosh.io/changelog/0.2.29)
- [Version 0.2.27 (2024-12-17)](https://ploosh.io/changelog/0.2.27)
- [Version 0.2.26 (2024-12-16)](https://ploosh.io/changelog/0.2.26)
- [Version 0.2.25 (2024-12-15)](https://ploosh.io/changelog/0.2.25)
- [Version 0.2.24 (2024-12-14)](https://ploosh.io/changelog/0.2.24)
- [Version 0.2.20 (2024-12-10)](https://ploosh.io/changelog/0.2.20)
- [Version 0.2.18 (2024-12-08)](https://ploosh.io/changelog/0.2.18)
- [Version 0.2.17 (2024-12-07)](https://ploosh.io/changelog/0.2.17)
- [Version 0.2.16 (2024-12-06)](https://ploosh.io/changelog/0.2.16)
- [Version 0.2.14 (2024-12-04)](https://ploosh.io/changelog/0.2.14)
- [Version 0.2.12 (2024-12-02)](https://ploosh.io/changelog/0.2.12)
- [Version 0.2.11 (2024-12-01)](https://ploosh.io/changelog/0.2.11)
- [Version 0.2.9 (2024-11-30)](https://ploosh.io/changelog/0.2.9)
- [Full content bundle](https://ploosh.io/llms-full.txt): All docs, blog posts and get-started guide concatenated in raw Markdown.