Piperider

Overview

Piperider is an open-source observability tool that integrates with dbt to profile and validate data models. It runs automated data quality checks and generates reports to catch anomalies before deployment. The tool is written in Python and maintained by the community.

Best for

Best for
dbt users who need a simple, open-source data quality and profiling tool

Use cases

• Profiling dbt models to detect schema changes or data drift
• Running automated data quality tests in CI/CD pipelines
• Generating data documentation and validation reports for stakeholders

Notes

Piperider is an open-source observability tool that integrates with dbt to profile and validate data models. It runs automated data quality checks and generates reports to catch anomalies before deployment. The tool is written in Python and maintained by the community.

494 stars on GitHub. Last updated 2025-01-03. Licensed Apache-2.0.

Use cases

• Profiling dbt models to detect schema changes or data drift
• Running automated data quality tests in CI/CD pipelines
• Generating data documentation and validation reports for stakeholders

Pros

• Free and open-source with no vendor lock-in
• Tight integration with dbt workflows and metadata
• Lightweight and easy to add to existing dbt projects

Cons

• Smaller community and fewer resources compared to commercial alternatives
• Limited to dbt environments, not a general-purpose observability tool
• May require manual configuration for complex data validation rules

Indexed from awesome-llmops and enriched against its public facts.