Data Observability Python Packages

acryl-datahub

The Context Platform for your Data and AI Stack

elementary-data

The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.

acryl-datahub-airflow-plugin

The Context Platform for your Data and AI Stack

openmetadata-ingestion

The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.

acryl-datahub-actions

The Context Platform for your Data and AI Stack

acryl-datahub-dagster-plugin

The Context Platform for your Data and AI Stack

acryl-datahub-gx-plugin

The Context Platform for your Data and AI Stack

openmetadata-managed-apis

The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.

prefect-datahub

The Context Platform for your Data and AI Stack

datahub-agent-context

The Context Platform for your Data and AI Stack

piperider-nightly

Code review for data in dbt

convalesce-emit

Client libraries that forward a data tool's own output to Convalesce, unchanged.

datasentry-ai

Find, explain, and safely fix bad data. Local-first data quality for files, databases, CI pipelines, and AI agents.

piperider

Code review for data in dbt

re-data

re_data - fix data issues before your users & CEO would discover them 😊

scherlok

A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.

dataops-testgen

DataOps Data Quality TestGen is part of DataKitchen's Open Source Data Observability. DataOps TestGen delivers simple, fast data quality test generation and execution by data profiling,  new dataset hygiene review, AI generation of data quality validation tests, ongoing testing of data refreshes, & continuous anomaly monitoring

proofframe

Ruff for data — Arrow-native data quality in Rust + Python: contracts, canonical fingerprints, keyed diffs, PII & leakage scans, and Ed25519 proof receipts

dqops

Data Quality and Observability platform for the whole data lifecycle, from profiling new data sources to full automation with Data Observability. Configure data quality checks from the UI or in YAML files, let DQOps run the data quality checks daily to detect data quality issues.

driftdb

Metrics Observability & Troubleshooting

upstrace

Trace a failed data quality check upstream to the change that caused it. Column-level drift detection and LLM root-cause analysis for dbt projects.

odd-collector-sdk

ODD Collector

koality

Library for data quality monitoring based on duckdb.

dcs-core

Open Source Data Quality Monitoring.