OpenMetadata is a unified metadata platform for data discovery, data observability, and data governance powered by a central metadata repository, in-depth column level lineage, and seamless team collaboration.

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.

Collect, aggregate, and visualize a data ecosystem's metadata

SQL Lineage Analysis Tool powered by Python

First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.

Marmot is an open-source data catalog designed for teams who want powerful data discovery without enterprise complexity. Catalog every data asset, enrich it with the context that matters and make it accessible to your team and your AI tools.

This dbt package captures metadata, artifacts, and test results so you can detect anomalies, monitor data quality, and build metadata tables. It powers Elementary OSS and feeds the wider context layer used by Elementary Cloud’s full Data & AI Control Plane.

One framework to develop, deploy and operate data workflows with Python and SQL.

Metrics Observability & Troubleshooting

Generate and Visualize Data Lineage from query history

Main repo including core data model, data marts, data quality tests, and terminology sets.

Open-source data framework for biology. Context and memory for datasets and models at scale. Query, trace & validate with a lineage-native lakehouse that supports bio-formats, registries & ontologies. 🍊YC S22

Enterprise Information Service

Relational data pipelines for the science lab

Make dbt docs and Apache Superset talk to one another

Reference implementation for real-time Data Lineage tracking for BigQuery using Audit Logs, ZetaSQL and Dataflow.

Visualize column-level data lineage in Spark SQL

🦆 Batch data pipeline with Airflow, DuckDB, Delta Lake, Trino, MinIO, and Metabase. Full observability and data quality.

数据血缘，Hive/Sqoop/HBase/Spark等，发送到kafka后，解析处理使用neo4j生成血缘