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.

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.

The open-source context layer for your AI. Catalog your tables, topics, queues and APIs then expose real metadata to your AI agents.

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 lakehouse for biology. Query, trace & validate with a lineage-native lakehouse that supports bio-formats, registries & ontologies. Context and memory for millions of datasets & transforms, across infrastructure. 🍊YC S22

The typed graph between your code and whichever warehouse, table format, or query engine you've chosen — typed compiler, branches, replay, column-level lineage, compile-time contracts, per-model cost. Adapters: Databricks, Snowflake, BigQuery, DuckDB. Single static Rust binary. Apache 2.0.

Enterprise Information Service

Relational Workflows: where database schemas define executable data pipelines.

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.