How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Gravitino is a federated metadata lake and unified data catalog designed to manage tables, files, and AI models across diverse data sources and cloud storage. It serves as a centralized interface for governing schemas, access controls, and tagging across relational databases, messaging queues, and object stores. The project distinguishes itself by unifying the management of AI assets, such as machine learning models and their version lineages, alongside traditional tabular data. It also implements the Iceberg REST specification to provide a standardized metadata server and proxy for lakehouse
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
Elementary OSS: dbt-native data observability
The main features of elementary-data/elementary are: Data Catalogs, Data Quality, Data Quality and Observability, Open Source Catalogs.
Open-source alternatives to elementary-data/elementary include: sodadata/soda-core. grai-io/grai-core. odpi/egeria — Egeria provides the Apache-2.0 licensed open metadata and governance type system, frameworks, APIs, event payloads and… datahub-project/datahub — DataHub is a metadata management platform designed to unify technical, operational, and business context across… apache/gravitino — Gravitino is a federated metadata lake and unified data catalog designed to manage tables, files, and AI models across… dagworks-inc/hamilton — Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode…
