Improving the Reliabilityof Data Infrastructure

An open-source engine for running SQL data transformations safely and correctly in analytics pipelines.

An open standard for data lineage—tracking where data comes from, how it changes, and where it goes.

An open catalog for Iceberg tables—the shared directory that helps engines find and manage lakehouse datasets.

An open table format for large analytic datasets, so teams can update and query lakehouse data reliably at scale.

A SPARQL graph database and RDF toolkit for storing and querying knowledge graphs.

A Spark data-quality toolkit that helps teams catch bad or inconsistent data before it reaches dashboards and models.

A lightweight semantic layer that gives AI agents and people a shared, governed definition of their data.

A local-first project manager for developers and AI agents, with a CLI, web app, and shared board for tasks, plans, and docs.

A Python library that loads data from APIs and databases into analytics warehouses with less glue code.

A vendor-neutral YAML specification for exchanging semantic metadata—metrics, dimensions, and relationships—across analytics, AI, and BI platforms.

A fast in-memory Datalog rule engine for analytic data processing with RDF and SPARQL compatibility.