Featured upstream case study
I audited Polaris setup as a state round trip and fixed seven ways it could report success with incomplete state or silently change catalog configuration. The merged series now preserves nested namespaces, same-named policies, policy content, and entity and role metadata, while returning failure when apply or export is incomplete.
5,637 GitHub contributions in 2026, flowing through a data pipeline.
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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 table format for large analytic datasets, so teams can update and query lakehouse data reliably at scale.
An open catalog for Iceberg tables—the shared directory that helps engines find and manage lakehouse datasets.
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 Python library that loads data from APIs and databases into analytics warehouses with less glue code.
A lightweight semantic layer that gives AI agents and people a shared, governed definition of their data.
A fast in-memory Datalog rule engine for analytic data processing with RDF and SPARQL compatibility.
A vendor-neutral YAML specification for exchanging semantic metadata—metrics, dimensions, and relationships—across analytics, AI, and BI platforms.