Featured Articles

AI agent observability gives you traces, metrics, and evals to see how agents behave, and the upstream data layer most setups miss. Here's both.

Agentic data management lets AI agents run the data lifecycle. Learn why autonomy only works when data is trustworthy at the source, via data contracts.

Agentic AI data governance means controlling how autonomous agents read, write, and act on data. Learn why monitoring falls short and contracts work.

A core idea behind shifting Data Left is simple but often overlooked: data is code. Or more accurately—data is produced by code. It’s not just some downstream artifact that lives in tables and gets piped into dashboards and spreadsheets. Every record, event, or log starts somewhere—created, updated, or deleted by a line of code. And just like DevOps demonstrated, if you want to manage something well, you start at the point of creation.

Understanding dataflow’s key elements and challenges, as well as why a data-centric POV is critical, is becoming essential for modern software engineers.

Learn what the BCBS 239 framework is, why it’s important for data governance and risk reporting in banks, and how to implement it to improve risk decisions.

Discover how code cataloging adds structure and governance to schemas, APIs, and contracts defined in code, closing the gap between engineering and data teams.

Learn how data scanning goes beyond discovery to identify sensitive data, enforce security upstream, and reduce breach risk using a code-first approach.

Missing data ownership leads to broken systems and lost trust. Learn why ownership is the true foundation for highly scalable, distributed data systems.