The Data Splash

Teams keep attaching AI agents to their CRM, then realize they need to deduplicate it first. Abe Gong, CEO and co-founder of Great Expectations, joins Ido Bronstein to explain why organizational memory – not the model – is the real bottleneck for AI. They cover why "context graph" is the most overhyped phrase in data, the curation test every knowledge base should pass, how agents ended the analyst...

For a decade, the data moat was infrastructure—whoever could afford 20 engineers to maintain scrapers won. Nimble's Amaury Desrosiers explains why that moat is gone, how external web data became a first-class citizen alongside your warehouse, and why real-time will soon be a default property, not a category. Plus the three-circle model for internal vs. external data and the one move every data lea...

Twenty years of new tools were supposed to make data engineering easier. Shachar Meir (former Director of Data Engineering at Meta, former data lead at PayPal) argues they made it more chaotic. He explains how "store now, model later" broke the data contract we're now scrambling to rebuild, why data teams fail for reasons that have nothing to do with technology, and the two tips he gives every CDO...

Everyone says they need data lineage. Few can say why. Harel Shein, Senior Engineering Manager at Datadog and an OpenLineage steering committee member, makes the case that AI turns lineage from a nice-to-have graph into infrastructure. We cover what OpenLineage actually is, why agents querying your warehouse need it to understand what data means, the maintainer tax of vibe-coded PRs, and Harel's w...

Atlassian's Head of Data Engineering & AI Enablement, Prakash Reddy, joins host Ido Bronstein, Upriver's Co-founder and CEO, for an honest conversation about using AI to automate data engineering work at scale. Six months in, Atlassian is shipping real results, 5-day tickets in under 3 days, an on-call agent that triages production failures, and a clear roadmap for AI-ready data. But the most...