The Data Splash

How Data Teams Power AI - with Abe Gong

Upriver — Wed, 08 Jul 2026 13:15:00 GMT

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 bottleneck, and why data teams must evolve from technical function to the organization's arbiter of truth. Plus: why a good knowledge base makes company culture legible for the first time.

⏱ CHAPTERS 0:00 Welcome and guest intro 1:02 The 30 Second Splash 1:45 Abe's path from algorithms to engineering problems 4:03 What actually counts as a knowledge base 6:12 Curation: why storage alone isn't enough 8:24 How companies manage knowledge today 10:04 The landscape: search, semantic layers, and skills 12:31 Shifting bottlenecks: code review and the end of the analyst gate 14:23 Nine definitions of churn — democratization, solved? 15:30 How knowledge bases reshape the data team 18:49 The data team as the organization's judge 19:52 Planning for agents that do the technical work 22:04 The prize: human–agent collaboration 23:34 Your knowledge base is your culture 26:13 Takeaways and wrap

🎙 ABOUT DATA SPLASH: Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver.

🔔 Subscribe for new episodes weekly.

🔗 LINKS • Upriver: [https://www.upriverdata.com/] • Connect with Abe Gong: [https://www.linkedin.com/in/abe-gong-8a77034/] • Connect with Ido Bronstein: [https://www.linkedin.com/in/ido-bronstein/]

#DataEngineering #AI #DataPlatform #LLMs #AIAgents

What Data Should You Rely On? (AI + Real-Time Data) - with Amaury Desrosiers, Nimble

Upriver — Wed, 17 Jun 2026 19:30:00 GMT

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 leader should make tomorrow.

⏱ CHAPTERS 0:00 Introduction 0:51 The 30 Second Splash 1:44 What Nimble does 2:08 Questions internal data can't answer 3:22 The three concentric circles 4:28 Ground truth vs. context — joining the two 5:40 Why web data used to be a nightmare 6:27 Solving connection and parsing end to end 7:53 The moat moves from infra to usage 9:32 Why real-time value compounds 11:55 Batch vs. ad hoc pipelines 14:24 One layer, not one connector per site 15:34 Schema by use case 16:58 Scale changed, the data model didn't 18:58 Three predictions for the next three years 20:25 What data leaders should do tomorrow 21:19 Takeaways and wrap

🎙 ABOUT DATA SPLASH: Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver.

🔔 Subscribe for new episodes weekly.

🔗 LINKS • Upriver: [https://www.upriverdata.com/] • Connect with Amaury Desrosiers: [https://www.linkedin.com/in/amaurydesrosiers] • Connect with Omri Lifshitz: [https://www.linkedin.com/in/omri-lifshitz-8a531814a/]

#DataEngineering #AI #DataPlatform #LLMs #AIAgents

Future of the Data Engineering Role

Upriver — Thu, 04 Jun 2026 21:22:59 GMT

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. Plus the photographer analogy that explains why he's still not convinced data engineering is going anywhere. Hosted by Ido Bronstein on Data Splash.

⏱ CHAPTERS 0:00 Introduction 0:59 The 30 Second Splash 1:34 What a data advisor actually does 2:50 Why data teams fail: the missing ingredients 4:02 The DBA era — when data was always modeled 7:03 Breaking the contract: Hadoop and data lakes 8:45 Did technology make the job easier? 9:46 Two tips for every CDO 11:34 Enter AI: risks and the how-vs-what shift 15:03 Shifting ownership to the business 16:46 Is managing data still a profession? 18:46 The photographer analogy 19:06 Career advice for early-career data people 21:34 Why the business matters more than ever 22:28 Wrap-up

🎙 ABOUT DATA SPLASH: Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver.

🔔 Subscribe for new episodes weekly.

🔗 LINKS • Upriver: [https://www.upriverdata.com/] • Connect with Shachar Meir: [https://www.linkedin.com/in/shacharmeir/] • Connect with Ido Bronstein: [https://www.linkedin.com/in/ido-bronstein/]

#DataEngineering #AI #DataPlatform #LLMs #AIAgents

OpenLineage, AI Agents, and the Future of Data Engineering - with Harel Shein, Datadog

Upriver — Mon, 11 May 2026 14:53:02 GMT

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 wishlist: push the standard into every engine like Spark, and extend it to ML. One spec to rule them all.

⏱ CHAPTERS 0:00 Intro — What we're getting into 1:05 Rapid-fire questions 2:18 What OpenLineage actually is 3:22 Why Apache Airflow built it in (and the Linux Foundation) 5:10 How AI is reshaping open source maintenance 6:10 Vibe-coded PRs, maintainer burden, and the AGENTS.md fix 7:37 Why coding agents understand open source better than closed source 8:35 Real-world use cases: ops, data quality, compliance, cost 11:38 How AI changes the lineage use case — amplification 13:59 Lineage as the foundation for agents 16:05 MCP, self-serve data, and the trust problem 19:37 Lineage for unstructured data — the hard problem 22:03 If you had an army of engineers, what would you build? 24:39 Where data engineering is heading 26:29 Takeaways and one spec to rule them all

🎙 ABOUT DATA SPLASH: Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver.

🔔 Subscribe for new episodes weekly.

🔗 LINKS • Upriver: https://www.upriverdata.com/ • Connect with Harel Shein: https://www.linkedin.com/in/harelshein/ • Connect with Ido Bronstein: https://www.linkedin.com/in/ido-bronstein/

#DataEngineering #AI #Datadog #LLMs #AIAgents

How Atlassian Is Automating Data Engineering

Upriver — Tue, 28 Apr 2026 13:58:04 GMT

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 useful part of this episode is what had to be true before any of it worked: a multi-year migration to declarative YAML pipelines, environment isolation, and a clean medallion architecture.

In this episode: • Why AI doesn't work without foundational data architecture • The 3 pillars Atlassian picked for AI ROI (and what they skipped) • How to measure productivity gains in total cost of ownership, not velocity • Why hallucinations (3-4 out of 10) are a workflow problem, not a model problem • The "coalition of the willing" approach, bottom-up experiments + top-down consolidation • A prediction on role convergence: knowledge engineer, context engineer, agent orchestrator • Why the moat stops being SQL, and what replaces it

Whether you're a data engineer trying to make sense of where the role is headed, a data leader planning an AI rollout, or just curious how a company at Atlassian's scale is approaching this, this episode is built for you.

⏱ CHAPTERS 00:00 Intro & 30-Second Splash 02:00 How Atlassian's data org is structured 05:00 Why automate data engineering with AI? 08:00 The foundation that made AI possible 10:00 The 3 pillars: incremental dev, on-call, AI-ready data 13:00 Real productivity numbers (and how to measure them honestly) 17:00 Hallucinations, guardrails, and what actually breaks 21:00 Org design: bottom-up + top-down 25:00 The future of data roles — convergence is coming 30:00 Closing thoughts

🎙 ABOUT DATA SPLASH Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver.

🔔 Subscribe for new episodes weekly.

🔗 LINKS • Upriver: [https://www.upriverdata.com/] • Connect with Prakash Reddy: [https://www.linkedin.com/in/prakashreddy1357/] • Connect with Ido

Bronstein: [https://www.linkedin.com/in/ido-bronstein/]

#DataEngineering #AI #Atlassian #DataPlatform #LLMs #AIAgents