Joe Reis

For decades, data engineering has focused on the data engineering lifecycle: building pipelines, orchestrating workflows, managing storage and compute, and overseeing the undercurrents – security, architecture, data governance, and more. But as AI transitions to systems that take action, infrastructure is no longer the main bottleneck.

For decades, data engineering focused on building pipelines, orchestration, and compute. But as AI transitions to systems that take action, infrastructure is no longer the main bottleneck.

In this interactive roundtable, Joe Reis will challenge common assumptions about AI readiness and explore what data leaders should prioritise to build trusted, scalable AI capabilities. Expect a candid discussion separating hype from reality, avoiding costly mistakes, and creating the conditions for AI to deliver measurable business value. Key Takeaways Understand the critical foundations required to scale AI successfully—from trusted data to effective governance. Identify the common gaps that prevent organisations from becoming truly AI-ready, despite significant investment. Gain practical insights into where data leaders should focus their efforts to build trusted, scalable AI that delivers business value.

For decades, data engineering focused on perfecting the infrastructure that moves and processes data. But as AI agents increasingly reason and act on our behalf, infrastructure is no longer the primary bottleneck. The challenge is shifting from machinery to meaning. Tracing the evolution of the field, this presentation explores why semantics, intent, trust, and organizational context will define its next era. The future of data engineering extends beyond optimizing pipelines and storage. It is about giving machines enough context to understand our world, and act responsibly within it.

Every one of us has a list. The tool that should never have shipped. The job title that means nothing. The architectural fashion that cost your last three employers eighteen months each. We all complain about them over coffee and then go back to using them on Monday. Room 404 is where they go to die. Our experts each bring their nominations - a practice, a pattern, a piece of terminology, a technology; they get five minutes to argue why it should never see the light of day again. The rest of the panel either leaps to its defense, or piles right on alongside. Will the nomination go through, will it be consigned forever to the vault - the audience decides, and the verdict is final. Banned or spared, no appeals.

Last year at Current London, we filled a room and had an honest conversation about AI writing code. At the time, it felt like the early days. A year later, the agents arrived, and they're not just suggesting code anymore. They're writing and maintaining codebases, pipelines, and increasingly making architectural decisions that used to belong to humans. Adoption is racing ahead of trust. Most streaming teams are shipping AI-generated infrastructure code faster than they can meaningfully review it, and almost nobody has a clear answer for what that means in two years, whether for system reliability, for engineering skills, or for the engineer's job itself. This session is not a keynote. It's a structured conversation with the people in the room - the engineers and architects actually living this shift in their day jobs. We'll talk about what agents are genuinely good at in writing and maintaining code (and where they still fail spectacularly), how teams are rethinking review and accountability, what skills still matter, and what happens when agents start writing infrastructure for other agents to consume. Come ready to participate and discuss what's happening today, and what's…

Practicing analytics well takes more than just tools and tech. It requires data modeling practices that unify and empower all teams within analytics, from engineers to analysts. This is especially true as AI becomes a part of analytics. Without a governed data model that provides consistent data interpretation, AI tools are left to guess. Join panelists Joe Reis, Sarah Levy, Harry Gollop, Rob Hulme, Shachar Meir, and Guy Fighel, as they share battle-tested advice on overcoming conflicting definitions and accurately mapping business intent to data, reports and dashboards at scale. This panel is for data & analytics engineers seeking a clear framework to capture business logic across layers, and for data leaders focused on building a reliable foundation for Gen AI.

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs. This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

Explore how the rise of agentic ETL and copilots is revolutionizing data engineering. This session will delve into the transformative potential of automation in data integration, reshaping daily workflows, and empowering data teams to focus on innovation rather than routine tasks. Discover how these advancements dramatically enhance productivity, streamline pipeline development, and ensure seamless maintenance in the evolving data landscape. Hear the candid and entertaining views of Joe Reis, best-selling author of "The Fundamentals of Data Engineering."

Vibe Coding is everywhere. AI makes churning out endless code, data, and software easier than ever. Alongside this rise, a new mindset has emerged: forget craftsmanship, abandon discipline—just vibe and let the AI cook. If this gives you pause, that's good! This talk explores a brave new world where practitioners suspend judgment and treat their craft as merely cranking out endless disposable AI-generated content. The consequences are severe: a digital ecosystem drowning in slopware, poor data quality, and crushing technical debt that makes our current challenges seem trivial by comparison. But this talk isn’t all doom and gloom. Joe offers practical advice and solutions for practitioners and leaders to elevate their AI practices today. His strategies help organizations harness AI's power while maintaining code, data quality, and craftsmanship, preventing what could become a gigantic mess beyond comprehension.

Pendant des décennies, la modélisation des données a été fragmentée par les cas d’utilisation : applications, analytique, et ML/IA. Avec l’émergence de l’IA, des flux de données en continu, et l'anticipation de la modélisation des données, ces approches cloisonnées créant des ...

For decades, siloed data modeling has been the norm: applications, analytics, and machine learning/AI. However, the emergence of AI, streaming data, and “shifting left" are changing data modeling, making siloed data approaches insufficient for the diverse world of data use cases.

A good relationship between executive leadership and data practitioners is crucial in the swiftly evolving data landscape. This discussion, presented by decorated data executive Sol Rashidi and veteran data practitioner Joe Reis, delves into the dynamic interplay between strategy and practical implementation in the world of data.

Joe Reis and Matt Housley, co-authors of Fundamentals of Data Engineering, unpack data engineering from first principles. Along the way, they'll discuss the data engineering lifecycle, its undercurrents, and current and future trends in data engineering.