Matt Jones

AI initiatives are creating an unprecedented demand for high-quality, real-time data. Every model, agent, and application depends on a constant flow of fresh, reliable information - and the traditional approach to hand-coding logic can’t scale at the same rate. This session explores how AI-first pipeline authoring, powered by declarative frameworks, enables teams to meet this new scale and speed. We’ll look at how declarative approaches let data engineers define intent rather than low-level logic, unlocking faster iteration, stronger governance, and easier integration with AI-driven tooling. Finally, we’ll share how data intelligence brings this vision to life, helping organizations build AI-ready data foundations with less complexity and more confidence.

As part of the new Lakeflow data engineering experience, Lakeflow Declarative Pipelines makes it easy to build and manage reliable data pipelines. It unifies batch and streaming, reduces operational complexity and ensures dependable data delivery at scale — from batch ETL to real-time processing.Lakeflow Declarative Pipelines excels at declarative change data capture, batch and streaming workloads, and efficient SQL-based pipelines. In this session, you’ll learn how we’ve reimagined data pipelining with Lakeflow Declarative Pipelines, including: - A brand new pipeline editor that simplifies transformations - Serverless compute modes to optimize for performance or cost - Full Unity Catalog integration for governance and lineage - Reading/writing data with Kafka and custom sources - Monitoring and observability for operational excellence - “Real-time Mode” for ultra-low-latency streaming Join us to see how Lakeflow Declarative Pipelines powers better analytics and AI with reliable, unified pipelines.

Regrow Ag empowers businesses to reduce GHG emissions in their agricultural supply chains by providing key baseline data, monitoring resilient agricultural practices, and quantifying soil carbon gains and reduced emissions. One of our core products, Sustainability Insights, provides users with supply-shed level emissions factors for key commodities in their sourcing regions by 1) delineating field boundaries 2) mapping crop production annually 3) classifying the practices used on each field and 4) modeling how these practices change SOC levels over time.