In the era of Agentic & GenAI, having a strong data engineering foundation is more important than ever. Learn how to build Simplified, governed, performant & resilient data engineering framework for the modern era
In today’s digital-first world, data engineering is at the heart of transforming how lending works—from credit risk modeling to real-time loan approvals. This session explores how modern data pipelines, scalable architectures, and intelligent automation are powering more accurate, faster, and inclusive lending decisions. Learn how financial institutions are leveraging data engineering to drive innovation, ensure compliance, and deliver seamless borrower experiences.
As data volumes surge, traditional data engineering struggles with complexity, cost, and agility. This keynote introduces a paradigm shift: agentic systems — AI-native agents that autonomously plan, adapt, and collaborate across the data lifecycle. These systems transform ingestion, quality, pipeline design, and observability, freeing engineers to focus on design and value, not manual tasks.
We explore today’s DataOps limitations, define agentic capabilities, showcase real-world use cases, and examine shifts in skills, teams, and ethics. For CDOs, platform leaders, and architects, this session offers a bold vision: Data Engineering reimagined through intelligent agents that deliver faster, smarter, and more scalable business outcomes.
As AI evolves towards autonomous agents, integrating real-time data becomes crucial. This session explores how StarTree’s support for the Model Context Protocol (MCP) and native vector auto-embedding empowers AI agents with live, structured data access. Attendees will gain insights into building scalable pipelines that combine streaming ingestion (Kafka), real-time analytics (Apache Pinot), and AI models. A live demonstration will showcase the integration of Kafka → Pinot → AI agent, highlighting the architecture that enables real-time decision-making with contextually relevant information. This talk emphasizes practical takeaways for architecting systems that seamlessly blend AI and analytics technologies.
Over the last couple of years, the capabilities of large language models (LLMs) and Generative AI have evolved at a breakneck pace, with enterprise adoption accelerating rapidly. These technologies are not only driving automation but are fundamentally challenging traditional approaches to how data is engineered, managed, and consumed.
We are entering a new paradigm—one where emerging ways of working are redefining what’s possible across the enterprise data ecosystem. Join us for this Keynote as we explore what this transformation means for data engineers and business leaders. We’ll examine real-world use cases, the evolving skill sets needed to thrive, and how teams can adapt to stay ahead in a rapidly shifting landscape.
Unifying Data & AI starts here. Explore how Starburst’s open hybrid lakehouse powers advanced RAG workflows across modern data platforms. With data mesh tying it all together, discover the future of seamless, scalable analytics.
The current technological landscape underscores a significant convergence, where functionalities traditionally associated with separate domains of data management and AI are now unified within intelligent data platforms. This integration marks a fundamental shift in perspective, moving away from the concept of AI as merely an application layer to AI-integrated data infrastructure. This amalgamation facilitates a more streamlined and effective utilization of data for AI initiatives, fastens the pace of iteration in model development and ensures a tighter alignment between an organization’s overall data strategy and AI vision. Furthermore, the emphasis placed on the delivery of “actionable insights” and the facilitation of “powerful decision-making” highlights the primary business-oriented purpose of these platforms. The evolution of data platforms reflects an imperative to not only manage the exponentially growing data volume but also derive intelligence that can inform and drive strategic decisions. Intelligent data platforms are engineered explicitly with this objective at their core, leveraging AI to automate insight and offer recommendations for accelerated organizational innovation.
The data landscape is transforming with generative AI driving innovations in pipeline architecture, real-time stream processing, and modern ETL practices. Emphasis on data trust, quality, and scalable monetization, alongside advancements in schema design and access layers, is unlocking new opportunities and efficiencies for businesses.
