BUILD with us
in the Age of AI

This session will introduce the general availability of Snowflake Intelligence, a powerful tool that helps enterprises turn all their data into action. It lets you ask questions against your structured and unstructured data in plain language to get instant insights and trigger workflows, bridging the gap between data and business outcomes.

Enterprise can launch MCP servers easily retrieve structured and unstructured data in their agentic applications. MCP clients can discover and invoke tools including Cortex Search and Cortex Analyst to enable AI agents can interact using unified, standards-based interface. In this session, we’ll cover development of a. unified, secure, and governed platform to turn complex enterprise data into trustworthy, actionable insights.

Join this session to learn how Horizon Catalog makes AI adoption and innovation easy, fast, and safe. We’ll cover the latest innovations around interoperability via open standards like Apache Iceberg™ and Apache Polaris™, as well as new products in AI security and governance that make Snowflake a true built-in contextual metadata layer for AI.

Postgres has been around for decades, but it’s more relevant than ever. In fact, it’s now the go-to database thanks to the rise of AI.

This session will explore why Postgres is the ultimate tool for data teams looking to harness AI and how Snowflake Postgres is bringing production-ready Postgres to developers like you.

We’ll start with a brief overview of Postgres and then dive into the core features that make it so adaptable, including its rich data typing, powerful constraints, and native support for complex data types like JSON and GIS. You’ll see how these features make building AI agents and apps both easy and efficient.

Next, we’ll cover how Postgres’s AI data types, especially vectors, are revolutionizing data management. You’ll learn how to connect LLMs directly to your database and, in a live demo, we’ll walk you through the process of building an AI agent with Postgres.

What you’ll learn:
– An overview of Postgres and why it’s dominating the database landscape
– The core features that make Postgres so flexible and powerful
– How to leverage Postgres for AI, with a focus on vector data types and integrating LLMs
– A practical demo of building an AI agent with Postgres

AI is fundamentally changing data engineering, from what data we need to how we build our pipelines. Join us for a deep dive and demo on the latest Openflow capabilities. We’ll show you how to effortlessly connect and integrate unstructured data from sources like SharePoint directly with the Snowflake. You’ll learn how to build a real AI-powered application by demonstrating a real world example. We’ll also cover the latest improvements we’ve made to Openflow and how they can accelerate your pipeline projects.

Tired of moving data? Overcome data fragmentation with a truly open and fully secure Enterprise Lakehouse.

Join us for hands-on-demos covering the latest Apache Iceberg ™ features being announced at Build 2025. Get a first look for unlocking interoperability across more use cases with Snowflake fully managed infrastructure. And learn how to establish a new level of enterprise security with built-in protections for disasters, managing multi-engine access without lock-in and isolating data access across your data estate.

Discover how to build multi-agentic AI workflows using the official langchain-snowflake integration, combining LangGraph’s orchestration capabilities with Snowflake’s Cortex AI platform. Through LangGraph Studio, we’ll develop a supervisor-agent pattern where three specialized Snowflake agents collaborate on customer churn analysis using Cortex Search, Cortex Analyst, and custom tools, demonstrating key routing decisions and agent orchestrations.

Learn how to operationalize your dbt Projects in Snowflake! This session provides a comprehensive deep dive into Snowflake’s dbt Projects feature, with a focus on how to productionalize your dbt Projects. Discover best practices for deploying, orchestrating and monitoring your dbt Projects in Snowflake. And, of course, this session will include demos, so you can see the ideas in action!

We’ll help you:
– Understand the internals of dbt Projects (including Workspaces)
– Deep dive into how to deploy, execute and monitor dbt Projects
– And of course an overview of how to automate dbt Project deployments in CI/CD pipelines

Discover what’s new across Snowflake for app development, from major Snowflake Native App Framework advancements like Shareback and App Composability to critical multi-tenancy enhancements for managed apps. Combined with the GA of Snowpark Container Services integration with Snowflake Native app Framework on all three clouds, new enterprise-grade security controls, and significant performance boosts, you now have a complete platform to build, monetize, and scale any application directly on your data.

Join this session to learn how Snowflake makes it easy to scale your pandas and ML development. Transform massive datasets with ease using your favorite pandas operations, train highly complex ML models using distributed ML algorithms, all within the convenience of your local IDE.

Apache Spark is the go-to engine for many data developers. But what if you could get the performance of Spark without the operational overhead of managing clusters? In this session, we’ll dive into the Snowflake’s Snowpark portfolio, showcasing how Snowflake is meeting developers where they are. Then, we’ll show you how to get hands-on with the new Snowpark Connect for Apache SparkTM to run your existing Spark code directly on Snowflake’s powerful compute engine, all while benefiting from significant performance gains and cost savings.

You’ll walk away knowing how to:
– Seamlessly integrate your existing Spark workflows with Snowflake.
– Leverage Snowpark to build and scale data pipelines using your preferred languages.
– Unify your data engineering and AI/ML efforts on a single, governed platform.

Join this session to learn how global ML teams are accelerating their predictive model workflows in Snowflake ML. We’ll show you how to leverage the latest platform innovations to build, deploy, and manage your entire ML lifecycle.

In a demo, we’ll build an end-to-end ML pipeline, covering everything from distributed training to automated deployment to full-lifecycle tracking – all directly within Snowflake. You’ll leave with a clear understanding of the architectural patterns and best practices for maximizing your team’s ML productivity.

The Snowflake Intelligence Agent enables users to interact with their data environment using natural language, making performance and cost optimization more intuitive and data-driven. By interpreting user queries in plain English, the agent analyzes warehouse performance, query execution patterns, and cost metrics across workloads. It uses built-in AI models to detect inefficiencies such as query bottlenecks, idle compute clusters, or skewed resource usage. Based on these insights, the agent automatically recommends a personalized optimization roadmap—covering areas like right-sizing virtual warehouses, enabling auto-suspend, improving query structures, and optimizing storage. It can also simulate the impact of proposed changes, giving users a clear view of expected savings and performance gains. With continuous learning and adaptive intelligence, the Snowflake Intelligence Agent transforms complex tuning into guided, conversational analytics—empowering teams to maintain peak efficiency with minimal manual intervention.

Discover how Snowflake leverages its own platform to build enterprise-grade applications that orchestrate global team operations across skills management, real-time availability tracking, certification workflows, and intelligent coverage optimization. This session showcases a complete Snow-on-Snow architecture—from native task orchestration and dynamic tables to Streamlit applications—demonstrating how we transformed complex workforce coordination challenges into automated, data-driven solutions that scale seamlessly across distributed teams worldwide.

The session will primarily focus on a live demo, showcasing how Snowflake Intelligence can be leveraged to provision Snowflake environments seamlessly using natural language

As data continues to grow in volume, variety, and velocity, Snowflake’s Openflow offers a scalable, native, and low-code solution to simplify data ingestion into the Snowflake Data Cloud. This session demystifies Openflow and demonstrates how it streamlines onboarding of structured, semi-structured, and unstructured data using reusable, parameterized workflows. We’ll explore its powerful ingestion framework—featuring the Openflow engine, centralized metadata management, automated alerting via Snowflake Native Notifications, and tiered architecture integration. Learn how to build declarative pipelines through a visual UI, gain observability via metadata/log tracking, and enable auto-triggered failure alerts. Through practical demos, we’ll walk through the full ingestion lifecycle—pipeline definition, metadata processing, template execution, and operational monitoring. Attendees will gain actionable insights into deploying and customizing Openflow to reduce orchestration overhead while accelerating data availability. Whether you’re a Data Engineer, Architect, or Scientist, Openflow equips you with the tools to operationalize ingestion natively within Snowflake.

In today’s data-driven world, the quality of your data directly impacts the decisions you make. This session explores how Snowflake empowers organizations to assess and improve data quality using Data Metric Functions (DMFs). Learn how built-in system DMFs help monitor key metrics and how custom DMFs can be tailored to meet specific business needs. Whether you’re just starting your data quality journey or looking to fine-tune your approach, this talk will provide practical insights to help you turn raw data into trusted intelligence.

In this demo session, we’ll showcase how to build a Streamlit app within Snowflake that converts legacy SAS code into modern, Snowflake-compatible SQL or Python using Cortex LLMs. This session is designed for data engineers and developers who are looking to modernize analytics platforms and eliminate reliance on SAS. We’ll walk through how to use Snowflake’s Native App Framework to host the app, leverage Cortex’s built-in LLMs like snowflake-arctic, mistral-large, gemma-7b, and integrate model switching for accuracy comparison. Attendees will see a live demo with real SAS code examples and their converted outputs, supported by implementation tips and tricks and model prompts. We’ll also explain how to customize the prompt chaining logic and discuss how this approach supports scalable code refactoring across enterprise environments. This is a code-first, slide-light session built for real-world impact. Focus is to turn legacy into leverage with Cortex and Streamlit.

80% of the enterprise data exists in unstructured formats like documents, text, images etc. The recent development in AI has made it possible for companies to utilize this data for comprehensive analytics. With Snowflake’s latest releases, developers gain the ability to run analytics on structured and unstructured data using the familiar SQL syntax. In this session, you’ll learn:
-How to build pipelines to conduct batch analytics and extract insights at scale
-How the recent functions like AI_Transcribe can make the insights available to service agents
-How to use key document intelligence features that will go GA at BUILD.

Discover the future of application architecture with the Agentic Data Architecture enabled by the MCP Server on Snowflake. This session will demonstrate how this powerful pattern simplifies your data environment by effectively turning Snowflake into an agentic hub that hosts and orchestrates your integration logic, eliminating the need for complex, custom middleware.

Join us to see how the MCP Server on Snowflake drives a truly agentic and simplified architecture, allowing your data and business logic to operate seamlessly together. This is essential viewing for anyone building modern, scalable applications on the Snowflake platform.

Discover how to modernize database DevOps by fully automating Snowflake schema migrations using GitHub Actions and the open-source schemachange tool. This session outlines an actionable CI/CD pipeline enabling rapid, reliable, and auditable schema changes. Attendees will learn how teams of any size can ensure robust governance, minimize operational risks, and accelerate delivery by leveraging code versioning, branch approvals, and automated deployment workflows. Ideal for those building scalable, secure cloud data infrastructures.