Your enterprise data holds the answers to your most critical business questions, but are you equipped to find them quickly? The gap between data and decision is where opportunity is lost. This session introduces Snowflake Intelligence, the breakthrough that puts the power of sophisticated AI directly into the hands of your business teams. By leveraging AI Agents within the Snowflake AI Data Cloud, every user can now converse with their data in plain language.

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.

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 revolutionising 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

What if you could build and test AI solutions in Snowflake without writing a single line of code? Meet the Snowflake AI Toolkit, an award-winning, no-code native app built on Streamlit, recently recognised by DevNetwork and ranked among the top-starred repositories in Snowflake Labs. This session will show how the toolkit simplifies the entire spectrum from writing simple prompts to building full agentic workflows within a no-code environment using Snowflake Cortex features like text generation, hybrid search, Retrieval-Augmented Generation (RAG), and more. With everything available in one interactive space, users can go from idea to prototype in minutes using real data from Snowflake, no setup required. If you’re looking for a hands-on way to explore Snowflake’s AI capabilities quickly and effectively, this session is made for you.

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.

Join us for an instructor-led virtual hands-on lab, to build a Cortex LLM powered chatbot. This session will be delivered live by our local team based in APAC. This Streamlit-built chatbot uses retrieval augmented generation (RAG) by retrieving prior support chat logs and public-facing documents (Annual Reports, FAQs) to assist in answering questions from customers. In this Snowflake virtual hands-on lab, we will go through all of the pieces required to build the Streamlit in Snowflake application, including Cortex LLMs and RAG fundamentals.

Stay ahead of the curve with an overview of the key capabilities and features for data engineers on Snowflake.

This session focuses on how the platform is evolving to support modern data workloads, streamline complex transformations, and help you build future-proof data products.

Discover the latest tools that simplify development and enable faster insights from your data.

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.

Learn how to operationalise your dbt Projects in Snowflake! This session provides a comprehensive deep dive into Snowflake’s dbt Projects feature, with a focus on how to productionalise 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

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.”

Perhaps you want to build a complete machine learning project from the ground up but are daunted by its perceived complexity.

In this hands-on lab, we’ll embark on a journey to create an end-to-end machine learning project from scratch in Python. Briefly, we’ll start by ingesting data into Snowflake, perform exploratory data analysis, build machine learning models and deploying it into an interactive, data app.

By the end of the lab, you’ll see that the creation of your first machine learning project doesn’t have to be a difficult and tedious process but rather is approachable while abiding to best practices, all in a streamlined workflow. To empower your own exploration, we’re providing all the underlying data and code, so you can dive in and experiment immediately!

This hands-on quickstart demonstrates building a real-time Change Data Capture (CDC) pipeline from PostgreSQL to Snowflake using Snowflake Openflow. You will learn how to capture both initial data snapshots and incremental changes (INSERT, UPDATE, DELETE operations) from transactional databases, enabling real-time analytics with subsecond latency.

Using a healthcare appointment management system as the demo dataset, participants will configure PostgreSQL logical replication, set up the Openflow CDC connector in Snowpark Container Services (SPCS), and leverage Snowflake Intelligence for natural language querying of live CDC data.

Key Learning Outcomes:
– Configure PostgreSQL logical replication for CDC
– Deploy and manage Openflow SPCS runtime
– Capture snapshot and incremental database changes in real-time
– Analyse CDC metadata and audit trails
– Query live data using AI-powered natural language with Snowflake Intelligence

This approach reduces data latency from hours/days (traditional batch ETL) to seconds while minimizing database load and network impact by transferring only changes. The same patterns apply across e-commerce, CRM, ERP, and other transactional systems.

Explore the revolutionary world of Snowflake Cortex AI and Agents. This session will provide a comprehensive look at how to leverage Snowflake’s fully managed AI service to embed intelligence directly into your data.

We’ll cover the core components that enable you to query data using natural language, build sophisticated AI assistants that orchestrate multi-step tasks, and seamlessly unify insights from both structured and unstructured data sources.

Learn how Cortex AI accelerates development, ensures governance and security for your AI initiatives, and fundamentally changes how technical and business users interact with data to drive automated, informed decisions.

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.

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.

Learn how to build and deploy a data pipeline using Snowflake Workspaces integrated with Git. This session walks through transforming Snowflake sample data into a dimensional data mart using SQL scripts managed in a Git repository. You’ll learn how to develop collaboratively within Workspaces, pull and push code, resolve merge conflicts as well as use Copilot to write and test code. You will finally deploy your code with a single deployment script, all from a unified user interface hosted in the Snowflake platform.

AI agents are becoming essential components of modern applications — but giving them secure, structured access to enterprise data is still a major hurdle. In this hands-on workshop, developers will learn how to use Cortex AI Model Context Protocol (MCP) servers to connect AI agents to real enterprise data sources with minimal setup.

You’ll get practical experience with:

– Spinning up a Cortex AI MCP server using Snowflake’s open source tooling
– Connecting AI agents to Cortex Analyst, Cortex Search, and Cortex Agents through MCP
– Executing multi-step tasks like document summarization, insight extraction, and dashboard generation — all orchestrated by an agent

By the end of the session, you’ll have a working data agent powered by Cortex AI MCP Servers, a clear understanding of how MCP fits into modern AI architectures, and the tools to extend your own applications with powerful, agent-native workflows on Snowflake.

You’ll learn to architect an end-to-end analytics pipeline that seamlessly processes audio files, extracts meaningful conversation insights, and delivers an intuitive conversational interface enabling business users to query call center data using natural language—all powered by Snowflake’s integrated Cortex AI platform.

This technical lab demonstrates how to build a comprehensive call center analytics solution leveraging Snowflake’s Cortex AISQL for intelligent audio transcription (Cortex AI Transcribe), conversation analysis, and speaker identification. The solution integrates Streamlit dashboards to provide organizations with actionable insights from call center conversations through advanced AI-powered analytics, sentiment analysis, and natural language querying capabilities on audio file. This session includes code walkthrough, demonstrations and resource links.

Discover the latest AI innovations that are ready for production today. This keynote brings together compelling use cases, success stories, and live demos to illustrate how Snowflake and its ecosystem enable enterprises to operationalise AI with ease—driving efficiency, productivity, and value at scale.

We will also showcase the practical steps for transforming your raw structured and unstructured data into intelligent, user-friendly recommender systems. Walk away with your step-by-step guide to turn your data into tangible business outcomes.

What if your data could detect a problem before it happened—and resolve it automatically? Discover how to turn streaming data into real-time, event-driven operations with Snowflake and Domo. See how AI-powered workflows detect anomalies, predict failures, and automate response actions—all built on Snowflake’s Data Cloud and Domo’s low-code orchestration. From alert to resolution, experience how developers can operationalise data pipelines that drive instant action and measurable business impact.

Unlock the full potential of your SAP data. This session explores how companies are leveraging Snowflake to accelerate their SAP modernization journey, moving beyond traditional analytics to achieve a connected data cloud. We’ll dive into different strategies for effectively leveraging your SAP ERP data within Snowflake, providing a robust, multi-data type foundation for advanced analytics, machine learning, and AI on all your company’s and business partner data.

Learn how to enhance business outcomes by applying AI to your SAP data and discover compelling customer examples that showcase real-world success. You’ll leave with actionable insights on how to transform your SAP data into a strategic asset that drives innovation and growth.

Learn how Snowflake’s integration with Apache Polaris and Iceberg creates a unified, open catalog experience for multi-cloud data architectures. This demo reveals how organizations maintain vendor neutrality while leveraging Snowflake’s performance and governance features across diverse data lake environments.

Ready to move beyond AI experiments and truly unlock its value for your enterprise? Join us to discover how Agentic AI offers a powerful path forward. In collaboration with partners like Anthropic and AWS, we bring leading LLMs directly to your data in Snowflake. This session will dive into the technical blueprints, detailed tool integrations, and practical considerations that will guide your organization from AI proof-of-concept to production, enabling you to drive meaningful business outcomes with AI agents, without the complexity.