In this session we present TPC-DS benchmarking results on Iceberg tables with varying sizes of Parquet files, to help you optimize your lake house. We also show how we generate the files and analyzed the results.

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Successfully operationalizing machine learning (ML) is critical for organizations seeking to maximize the return on their initial investments. Building ML solutions in Snowflake requires not only architectural choices but also careful consideration of how to seamlessly integrate all components across the ML lifecycle. This session explores key trade-offs, lessons learned and other factors to effectively operationalize ML in Snowflake.

With over 50,000 active users, discover how we transformed enterprise data interaction through Snowflake's Cortex Analyst API with SiemensGPT. Our plugin architecture, powered by the ReACT agent model, converts natural language into SQL queries and dynamic visualizations, orchestrating everything through a unified interface. Beyond productivity gains, this solution democratizes data access across Siemens, enabling employees at all levels to derive business insights through simple conversations.

Learn how Trade Republic builds its analytical data stack as a modern, real-time Lakehouse with ACID guarantees. Using Debezium for change data capture, we stream database changes and events into our data lake. We leverage Apache Iceberg to ensure interoperability across our analytics platform, powering operational reporting, data science, and executive dashboards.

The presentation outlines a strategic, three-phase framework for developing data and AI solutions that yield a high return on investment. The session addresses the critical challenge that a significant majority of companies face: the inability to scale and achieve tangible value from their AI initiatives. The blueprint is valuable for attendees because it provides a clear framework for identifying a project's primary function and its associated costs, enabling them to justify scaling and ensure maximum value is realize.

Snowflake ML enables efficient development and deployment of advanced models without any data movement. With multi-GPU support, MLOps integration and Git-based workflows, Container Runtime provides a scalable environment for training, and Snowflake ML’s products such as Model Registry and Model Serving make it easy to deploy these models in production. This session explores best practices for scalable ML workflows and the creation of production-ready ML pipelines in Snowflake.

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Ratepay is leveraging Snowflake's native LLM and ML features to build an automated quality assurance routine for its customer service. By ingesting knowledge base data from various sources—including emails, and images—this system automatically checks the outcome of all outgoing correspondence. This innovative approach is on track to save Ratepay significant costs by reducing reliance on external vendors and moving away from manual spot-checks. Join us to learn how to implement an automated AI-driven solution that ensures quality, reduces operational costs, and scales efficiency in customer service.

Unlock the full potential of your data. Discover how the Zero Copy integration between Salesforce Data Cloud and Snowflake creates a unified, trusted data foundation to power your AI strategy—without moving your data. We'll demonstrate how secure, governed bi-directional Zero Copy access enables real-time customer interactions and provides AI agents with the reliable context they need to succeed. Join an expert from our Munich Engineering team for an inside look at what makes Zero Copy possible: Hyper, Data Cloud's high-performance query engine, envisioned and built right here in Munich.

Snowflake is re-envisioning ease of performance in the AI era. Join us to learn about groundbreaking adaptive compute, brand new intelligent workload optimization capabilities, and industry-leading DML performance.

tesa SE is a global adhesive manufacturing company. In their highly automated tape production process it's needed to observe quality and effiency abnormal events with very short latency to avoid high costs.

Utilizing Snowflake's Machine Learning capabilities, Tesa SE is monitoring various KPI's that indicate the correct production process.

Tesa's newest innovative usecase aims to decrease waste during the production process using anomaly detection methodologies, which are trained on Snowflake, and used for inference on-edge for optimal latency.

The machine learning model pipeline components are built and served leveraging Snowflake features such as Snowflake CLI, Snowpark Pandas and other Snowflake capabilities to streamline the overarching ML process.

Snowflake recently launched new capabilities to help organizations build data agents connected to enterprise data environment. This session explores why Snowflake makes it easy to iterate, monitor and observe agentic AI systems. Learn associated best practices related to RBAC integration in agentic workflows and applications. The session will also focus on change control and environment management and the software development lifecycle within the AI agents development lifecycle.

Discover how Boehringer is managing data translation and harmonization enabling the creation of seamless data integrations worldwide. This session will detail how Boehringer transformed its fragmented, data landscape into a centralized data factory on Snowflake, reducing costs while dramatically improving scalability and efficiency. We will explore how leveraging Cortex LLM Functions automates data mapping and consistency, enabling the creation of integrated data products that empower global business steering. Join us to learn how to overcome data harmonization challenges and build a data factory mindset for better decision-making and unprecedented growth.

In this session, we’ll walk through why we decided to abandon Redshift, driven by the need for elasticity, cost efficiency, and faster iteration. We'll also discuss how we designed a structured migration blueprint, including a vendor evaluation, proof of concept, and a four-pillared framework to guide our journey.

We’ll then dive into the areas where things got turbulent: why off-the-shelf translation tools failed, why LLMs didn’t help, and the specific technical challenges we encountered. From surrogate key issues to Stitch's unflattened data and Snowplow’s monolithic event tables.

We’ll explain how we kept the migration on track by turning our analysts into co-navigators, embedding them into validation loops to leverage domain knowledge, and how a parallel ingestion strategy helped stabilize progress during high-risk phases.

Finally, we’ll share what this migration unlocked for us: faster ingestion, better permission management and more. Expect real-world pitfalls, lessons learned, and actionable insights for your own migration journey.

Discover how Snowflake Dynamic Tables revolutionize the implementation of Slowly Changing Dimensions (SCDs). Learn to create automated, self-maintaining data pipelines that eliminate complex ETL processes and reduce maintenance overhead. This session demonstrates practical examples of building Type 1 and 2 SCDs using Dynamic Tables, showcasing their cost-efficiency and performance benefits while ensuring data accuracy and historical tracking.

This session highlights Wipro's innovative approach to transforming financial operations using AI agents. By leveraging Wipro's deep industry expertise and Snowflake's data prowess, we have developed a scalable, secure, and intelligent system that revolutionizes the management of supplier-related queries around invoices and payments. Our solution has significantly increased processing efficiency, reduced latency and manual activities, and achieved high accuracy in responses. The implementation has led to substantial cost savings and improved response times, demonstrating the potential of AI-powered solutions in enhancing business operations.

Allianz replaced its legacy campaign system with a cloud-native Campaign Data Hub (CDH) powered by Snowflake, unifying data from three core business lines onto a single, real-time platform. This modern architecture reduces costs and frees up IT resources, while empowering over 8,000 agents with on-demand customer insights and the ability to pivot campaign messaging in minutes. The result is a strategic shift from legacy complexity to data-driven growth, enabling Allianz to launch hyper-personalized campaigns at scale and drive a sharp increase in agent productivity and conversion rates.

FFT, a global leader in innovative manufacturing systems, has developed a solution to the persistent challenge of bridging IT and OT data. In just six months, FFT launched the FFT DataBridge, which resides on shopfloor edge devices and seamlessly integrates production data into the Snowflake AI Data Cloud. This solution unlocks critical shopfloor analytics, AI-based forecasting, and predictive maintenance. By leveraging the power of Snowflake, FFT is helping manufacturing companies transform their operations and is continuing their journey by building a suite of IT/OT-centric applications for the Snowflake Marketplace.