Think you know Snowflake? Think again. Snowflake has been on a journey to break down silos to help organisations put their data to work. From high-performance analytics at scale to global collaboration across shared data to enabling full programmability without sacrificing governance, Snowflake has redefined what’s possible with data.

The Snowflake Data Cloud enhances collaboration across multiple teams to go from raw data to production-grade data pipelines, machine learning models, and applications, but how? At Snowflake Summit we unveil the next wave of innovations – including advancements in open table formats, generative AI and LLMs, flexible programmability with Snowpark, and much more.

At Snowflake Summit we unveiled the next wave of innovations – including advancements in open table formats, generative AI and LLMs, flexible programmability with Snowpark, and much more. Join Natalie Mead – VP Sales Engineering APJ – to explore the latest announcements, technical demos, and learn how you can accelerate with the Data Cloud.

Join us to hear the latest Snowflake platform innovations including new out-of-the-box performance improvements, advanced analytics capabilities and enhanced business continuity.

You’ll also learn new ways to monitor, control and optimise spend alongside our latest built-in workload optimisation.

Do you ever wonder about the lifecycle of a Snowflake query?

And how to investigate and understand factors that impact your query performance? This session aims to address these questions and more while providing actionable insights and practical tips to help you improve query performance.

Topics addressed include a comprehensive overview of Snowflake query performance, covering the lifecycle of a Snowflake query, the Snowflake optimiser, factors affecting query performance and best practices to improve performance.

Participants will gain a deeper understanding of the fundamentals of the Snowflake compiler and optimiser, learn troubleshooting techniques to identify performance bottlenecks and see how Snowflake features and services can optimize query performance for different scenarios.

Productising ML models, programmability and processing data to generate insights are the top 3 P’s of data scientists but they often struggle with them. Join us in this session to learn how you can use Snowflake as the data cloud platform to power your ML workloads without having to worry about security and governance.

Learn how the Snowpark ML Model Development API can help data scientists to build models, empower ML engineers to maintain their ML pipelines and streamline MLOps, and assist data analysts with pre-built ML functions to drive insights with Snowflake.

A big part of data science and machine learning workflows is data pre-processing and transformation for creating useful features that serve as inputs for ML models. Once useful features are defined and computed, they need to be kept up to date with new data and used consistently with a single source of truth across all use cases and across teams.

In this session, we will show how Snowflake provides powerful functionality for supporting large-scale feature engineering through new capabilities for data transformation in Snowpark, and management of these features via feature store infrastructure linked to an end-to-end machine learning operations solution.

This helps you bring feature engineering to where your data is, reducing need for unnecessary data movement and taking advantage of Snowflake’s scalability, security, and governance.

With Snowpark Python many who attended Discover sessions would like to bring production workloads in Python to Snowflake. But, how? For instance, how should you think about the DataFrame API vs. user-defined functions vs. UDTFs?

In this session, you’ll learn core design principles for building capabilities on top of Snowpark Python (across data engineering, data science, and data application workloads).

Snowpipe Streaming reduces ingestion cost as it is on demand compared to running a streaming receiver like Spark that is running 24-7-365.

These serverless features self-heal, which reduces the cost of maintenance as well. In this session, you’ll see an ingestion and transformation pipeline build that saves cost on streaming data using new Snowpipe Streaming and Dynamic Table functionality.

With the Snowflake Native App Framework you can now bring your application to your data, natively in the Data Cloud! In our prior Apps Discover sessions we looked at connected apps and now we look at the new Native App capabilities.

In this session we dive into the framework, user models and samples of how you can build Snowflake Native Apps, distribute and monetise them via Snowflake Marketplace.

Join this session to learn more about Self-Service Analytics, an application built using Streamlit that enables its users to create self-service visualisations.

Instead of spending time writing Python and SQL code to develop a dashboard or analytical report, this application automatically provides users with a simple selection-based GUI, helping create reports and interactive dashboards.

Join this session to learn how you can use Self-Service Analytics on Streamlit to create visually arresting charts, filters and aggregations-all out of the box. Learn about the benefits of saving and retrieving work any time you need it, helping replace licensed visualisation and reporting tools, and saving costs org-wide.

In this session we look at Snowpark with a twist! Snowflake’s powerful data utilisation capabilities, combined with the productivity of ChatGPT, make many things easier to create and analyse.

This session was created by one of our local SE’s and is designed as a fun exploration of Snowpark.

Cybersecurity professionals need to shift from their obsession with just ingesting volumes of data to focus on the quality of data they are gathering.

But how does one define data quality? And how does one handle a lack of data quality which could result in a breach?

To understand, specify, monitor, and handle data-quality issues, Snowflake’s security and data science teams are leveraging Streamlit, Snowpark, and other Snowflake features to create a no-code, no-expertise-required solution.

This session will highlight how Snowflake’s security organisation leverages data science techniques that effortlessly dovetail with Snowflake’s features to present a novel approach to handling data quality at large volumes.