Last year at Summit, we announced the public launch of the Snowflake Performance Index (SPI), an aggregate index for measuring real-world improvements in Snowflake performance experienced by customers over time.

It is estimated that between 80% and 90% of the world’s data is unstructured1, with text files and documents making up a significant portion. Every day, countless text-based documents, like contracts and insurance claims, are stored for safekeeping. Despite containing a wealth of insights, this vast trove of information often remains untapped, as the process of extracting relevant data from these documents is challenging, tedious and time-consuming.

Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with the underlying data. Many enterprises stitch together a complex mix of various MLOps tools to build an end-to-end ML pipeline. The friction of having to set up and manage separate environments for features and models creates operational complexity that can be costly to maintain and difficult to use.

Today’s data-driven world requires an agile approach. Modern data teams are constantly under pressure to deliver innovative solutions faster than ever before. Fragmented tooling across data engineering, application development and AI/ML development creates a significant bottleneck, hindering the speed of value delivery required to stay competitive. Disparate tools create a complex landscape for developers and data teams, hindering efficient pipeline development and deployment.

Discovering and surfacing telemetry traditionally can be a tedious and challenging process, especially when it comes to pinpointing specific issues for debugging. However, as applications and pipelines grow in complexity, understanding what’s happening beneath the surface becomes increasingly crucial. A lack of visibility hinders the development and maintenance of high-quality applications and pipelines, ultimately impacting customer experience.

We’re pleased to announce the launch of Snowflake Notebooks in public preview, a highly anticipated addition to the Snowflake platform tailored specifically to integrate the best of Snowflake within a familiar notebook interface. Snowflake Notebooks aim to provide a convenient, easy-to-use interactive environment that seamlessly blends Python, SQL and Markdown, as well as integrations with key Snowflake offerings, like Snowpark ML, Streamlit, Cortex and Iceberg tables.

Snowflake announced the global winners of the sixth annual Data Drivers Awards, the premier data awards that honor Snowflake customers who are leading their organizations and transforming their industries with the AI Data Cloud. This year’s winners of the Data Drivers Awards include data leaders from across global organizations, including Caterpillar, Bentley, Mitsubishi Corporation, Zoom and more.

Apps are the way to democratize AI: to make it accessible to everyone and streamline customers’ experiences with faster time to insights. According to a recent IDC survey, AI applications is currently the largest category of AI software, accounting for roughly one-half of the market’s overall revenue in 2023.

In today’s world, innovation doesn’t happen in a vacuum; collaboration can help technological breakthroughs happen faster. The rise of AI, for example, will depend on the collaboration between data and development. We’re increasingly seeing software engineering workloads that are deeply intertwined with a strong data foundation.

Open source file and table formats have garnered much interest in the data industry because of their potential for interoperability — unlocking the ability for many technologies to safely operate over a single copy of data. Greater interoperability not only reduces the complexity and costs associated with using many tools and processing engines in parallel, but it would also reduce potential risks associated with vendor lock-in.