top of page

Takeaways From Day 2 of the Snowflake Summit 2024

Snowflake Summit 2024 unveils advancements in AI, data collaboration, and developer tools, empowering enterprises to unlock the full potential of their data.



Snowflake Data Cloud Summit 2024 has taken the data world by storm, showcasing groundbreaking innovations that empower enterprises to harness the power of AI and drive unparalleled value from their data. The event has set the stage for a new era of enterprise AI, from advancements in Snowflake Cortex AI and Snowflake ML to enhanced data collaboration and developer tools.


Strengthening the Data Foundation

During the Product Keynote, Christian Kleinerman, SVP of Product at Snowflake, emphasized the importance of strengthening the data foundation. "Our single goal with the Snowflake AI Data Cloud is to help you get more value out of your data and help you achieve the goals for your company faster, better, and, of course, leverage trends like artificial intelligence," Kleinerman stated.


He highlighted the introduction of Document AI, which allows users to query data using natural language, and announced that it will be generally available soon. Kleinerman also showcased the power of streaming and batch data processing with Snowflake, citing Woodside Energy's ability to ingest up to 1.5 million rows per second.


Democratizing AI With Conversational Experiences

One of the key highlights from the summit was the introduction of new chat experiences within Snowflake Cortex AI. With Cortex Analyst and Cortex Search, organizations and individuals can now develop chatbots in minutes, enabling users to interact with structured and unstructured data using natural language. This democratization of AI allows businesses to extract valuable insights and make data-driven decisions with unprecedented speed and efficiency.


Snowflake Cortex AI also introduces Document AI, which enables users to easily extract content like invoice amounts or contract terms from documents using Snowflake's industry-leading multimodal LLM, Snowflake Arctic-TILT. With Arctic-TILT outperforming GPT-4 in the DocVQA benchmark test, organizations can intelligently process documents at scale, lowering operational overhead and increasing efficiency.


Governance and Discovery in the AI Data Cloud

During a one-on-one interview, Prasanna Krishnan, Head of Collaboration and Horizon at Snowflake, emphasized the importance of governance and discovery in the AI Data Cloud. "When it comes to enterprise AI, governance and the ability to have efficiency and not just spiraling costs are really important," Krishnan stated.


Snowflake Horizon, the built-in governance and discovery solution, provides a unified set of capabilities to protect data products and ensure compliance. Krishnan explained, "Based on RBAC, you can apply governance to data, models, and applications. It's about knowing what data exists, having classifications to identify PII, and then protecting it."


Introducing the Internal Marketplace allows users to curate and publish data products specifically for teams within their organization, facilitating secure collaboration and data sharing. Additionally, Snowflake is extending its industry-leading collaboration capabilities to include sharing AI models, Iceberg Tables, and Dynamic Tables.


Enhancing Data Discovery With AI-Powered Features

Krishnan also highlighted the significance of Universal Search, powered by Neeva's technology, which enables users to search the AI Data Cloud using natural language. "Universal Search understands things like column names, and we're also using our cortex NLM capabilities to auto-generate some of these descriptions," Krishnan explained. This feature enhances the user experience and makes it easier for teams to discover relevant content.


Moreover, the AI-Powered Object Descriptions feature automatically generates relevant context and comments for tables and views, improving data discovery and understanding for developers and engineers. Given that 80% of data analysts and data scientists’ time has been spent preparing data for analysis, AI-Power Object Descriptions will be hugely popular, saving time and money while making high-priced analysts and scientists much more productive.


Embracing Open Standards and Interoperability

Another key theme at the summit was Snowflake's commitment to open standards and interoperability. Kleinerman introduced Iceberg Tables, now generally available, which allow customers to work with data in open formats while benefiting from Snowflake's performance and governance capabilities.


The recently announced Polaris Catalog, a vendor-neutral and open catalog implementation for Apache Iceberg, provides organizations with increased choice, flexibility, and control over their data. Snowflake's partnerships with major cloud providers like AWS, Google Cloud, Microsoft Azure, and Salesforce further demonstrate their dedication to enabling seamless data collaboration and eliminating data silos.


Empowering Developers With Advanced Tools

For developers, the summit introduced a host of new tools and capabilities to accelerate building enterprise-grade pipelines, models, and AI-powered applications. Kleinerman showcased Snowflake Notebooks, natively integrated with the Snowflake platform, providing a single, easy-to-use development interface for Python, SQL, and Markdown.


Snowflake is also introducing the Snowpark pandas API, enabling Python developers to work with familiar syntax while benefiting from Snowflake's performance, scale, and governance. The new Database Change Management feature and Git integration streamline development collaboration and deployments across different environments.


Observability and Application Development in the AI Data Cloud

Kleinerman also introduced Snowflake Trail, a rich set of observability capabilities that empower developers to monitor, troubleshoot, and optimize their workflows easily. Built-in telemetry signals for Snowpark and Snowpark Container Services enable users to diagnose and debug errors using metrics, logs, and distributed tracing without manual setup.


Integrating Snowflake Native App Framework with Snowpark Container Services allows organizations to extend the breadth and variety of applications they build in the AI Data Cloud, leveraging configurable GPU and CPU instances for various use cases. Developers can develop their AI-powered Snowflake Native Apps once and deploy them across clouds and regions to thousands of customers through Snowflake Marketplace.


Future Trends and Challenges in Data Collaboration and Governance

Looking ahead, Krishnan identified key trends and challenges in data collaboration and governance. "AI is exciting, but when it comes to enterprise AI, it needs to be governed, efficient, and easy. That's the challenge clients are dealing with, and that's the opportunity where Snowflake is positioned to make that possible," Krishnan emphasized.


Conclusion

Day two of the Snowflake Summit 2024 has showcased the potential of the AI Data Cloud in transforming industries, unlocking new possibilities, and democratizing enterprise data. With advancements in AI, data collaboration, and developer tools, Snowflake is empowering enterprises to harness the full power of their data and drive innovation at an unprecedented scale. As organizations embrace the era of enterprise AI, Snowflake stands at the forefront, providing the foundation for a data-driven future.

Comments


bottom of page