Snowflake Delivers the Enterprise Lakehouse with Enhanced Open Data Access and Flexibility for Agentic AI

• New advancements to Snowflake Horizon Catalog offer thousands of customers and partners, including Merkle and RelationalAI, the universal AI catalog that provides context and governance for AI across all data
• Businesses can integrate their entire data ecosystem for agentic AI with Snowflake Openflow, and deliver immediate, interactive insights with Interactive Tables and Warehouses
• Snowflake is making it even easier to consolidate infrastructure and build agentic AI with Snowflake Postgres, bringing developers’ preferred database to the platform enterprises trust

No-Headquarters/BOZEMAN, Mont.--(BUSINESS WIRE)-- Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced innovations that make it easier for organizations to ingest, access, and govern their data across the entire data lifecycle, redefining the enterprise lakehouse for the AI era. New advancements to Snowflake Horizon Catalog and Snowflake Openflow (now generally available) enable enterprises to connect all of their data from disparate sources and catalogs, making it accessible for AI agents to drive value — all with consistent, built-in security and governance. Snowflake is also redefining how enterprises can build and power AI agents and apps by helping organizations turn data into immediate insights and near real-time experiences with Interactive Tables and Warehouses (generally available soon), as well as enabling customers to leverage real-time transactional data faster and more efficiently with Snowflake Postgres (public preview soon).

New advancements to Snowflake Horizon Catalog offer thousands of customers and partners, including Merkle and RelationalAI, the universal AI catalog that provides context and governance for AI across all data

“The enterprise lakehouse represents the evolution of how organizations manage and activate data for AI,” said Christian Kleinerman, EVP of Product, Snowflake. “With advancements to Horizon Catalog, we’re giving enterprises context and governance for AI across all their data by default — wherever it lives, and without vendor lock-in. Coupled with Openflow and Snowflake Postgres, it’s now even easier for customers to connect and use their data securely, turning every dataset into the fuel for intelligence.”

“In the marketing industry, data is everything — and at Merkle, protecting that data and our customers’ privacy is paramount,” said Peter Rogers, EVP, Head of Data and Technology, Americas, Merkle. “With Snowflake’s Horizon Catalog, built from the ground up with security and governance at its core, we can ensure customer data, including sensitive identity information, is handled responsibly while safeguarding customer trust.”

Expanding Interoperability Across the Enterprise Lakehouse

Organizations struggle with AI readiness due to fragmented governance and siloed data systems. In fact, a recent study found that 80% of IT leaders point to data silos as the primary obstacle to building successful AI strategies¹. Horizon Catalog addresses these challenges by providing context for AI and a unified security and governance framework that secures and connects data across every region, cloud, and format — all interoperable and without vendor lock-in. It’s designed to work seamlessly with any engine, any data format, and from anywhere — spanning native Snowflake objects, open table formats like Apache Iceberg™² and Delta Lake, and even data stored in relational databases such as SQL Server and Postgres.

By bringing open APIs from Apache Polaris™ (Incubating)³ and Apache Iceberg™ REST Catalog⁴ directly into Horizon Catalog, Snowflake now provides customers with the de facto enterprise lakehouse — centralizing governance, security, and interoperable access management across their data in open table formats. These advancements enable external engines to securely access data (public preview soon) in Apache Iceberg tables, as well as create, update, or manage data stored in Iceberg tables (private preview soon). Organizations now gain advanced flexibility, allowing teams to securely use their preferred engines on a single copy of data, making it easier to share, connect, and activate that data from a universal AI catalog. Snowflake’s latest integration offers customers and partners like Merkle and RelationalAI the freedom to confidently use the best engines and tools for their specific business needs across a single point of governance. Snowflake is also enhancing data resilience with Business Continuity and Disaster Recovery (now in public preview) for managed Iceberg tables, further safeguarding enterprises’ critical data across the entire enterprise lakehouse.

With Openflow, enterprise users can securely automate data integration and ingestion from virtually any source, making it easier to keep data centralized across the enterprise lakehouse. Hundreds of customers including Brightfire, EVgo, and Intelitics already utilize Openflow to unify their data across various types and formats, so they can rapidly deploy AI-powered innovations. Snowflake is also expanding integration options through its partnership with Oracle (now in private preview), enabling customers to harness near real-time change data capture built on Openflow to continuously stream transactional updates into the Snowflake AI Data Cloud.

Powering Immediate Insights and Near Real-Time Experiences

As AI raises expectations for speed and interactivity, today’s organizations are under pressure to deliver immediate, data-driven experiences. To meet this growing demand, Snowflake is extending its leadership in data performance across all data with the introduction of Interactive Tables and Interactive Warehouses. Providing low-latency and high-concurrency, this new advancement makes analytics feel instantaneous, enabling teams to uncover insights in sub-seconds, not minutes. Now, enterprises have the power to work with live data immediately across their business intelligence tools, powering fast, intelligent apps and AI agents — all under Snowflake’s unified and governed platform. Interactive Tables and Warehouses move customers beyond traditional, batch analytics to deliver truly interactive experiences, without the burden of managing complex infrastructure overhead.

Building on this foundation, Snowflake is introducing near real-time streaming analytics (now in private preview), enabling organizations to act on live data within seconds, using the familiar tools and secure platform they already trust. With built-in support for leading data streams like Kafka, Kinesis, and other sources, customers can now combine live data with historical context to power mission-critical use cases like fraud detection, personalization, recommendations, observability, and IoT monitoring. Whether modernizing existing analytics or launching new near real-time services, Snowflake customers now gain an end-to-end solution for immediate insights and streaming intelligence across all their data.

Delivering Enterprise-Grade Capabilities that Fuel AI Agents and Apps

Following Snowflake’s recent acquisition of Crunchy Data, the company has introduced Snowflake Postgres, a fully-managed service that brings the world’s most popular database onto the Snowflake platform. The separation of transactional data in Postgres from analytical data has long been a major architectural roadblock for enterprises, forcing costly data movement and preventing real-time data access for apps and AI agents. Snowflake Postgres changes this by extending support to transactional, hybrid, and analytical workloads natively on the Snowflake platform. Now, Snowflake is bringing the Postgres database and ecosystem developers love to the platform enterprises trust. With transactional Postgres data within the same secure foundation as enterprises’ analytics and AI, Snowflake is progressing the enterprise lakehouse beyond insight into action — helping organizations build AI agents and intelligent apps on operational data.

Snowflake is also open sourcing pg_lake (now generally available), a set of Postgres extensions designed to help developers and data engineers integrate Postgres with a powerful lakehouse system. With pg_lake, developers can directly query, manage, and write to Apache Iceberg tables using standard SQL — all from their familiar Postgres environment.

Building on these innovations, Snowflake is continuing to bring transactional and analytical workloads together through major advancements in Snowflake Unistore, powered by Hybrid Tables. Now generally available on Microsoft Azure, Hybrid Tables empower organizations to simplify their data management and build lightweight transactional apps on Snowflake. And to ensure these workloads meet the stringent security needs of modern enterprises, Snowflake is introducing enhanced security capabilities for Hybrid Tables — including Tri-Secret Secure support (now generally available), an extra layer of protection with a customer-managed key, and periodic rekeying (now generally available) to strengthen data protection and help organizations meet regulatory requirements.

Learn More:

• Dive deeper into how Snowflake is reimaging the enterprise lakehouse to build agentic AI at scale in this blog post.
• Learn how to get started with Snowflake Openflow with this Quickstart.
• Learn how to get started with Interactive Tables and Warehouses with this Quickstart.
• Check out all the innovations and announcements coming out of BUILD 2025 on Snowflake’s Newsroom.
• Stay on top of the latest news and announcements from Snowflake on LinkedIn and X, and follow along at #SnowflakeBUILD.

¹ MuleSoft's 2025 Connectivity Benchmark Report. MuleSoft’s 10th annual Connectivity Benchmark Report, in collaboration with Vanson Bourne and Deloitte Digital, is based on survey data from interviews with 1,050 IT leaders across the globe.
² Apache Iceberg is a high-performance format for huge analytic tables. “Apache” is a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
³ Polaris Catalog, a vendor-neutral, open catalog implementation for Apache Iceberg — the open standard of choice for implementing data lakehouses, data lakes, and other modern architectures.
⁴ Iceberg defines a REST-based Catalog API for managing table metadata and performing catalog operations. The REST catalog protocol is a common API (using the OpenAPI spec) for interacting with any Iceberg catalog.

Forward Looking Statements

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About Snowflake

Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 12,000 customers around the globe, including hundreds of the world’s largest companies, use Snowflake’s AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE: SNOW).

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Source: Snowflake Inc.