Snowflake Delivers the Foundation for Autonomous Data Engineering, Powering the Agentic Enterprise with Trusted, Live Data

Snowflake Delivers the Foundation for Autonomous Data Engineering, Powering the Agentic Enterprise with Trusted, Live Data

With new Snowflake innovations, AI agents help keep data pipelines healthy and businesses get live data they can act on

• The Blueprint for Self-Healing Pipelines, With Engineers in Control: CoCo Cloud Agents and CoCo Automations take on recurring pipeline fixes, with engineers providing guidelines and guardrails
• From Reactive Reporting to Proactive Action: Snowflakedelivers end-to-end, real-time analytics capabilities to power dashboards, apps, and AI agents
• More Data, Ready for AI: Teams can move Apache Spark™ workloads onto Snowflake with fewer code changes, so more data is governed and available to AI

Snowflake (NYSE: SNOW), the AI Data Cloud company, today at Snowflake Expedition 2026 announced new autonomous data engineering and real-time analytics innovations that help enterprises build and run the trusted data foundation AI agents rely on. Autonomous data engineering puts AI agents to work on the recurring tasks of building, running, and fixing data pipelines, with engineers deciding what goes into production. With new capabilities in Snowflake CoCo, the coding agent where you build faster, data teams can build autonomous workflows to tackle recurring, repetitive tasks like repairing pipelines. End-to-end, real-time analytics capabilities keep pipelines fed with live data from across the business, with governance controls applied from the moment it lands and everywhere it’s used.

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With new Snowflake innovations, AI agents help keep data pipelines healthy and businesses get live data they can act on

As companies hand more decisions to AI agents, the need for trusted data powering those agents matters more than ever. If an agent works with yesterday's numbers or with a pipeline that is broken, it will act on the wrong information. At the speed agents operate and the rate businesses are growing, those mistakes add up fast. Yet for many enterprises, that data still depends on hand-written pipelines and streaming systems pieced together from several tools. Data teams spend a lot of their time just keeping it all running. Snowflake's latest capabilities help enterprises scale AI with confidence. Agents act on live, governed data, decisions keep pace with the business, and data teams can take on more of what their organization actually needs.

“Every AI success story has a data team behind it,” said Christian Kleinerman, EVP of Product, Snowflake. “However, even as AI scales across the enterprise, those teams are still keeping data accurate largely by hand. With data volumes continuing to skyrocket and agents taking on more of the work, that approach won't hold. Snowflake is bringing autonomous agents into data engineering and real-time data onto one governed platform, so data teams can stop holding the foundation together and spend more time building on it.”

Putting Agents to Work on Recurring Data Tasks, So Teams Can Focus on Business Outcomes

At the pace AI is scaling, data engineers can't afford to spend their days keeping pipelines running. Most data teams already use AI coding assistants, but those tools typically only speed up one engineer's work on one laptop, and they don't know how a company's data connects or which rules apply to it.

With CoCo Cloud Agents1, teams can run CoCo in an isolated cloud environment that Snowflake manages, instead of on an engineer's laptop. Each session gets its own secure container, works on files in a shared Snowflake Workspace, and a data engineer can further restrict or limit what it can do with CoCo Restricted Session Scope1. Plus, because the work happens in the cloud, it can keep going after the engineer logs off, supporting long-running tasks and cross-team collaboration.

CoCo Automations1 allow data teams to run those agents on a schedule. Data engineers can describe a recurring task in plain language, like checking pipeline health or investigating failed jobs, and choose how often it runs. Teams can share CoCo Skills they have built, so a workflow one engineer creates can be reused across the team. Because it runs inside Snowflake, it works with an organization's lineage, catalog, and governance policies, giving it the context that a general-purpose coding assistant doesn't have.

When a scheduled run finds a problem in a pipeline, CoCo can trace the likely cause and propose a fix for engineers to review before they decide what goes to production. Snowflake for Slack3 brings CoCo into the channels where teams already work, so they can ask questions, start a workflow or review results without leaving the thread. With the CoCo GitHub Integration2, proposed code changes still go through the team's existing review process.

Together, these innovations lay the foundation for autonomous data engineering, allowing teams to create, deploy, and manage CoCo-powered agents that automatically detect and repair issues with pipelines, alerting engineers once the fix is ready for approval.

Keeping existing pipelines running is only half the job. As AI scales, teams also have to build new pipelines faster. With Snowflake Pipeline Builder2, teams can orchestrate pipelines by connecting steps in a visual editor in Snowflake Workspaces, or ask CoCo to do it for them. Companies such as auxmoney and New American Funding are already using Pipeline Builder to get their pipelines into production faster, and with fewer errors.

“As a leading digital-lending platform for consumer credit in Europe, our risk and analytics teams depend on data that is accurate and well governed,” said Marius Garbowski, Vice President, Data & Analytics, auxmoney. “We've been bringing more of that work onto Snowflake, so it runs where our data already lives, and Pipeline Builder helps us connect the steps our teams build into end-to-end pipelines, so new insights improve our models and reach the business sooner. Instead of piecing together our own tooling, we can rely on Snowflake to provide a robust foundation, allowing our teams to focus even more on data science and models that move our business forward.”

“Snowflake has changed what our data team can deliver for the business,” said Dian Xu, VP, Engineering, New American Funding. “Snowflake CoCo has taken on much of the heavy lifting as we modernize hundreds of legacy pipelines and reports. Pipeline Builder is taking it a step further to give us a consistent way to turn that work into pipelines we can rely on every day. This allows our engineers to spend more time on the products that help borrowers and loan officers.”

End-to-End Real-Time Analytics on One Governed Platform, So Enterprises Can Act Faster

Businesses increasingly need to act on what is happening right now, whether that means keeping a sales dashboard current throughout the day or making sure customers see accurate account information in an app. Getting live data has usually meant adding several separate tools, each with its own cost and security rules. Over time, that adds up to higher bills and teams working from numbers that don't match.

Snowflake’s real-time analytics capabilities deliver a path for live data, from capturing it to putting it to work on one governed platform. Apps and devices can send data straight into Snowflake through Snowpipe Streaming, which scales automatically as volume grows with Elastic Channels1. With Snowflake Datastream3, businesses already using Apache Kafka®5 can simply point their data-generating apps straight into Snowflake, eliminating the need to manage brokers or connectors. This allows organizations like Minted, Nexon, and Zywave to shift their focus from maintaining cluster infrastructure to building secure, governed data assets. As data arrives, Streaming Transformations4 let teams transform it in motion with standard SQL, so downstream data stays fresh and is ready for analytics and apps in seconds. Data engineers can build and run every step from one place in CoCo by simply describing what they need in plain language.

“Virtually every Minted order is personalized by the customer, from design to wording to paper choices, and each one generates a steady stream of events, including proofing, production, and delivery. Our teams need that data the moment it's created,” said Justin Boggs, Vice President, Data & Analytics, Minted. “We’re working closely with Snowflake as an early design partner to help shape Datastream, as we streamline how we ingest data across our many services. Our goal is for our existing Kafka streams to flow directly into Snowflake, with less infrastructure to manage and faster access to data for teams across Minted.”

“Millions of players interact with our games every day, and what they do guides every decision we make, from game design to live operations,” said Dukwon Kang, Head of AI Division, Nexon. “Snowflake is the platform where we bring our scattered data together. Since adopting AI analytics on Snowflake, we have made decisions 65% faster, and this is changing how we run our games across the board. We also load unstructured business data into Snowflake, not just game logs, so AI can use it as context. This makes it much easier for our teams to respond quickly to what players are doing and keep improving their experience.”

Interactive Analytics1 is where that live data goes to work. Many companies run a separate serving database just to power customer-facing dashboards and apps, because their main analytics platform slows down when large numbers of users and agents query it at once. Interactive Analytics serves fast, real-time answers to those apps and AI agents directly from Snowflake with sub-second latency, even at peak demand. There's no extra system to pay for or keep in sync. Data is stored once and governed from the first event onwards, so costs stay predictable as usage grows and every person and agent works from the same trusted copy. Today, businesses can stream up to 20 GB of data per second into Snowflake using its real-time analytics capabilities, with data ready to use in a median time of three seconds6.

Customers including Cboe Global Markets are using Snowflake's real-time analytics capabilities to replace costly standalone systems and act on live data at scale.

“Our global trading markets run on massive volumes of data. Our participants expect it to be accurate and available almost instantly, when they need it,” said Kevin Knapp, Senior Director Software Engineering, Cboe Global Markets. “Our teams used to wait until the end of the day to analyze and deliver actionable reporting on market data because our legacy system moved it in batches. With Snowpipe Streaming, we now bring almost 200 billion rows of market data into Snowflake every day, and our teams can query it within seconds or minutes. Our internal teams having real-time access to market insights and customer trading behavior, on a Snowflake platform that has proven to deliver cloud scalability and reliability, is critical to our business operations.”

A Simpler Path to AI-Ready Data, Wherever It Lives

For teams that run on Apache Spark™7, Snowpark Connect works with Apache Iceberg®8 tables in Snowflake or external catalogs1, and now supports DataFrame Writer V2 APIs and flexible partitioning2, so existing Spark pipelines that write to Iceberg tables can move to Snowflake with fewer code changes. Spark users can also load files directly into Snowflake with the File reader for Apache Spark3, unlocking fast processing for JSON, CSV, and Parquet files without having to manage external clusters. With Write-Audit-Publish on Iceberg tables2, Spark users can safely create agentic workloads or test new pipelines on a data branch to validate downstream impact before pushing it to production. CoCo can also review existing Spark code and rewrite it to run on Snowflake¹, taking much of the manual work out of migrating. Once those jobs are running, Monitoring UI2 lets teams see how each one is performing and quickly find the source of any issue.

“Moving our heavy Spark workloads to Snowflake cut our total cost of ownership by 60%,” said Kumar Maddali, VP Engineering and AI, Klarent. “Our longest pipeline went from more than 10 hours to under three, and our engineers no longer spend their time keeping clusters running. We now have a simpler, faster foundation for our data, and our team can focus on building.”

Snowflake’s innovations remove one of the biggest obstacles standing between enterprises and AI at scale. When pipeline issues are detected automatically, engineers approve the fix, and live data arrives already governed, AI projects no longer stall waiting for data. Data teams can focus on the initiatives the business is waiting for, and enterprises can move more agents from pilot to production knowing every one of them is working from data they can trust.

Learn More:

• Read more about how Snowflake is helping enterprises scale intelligence efficiently with AI-ready data in this blog post.
• Dive into how Snowflake helps organizations stream, process, and analyze real-time data on a single platform in this blog post.
• Learn more about how Snowflake is building agents to monitor failures, diagnose issues, and suggest fixes autonomously in this blog post.
• Stay on top of the latest news and announcements from Snowflake on LinkedIn and X, and follow along at #SnowflakeExpedition.

1 Snowflake product is generally available.
2 Snowflake product is in public preview.
3 Snowflake product is in public preview soon.
4 Snowflake product is in private preview.
5 Apache Kafka is an open-source distributed event streaming platform. “Apache” is a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
6 Throughput of up to 20 GB/s is the supported aggregate rate of uncompressed data per table with Snowpipe Streaming. The median (P50) data-readiness latency of 3 seconds was measured in Snowflake internal testing conducted on October 20, 2026, covering the full path through Snowpipe Streaming, Streaming Transformations and Interactive Analytics.
7 Apache Spark is a fast and general engine for large-scale data processing. “Apache” is a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
8 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.

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 14,500 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).

Forward-Looking Statements

This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake’s business strategy, plans, opportunities, or priorities (ii) Snowflake’s products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, (iv) Snowflake’s vision, strategy, and expected benefits relating to artificial intelligence and other emerging product areas, and (v) the integration, interoperability, and availability of Snowflake’s products, services, and technology offerings with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events.

© 2026 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s).

Media Contacts:
Caroline McInerney
Product PR Lead, Snowflake
press@snowflake.com

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