Self-Driving Data for the Snowflake AI Data Cloud

Two months ago, Anomalo introduced the concept of Self-Driving Data: a new agentic platform where data monitors itself, explains what changed, and surfaces what matters without waiting for humans to ask. Our platform delivers the deepest understanding of your data automatically and turns it into confident action. At Snowflake Summit this year, the question we’re here to answer is what that platform makes possible when it’s running natively inside the most ambitious AI data platform in the enterprise. Because the announcements from Snowflake — CoWork, CoCo Desktop, and Horizon Context — all point in the same direction: autonomous workflows that act on data without waiting for human review. And every one of them inherits the quality of the data layer underneath. What your monitoring misses, your agents miss. What it catches late, they catch late. And at machine speed, being late is expensive.

Manual monitoring doesn’t work at agentic speed. Rules-based thresholds can’t catch what they weren’t written to find. And no amount of dashboards will tell you what your data means fast enough to matter when a pricing agent is already running.

This is the data problem hiding inside your intelligence layer. And at Snowflake Summit this year, we’re here to show you exactly how Anomalo closes it.

Why “Integrated” Has to Mean More Than a Marketplace Badge

Every data quality vendor at Summit will claim Snowflake integration. A native connector, a listing on the Marketplace, a partnership badge, that’s expected. What our customers want is to work where they want to work. This means having data quality signals available inside the workflows Snowflake customers actually run, not alongside them. It means quality context surfaces where decisions get made, not in a separate tool you have to go check.

Anomalo received the Snowflake Summit Partner Legacy Award, honoring 5+ years of co-building the AI Data Cloud and for playing an undeniable role in shaping what’s next.

Our Snowflake integration runs five layers deep:

Layer 1: Enriching Horizon Context with data quality signals. Horizon Context is Snowflake’s new universal agentic catalog, designed to give AI agents the complete, trusted context they need for high-fidelity answers. But a catalog is only as good as the metadata it contains. Anomalo feeds deep quality signals like distribution shifts, changes in column correlations, volume anomalies, content-level deviations, directly into Horizon Context so every agent that reasons over your data inherits not just schema and lineage, but an understanding of whether that data is actually behaving the way it should. Without it, Horizon Context tells agents what the data is. With Anomalo, it also tells them whether they should trust it.

Layer 2: The first fully containerized Native App on Snowflake Marketplace. Anomalo runs 100% inside your Snowflake account on Snowpark Container Services, at full feature parity with our connected application. Data never leaves your managed environment. Deploy in minutes via the Marketplace. Eligible for Marketplace Capacity Drawdown credits. No separate infrastructure to stand up, no security review to run twice.

Layer 3: Cortex AI support. Anomalo’s AIDA conversational analytics agent supports Cortex-hosted models as an LLM option. Customers who’ve standardized on Cortex can use it to power natural-language queries, check creation, and data exploration inside Anomalo, without routing sensitive data through an external model provider.

Layer 4: Snowflake CoWork partnership. Anomalo is a Level 2 Snowflake CoWork partner. That means our data quality context is available directly inside Snowflake CoWork agents as Cortex agent tools, via stored procedures. When an agent encounters an anomaly, Anomalo provides the root cause, cross-platform, cross-warehouse, without leaving your Snowflake environment. The loop between detection and action closes automatically.

Layer 5: Detection depth that goes beyond metadata. Most tools monitor for the obvious things, freshness, volume, schema drift. Anomalo uses AI to monitor data at the content level, across billions of rows, without rules to write or thresholds to set. The engine learns what “normal” looks like for your specific data, field distributions, metric behavior across segments, how patterns shift over time, and flags meaningful deviations automatically. It even adjusts dynamically for seasonality and natural variation, so you’re not chasing false positives every Monday morning.

Five layers. Each one deeper than the last. No other data quality vendor covers all five.

What Production Looks Like at Enterprise Scale

The test of Anomalo’s agentic data platform is what happens in production, at enterprise scale, when the stakes are real:

• At Discover, the team needed to monitor a petabyte-scale financial data environment, the kind where thousands of manually written rules still leave gaps, and a missed anomaly has real downstream consequences. Keith Toney, Chief Data and Analytics Officer, summed up why they chose Anomalo: “Their machine learning and root cause detection technology identifies late, missing, or anomalous data across our petabyte-scale cloud warehouse.” Not with rules. Not with thresholds. With an engine that learns.
• At Faire, the CDO monitors hundreds of key tables in Snowflake continuously. Daniele Perito’s measure of success is exactly the one that matters: “I sleep better at night knowing our data is more reliable, and my team loves how easy it is to use and how insightful the notifications are.” If your data leader isn’t sleeping better, the monitoring isn’t working.
• At HomeToGo, the engineering team needed monitoring that could scale with their data without creating a maintenance burden. What they found was automated, AI-driven coverage, no manual rule maintenance, no alert fatigue, no weekend escalations for things the engine should have caught. The full story is on their engineering blog.

The common thread: the shift from reactive firefighting to proactive confidence. Anomalo’s deep integration enables organizational calm that comes from knowing your data is “self-driving”.

The Bigger Picture

Cortex agents are the beginning of something larger. Every enterprise that’s serious about AI will deploy agents that consume data and act without human review. The data layer underneath those agents will determine whether the automation compounds or corrodes.

If your monitoring layer is fragile, rules-based, partially deployed, reactive by design, your agents inherit that fragility. The things you miss, they miss. The shifts you catch late, they catch late. And at machine speed, being late is expensive.

Anomalo’s position isn’t just about data quality monitoring anymore. It’s about being the trust layer that makes Snowflake’s agentic capabilities reliable at enterprise scale. The Native App keeps your data secure inside your managed environment. The AI engine catches what rules miss and adjusts for what rules can’t anticipate. The Snowflake integration makes data quality context available exactly where your agents make decisions.

Your data needs a self-driving data layer that can keep up with what Snowflake is building.

The Best Foundation for Every Snowflake Data and AI Ambition

When your monitoring layer is as autonomous as the AI applications running above it, you stop firefighting data issues and start compounding data intelligence. Anomalo’s agentic platform enables three phases, each one expanding what your Snowflake investment can do:

• Data Monitoring, Conversational data quality exposed as Cortex agent tools. Ask questions in natural language. Create monitors without writing SQL. Get root cause analysis surfaced where decisions get made.
• Data Understanding, The Data Insights Agent watches your key datasets continuously, without prompts. When something meaningful changes, a metric shift, a distribution change, an upstream pipeline anomaly, it investigates, confirms the finding, and delivers an analyst-grade report before anyone thinks to ask. The Data Documentation Agent generates and maintains comprehensive documentation from your Slack conversations, wikis, metadata, and Anomalo’s own profiling, so every table your agents consume has the context they need to use it correctly.
• Data Analytics, Natural language dashboards and reports. Business KPI monitoring that alerts you when the numbers that matter move unexpectedly. Experiment evaluation that applies correct statistical methods automatically. And AIDA, Anomalo’s Intelligent Data Analyst, as the unified conversational interface to everything.

This is what we call “agentic AI for your agentic AI”. The same autonomous capabilities that make Snowflake CoWork powerful downstream require an autonomous data layer underneath in order to ensure you have trustworthy data. Anomalo is that layer.

Find Us at Snowflake Summit

If you’re at Snowflake Summit, come find us at the Anomalo booth #1206. We’ll show you the integration live and walk you through the agentic platform. If you’re reading this after Summit: the fastest way to see the depth is to try it yourself. Get started on Snowflake Marketplace → or request a custom demo.

Backed by Snowflake Ventures. Snowflake Premier Technology Partner. Snowflake CoWork Launch Partner.

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