Solution by Platform

Snowflake

See the Whole Picture with Sifflet and Snowflake

Your AI is only as good as the Data feeding it.

Sifflet is the control plane for Data and AI — enhanced by business context, run by humans and agents. Built for Snowflake from the ground up.

Talk to our Experts

Explore at your own pace

One control plane. The whole stack.

SEE EVERY ASSET, EVERY DEPENDENCY. Column-level lineage from your sources, through Snowflake, into every dashboard, model, and AI system that consumes the data.

THREE AGENTS ON YOUR DATA TEAM. Sentinel finds what's broken before your business does. Sage explains why — tracing root cause across Snowflake, dbt, Airflow, and your BI tools in seconds. Forge fixes it with human approval at every step.

BUILT FOR THE REGULATED ENTERPRISE. Metadata-only architecture. No data egress. SOC 2 Type II and ISO 27001. Deploy fully managed or in your VPC — or buy directly with your Snowflake committed spend.

‍

Deep Integration with Snowflake

Sifflet enhances the observability of your Snowflake stack by letting you:

Prioritize What Matters Most

Not every broken table is worth a PagerDuty alert. Sentinel auto-generates and tunes monitors from your Snowflake metadata, query patterns, and lineage — no manual rule-writing. 50+ templates covering freshness, volume, schema, distribution, format, and referential integrity.

‍

Map Lineage with Business Logic

See exactly how data flows from your sources, through Snowflake, into every dashboard and AI model that depends on it. Column-level, field-level, end-to-end. Sifflet's Declarative Assets & Lineage API tells you not just what broke, but what decision is now at risk and who needs to know.

‍

Root Cause in Seconds, Not Hours

When a Looker dashboard breaks, Sage traces it back through dbt, Airflow, and Snowflake to the actual upstream change — in seconds. Uses Snowflake Time Travel for point-in-time root cause and drift detection.

Fix It with Confidence

Forge proposes and executes remediation — schema fixes, monitor updates, alert routing, data backfills — with human-in-the-loop approval at every step. And the Sifflet MCP Server lets you query your observability layer from Claude, Cursor, or any MCP client, so your AI tools know what data is safe to use, in real time.

Snowflake-specific assets

Sifflet supports multiple Snowflake-specific objects, like streams and stages, for exhaustive coverage.

‍

Usage and Snowflake metadata

Get detailed statistics about the usage of your Snowflake assets, in addition to various metadata (like tags, descriptions, and table sizes) retrieved directly from Snowflake.

‍

Field-level lineage

Have a detailed understanding of how data flows through your platform via field-level end-to-end lineage for Snowflake.

‍

Built for Modern Data Teams on Snowflake

Trusted by Snowflake-Centric Enterprises Across Europe and the U.S.
Native integration with Snowflake’s metadata and query engine
Designed for scale, trust, and business alignment

“With Sifflet, we don’t just detect anomalies in Snowflake. We understand their real-world impact, and we act before anyone downstream even notices.”
Head of Data Governance, European Retail Leader

‍

Perfect For…

Data Engineers — stop firefighting. Sentinel monitors automatically, Sage finds root cause fast.
Data Leaders — full visibility from sources to AI models. Know which decisions are at risk before the business does.
Governance & Risk Teams — metadata-only architecture, SOC 2 Type II, ISO 27001, no data egress.
AI & ML Teams — your AI is only as good as the data feeding it. Sifflet is the trust layer between your Snowflake data and your models.

Go Further

Data Observability for your data stack with Snowflake

Discover the Core Capabilities That Ensure Data Quality in Snowflake

Read Now

Do You need Data Observability?

Every data-driven team dreams of creating "fireworks" with their insights. But without control, the "fire" of the modern data stack can spark chaos.Discover if you're fighting fires or igniting fireworks:

Take the quizz

Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

Still have a question in mind ?

Frequently asked questions

How does Sifflet's Data Sharing feature help with enforcing data governance policies?

Great question! Sifflet's Data Sharing provides access to rich metadata about your data assets, including tags, owners, and monitor configurations. By making this available in your own data warehouse, you can set up automated checks to ensure compliance with your governance standards. It's a powerful way to implement scalable data governance and reduce manual audits using our observability platform.

Is this integration useful for teams focused on data governance and compliance?

Yes, it really is! With enhanced lineage and metadata tracking from source to destination, the Fivetran integration supports better data governance. It helps ensure transparency, traceability, and SLA compliance across your data ecosystem.

How does Sifflet help reduce AI bias and improve model fairness?

Reducing AI bias starts with understanding your data. Sifflet’s observability platform gives you deep visibility into data sources, transformations, and quality. By tracking data lineage and applying data profiling, teams can identify and correct biased inputs before they affect model outcomes. This transparency helps build more ethical and reliable AI systems.

What benefits does end-to-end data lineage offer my team?

End-to-end data lineage helps your team perform accurate impact assessments and faster root cause analysis. By connecting declared and built-in assets, you get full visibility into upstream and downstream dependencies, which is key for data reliability and operational intelligence.

How do the four pillars of data observability help improve data quality?

The four pillars—metrics, metadata, data lineage, and logs—work together to give teams full visibility into their data systems. Metrics help with data profiling and freshness checks, metadata enhances data governance, lineage enables root cause analysis, and logs provide insights into data interactions. Together, they support proactive data quality monitoring.

What’s a real-world example of Dailymotion using real-time metrics to drive business value?

One standout example is their ad inventory forecasting tool. By embedding real-time metrics into internal tools, sales teams can plan campaigns more precisely and avoid last-minute scrambles. It’s a great case of using data to improve both accuracy and efficiency.

What is the difference between data monitoring and data observability?

Great question! Data monitoring is like your car's dashboard—it alerts you when something goes wrong, like a failed pipeline or a missing dataset. Data observability, on the other hand, is like being the driver. It gives you a full understanding of how your data behaves, where it comes from, and how issues impact downstream systems. At Sifflet, we believe in going beyond alerts to deliver true data observability across your entire stack.

What makes Sifflet’s approach to data observability unique?

Our approach stands out because we treat data observability as both an engineering and organizational concern. By combining telemetry instrumentation, root cause analysis, and business KPI tracking, we help teams align technical reliability with business outcomes.

Read all FAQs

Want to try Sifflet on your Snowflake Stack?

Get in touch Now

I want to Try