Snowflake
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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.

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.

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
Dynex Capital
Euronext
Dailymotion
Saint-Gobain
ShopBack
Servier
Penguin Random House
Adaptavist
Mollie
Hypebeast
Deuna
BBC Studios
Carrefour
Etam
Auchan
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Frequently asked questions

How can organizations choose the right observability tools for their data stack?
Choosing the right observability tools depends on your data maturity and stack complexity. Look for platforms that offer comprehensive data quality monitoring, support for both batch and streaming data, and features like data lineage tracking and alert correlation. Platforms like Sifflet provide end-to-end visibility, making it easier to maintain SLA compliance and reduce incident response times.
Can Sifflet help with root cause analysis in complex data systems?
Absolutely! In early 2025, we're rolling out advanced root cause analysis tools designed to help you detect subtle anomalies and trace them back to their source. Whether the issue lies in your code, data, or pipelines, our observability platform will help you get to the bottom of it faster.
Why is data observability essential for building trusted data products?
Great question! Data observability is key because it helps ensure your data is reliable, transparent, and consistent. When you proactively monitor your data with an observability platform like Sifflet, you can catch issues early, maintain trust with your data consumers, and keep your data products running smoothly.
How does Sifflet help with root cause analysis and incident resolution?
Sifflet provides advanced root cause analysis through complete data lineage and AI-powered anomaly detection. This means teams can quickly trace issues across pipelines and transformations, assess business impact, and resolve incidents faster with smart, context-aware alerts.
How does Sifflet help with root cause analysis in data pipelines?
Sifflet uses AI-powered agents that continuously analyze metadata and behavioral patterns across your stack. When issues arise, these agents perform root cause analysis by tracing data lineage and identifying where problems originated, making it easier for teams to resolve incidents quickly and confidently.
How does data observability support MLOps and AI initiatives at Hypebeast?
Data observability plays a key role in Hypebeast’s MLOps strategy by monitoring data quality from ML models before it reaches dashboards or decision systems. This ensures that AI-driven insights are trustworthy and aligned with business goals.
How does Flow Stopper improve data reliability for engineering teams?
By integrating real-time data quality monitoring directly into your orchestration layer, Flow Stopper gives Data Engineers the ability to stop the flow when something looks off. This means fewer broken pipelines, better SLA compliance, and more time spent on innovation instead of firefighting.
What makes observability scalable across different teams and roles?
Scalable observability works for engineers, analysts, and business stakeholders alike. It supports telemetry instrumentation for developers, intuitive dashboards for analysts, and high-level confidence signals for executives. By adapting to each role without adding friction, observability becomes a shared language across the organization.

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