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
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

Why is data distribution such an important part of data observability?
Great question! Data distribution gives you insight into the shape and spread of your data values, which traditional monitoring tools often miss. While volume, schema, and freshness checks tell you if the data is present and structured correctly, distribution monitoring helps you catch hidden issues like skewed categories or outlier spikes. It's a key component of any modern observability platform focused on data reliability.
How does Sifflet support AI readiness within enterprises?
Sifflet reinforces AI-powered capabilities through features like data freshness checks, data profiling, and anomaly scoring. These tools ensure your data is accurate and trustworthy, which is crucial for training reliable machine learning models and enabling predictive analytics monitoring.
What kind of real-time metrics can platforms like Sifflet or Monte Carlo provide that Metaplane doesn’t?
Platforms like Sifflet and Monte Carlo offer real-time metrics on ingestion latency, data freshness, and anomaly detection across your stack. They also provide telemetry instrumentation and dynamic thresholding, which help surface issues faster and with more context than Metaplane’s basic statistical profiling.
What makes Sifflet's approach to data quality unique?
At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.
Is there a data observability platform that supports both business and technical users?
Yes, Sifflet is designed to be accessible for both business stakeholders and data engineers. It offers intuitive interfaces for no-code monitor creation, context-rich alerts, and field-level data lineage tracking. This democratizes data quality monitoring and helps teams across the organization stay aligned on data health and pipeline performance.
What future observability goals has Carrefour set?
Looking ahead, Carrefour plans to expand monitoring to more than 1,500 tables, integrate AI-driven anomaly detection, and implement data contracts and SLA monitoring to further strengthen data governance and accountability.
How can I track the success of my data team?
Define clear success KPIs that support ROI, such as improvements in SLA compliance, reduction in ingestion latency, or increased data reliability. Using data observability dashboards and pipeline health metrics can help you monitor progress and communicate value to stakeholders. It's also important to set expectations early and maintain strong internal communication.
Can non-technical users benefit from Sifflet’s Data Catalog?
Yes, definitely! Sifflet is designed to be user-friendly for both technical and business users. With features like AI-driven description recommendations and easy-to-navigate asset pages, even non-technical users can confidently explore and understand the data they need.

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