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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

Why is data categorization important for data governance and compliance?
Effective data categorization is essential for data governance and compliance because it helps identify sensitive data like PII, ensuring the correct protection policies are applied. With Sifflet’s classification tags, governance teams can easily locate and safeguard sensitive information, supporting GDPR data monitoring and overall data security compliance.
What strategies can help smaller data teams stay productive and happy?
For smaller teams, simplicity and clarity are key. Implementing lightweight data observability dashboards and using tools that support real-time alerts and Slack notifications can help them stay agile without feeling overwhelmed. Also, defining clear roles and giving access to self-service tools boosts autonomy and satisfaction.
How does Sifflet help Adaptavist detect issues before they impact stakeholders?
Sifflet enables real-time metrics and data freshness checks that surface anomalies before they escalate. With features like alerting, lineage tracking, and pre-prod validation, teams at Adaptavist can spot and fix problems early, reducing surprise outages and improving SLA compliance.
Can non-technical users benefit from Sifflet’s data observability platform?
Absolutely. Sifflet is designed to be accessible to everyone. With an intuitive UI and our AI Assistant, even non-technical users can set up data quality monitors, track real-time metrics, and contribute to data governance without writing a line of code.
How can I monitor data freshness proactively instead of reacting to problems?
You can use a mix of threshold-based alerts, machine learning for anomaly detection, and visual freshness indicators in your BI tools. Pair these with data lineage tracking and root cause analysis to catch and resolve issues quickly. A modern data observability platform like Sifflet makes it easy to set up proactive monitoring tailored to your business needs.
How does Sifflet ensure data security within its data observability platform?
At Sifflet, data security is built into the foundation of our data observability platform. We follow three core principles: least privilege, no storage, and single tenancy. This means we only use read-only access, never store your data, and isolate each customer’s environment to prevent cross-tenant access.
What practical steps can companies take to build a data-driven culture?
To build a data-driven culture, start by investing in data literacy, aligning goals across teams, and adopting observability tools that support proactive monitoring. Platforms with features like metrics collection, telemetry instrumentation, and real-time alerts can help ensure data reliability and build trust in your analytics.
Why is data observability becoming such a priority for enterprises in 2025?
Great question! As more organizations rely on AI and analytics for decision-making, ensuring data quality, health, and reliability has become non-negotiable. Data observability platforms like Sifflet help teams detect issues early, reduce downtime, and maintain trust in their data pipelines.

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