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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
Still have a question in mind ?
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Frequently asked questions

Can Sifflet integrate with our existing data tools and platforms?
Absolutely! Sifflet is designed to integrate seamlessly with your current stack. We support a wide range of tools including Airflow, Snowflake, AWS Glue, and more. Our goal is to provide complete pipeline orchestration visibility and data freshness checks, all from one intuitive interface.
What makes Datadog and Splunk suitable for real-time data observability?
Both Datadog and Splunk excel at real-time telemetry instrumentation. They capture logs, metrics, and traces across applications, pipelines, and infrastructure. This real-time detection and unified observability platform make them great for environments where data reliability depends on fast incident detection and root cause analysis.
What’s the difference between data distribution and data lineage tracking?
Great distinction! Data distribution shows you how values are spread across a dataset, while data lineage tracking helps you trace where that data came from and how it’s moved through your pipeline. Both are essential for root cause analysis, but they solve different parts of the puzzle in a robust observability platform.
How does reverse ETL improve data reliability and reduce manual data requests?
Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.
What is the Model Context Protocol (MCP), and why is it important for data observability?
The Model Context Protocol (MCP) is a new interface standard developed by Anthropic that allows large language models (LLMs) to interact with tools, retain memory, and access external context. At Sifflet, we're excited about MCP because it enables more intelligent agents that can help with data observability by diagnosing issues, triggering remediation tools, and maintaining context across long-running investigations.
What exactly is the modern data stack, and why is it so popular now?
The modern data stack is a collection of cloud-native tools that help organizations transform raw data into actionable insights. It's popular because it simplifies data infrastructure, supports scalability, and enables faster, more accessible analytics across teams. With tools like Snowflake, dbt, and Airflow, teams can build robust pipelines while maintaining visibility through data observability platforms like Sifflet.
Can I see the health of my entire data pipeline in one place?
Absolutely! Sifflet’s Asset Page gives you a full view of your data pipeline monitoring, including table uptime, monitor coverage, and custom health scores. It’s a powerful dashboard for tracking pipeline resilience and making informed decisions with confidence.
Why are traditional data catalogs no longer enough for modern data teams?
Traditional data catalogs focus mainly on metadata management, but they don't actively assess data quality or track changes in real time. As data environments grow more complex, teams need more than just an inventory. They need data observability tools that provide real-time metrics, anomaly detection, and data quality monitoring to ensure reliable decision-making.

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