See the Whole Picture with Sifflet and Snowflake

Contextual Observability That Goes Beyond the Stack

Your Snowflake data powers decisions across your business, but when something breaks, it’s more than pipelines at risk. It’s dashboards, AI models, customer reporting, and trust. Sifflet brings business context into your observability layer so you can fix what matters, faster.

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Why chose Sifflet for Snowflake?

Your Snowflake data powers decisions across teams, but when quality issues strike, it’s not just pipelines that break. It’s customer experiences, revenue reporting, AI model accuracy, and more.

That’s where Sifflet stands apart.

Sifflet brings business context into the heart of data observability, so you don’t just know what’s broken, you know what matters. Our platform weaves metadata, pipeline behavior, and usage patterns into a unified map of technical and business logic, helping your team spot, triage, and resolve issues before they become downstream disasters.

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. Sifflet identifies which anomalies impact key dashboards, SLAs, or ML models, so your team focuses where it counts.

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Map Lineage with Business Logic

See how data flows across your stack, not just pipelines, but people. Sifflet combines metadata and usage patterns to show who’s using what, and why. From column to customer.

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Cut Through the Noise

Sifflet delivers context-rich alerts that combine technical symptoms with business impact. Your team gets fewer false alarms, and faster resolution.

Leverage Time Travel for Smarter Detection

Historical snapshots enhance anomaly detection with temporal intelligence.

Snowflake-specific assets

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

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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.

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Field-level lineage

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

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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

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Perfect For…

Data Leaders deploying Snowflake as the central nervous system of their organization
‍Analytics Teams needing reliable, self-serve dashboards and clear ownership
‍Governance & Risk Teams looking to enforce data quality, lineage, and auditability‍
AI & ML Teams training models on clean, explainable data they can trust

Go Further

Data Observability for your data stack with Snowflake

Discover the Core Capabilities That Ensure Data Quality in Snowflake

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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:

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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

Frequently asked questions

How does data lineage tracking help with root cause analysis in data integration?

Data lineage tracking gives visibility into how data flows from source to destination, making it easier to pinpoint where issues originate. This is essential for root cause analysis, especially when dealing with complex integrations across multiple systems. At Sifflet, we see data lineage as a cornerstone of any observability platform.

How does Kubernetes help with container orchestration?

Kubernetes makes it easier to manage large-scale containerized applications by automating deployment, scaling, and operations. It's a powerful observability tool that supports real-time metrics collection, resource utilization tracking, and pipeline orchestration visibility, helping teams stay on top of their data pipelines.

How does Sifflet help scale dbt environments without compromising data quality?

Great question! Sifflet enhances your dbt environment by adding a robust data observability layer that enforces standards, monitors key metrics, and ensures data quality monitoring across thousands of models. With centralized metadata, automated monitors, and lineage tracking, Sifflet helps teams avoid the usual pitfalls of scaling like ownership ambiguity and technical debt.

How does the Sifflet AI Assistant improve data observability at scale?

The Sifflet AI Assistant enhances data observability by automatically fine-tuning your monitoring setup using machine learning and dynamic thresholds. It continuously adapts to changes in your data pipelines, reducing false positives and ensuring accurate anomaly detection, even as your data scales globally.

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.

What is data lineage and why is it important for data observability?

Data lineage is the process of tracing data as it moves from source to destination, including all transformations along the way. It's a critical component of data observability because it helps teams understand dependencies, troubleshoot issues faster, and maintain data reliability across the entire pipeline.

What is data observability and why is it important for modern data teams?

Data observability is the ability to monitor and understand the health of your data across the entire data stack. As data pipelines become more complex, having real-time visibility into where and why data issues occur helps teams maintain data reliability and trust. At Sifflet, we believe data observability is essential for proactive data quality monitoring and faster root cause analysis.

Why is combining dbt Core with a data observability platform like Sifflet a smart move?

Combining dbt Core with a data observability platform like Sifflet helps data teams go beyond transformation and into full-stack monitoring. It enables better root cause analysis, reduces time to resolution, and ensures your data products are trustworthy and resilient.

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