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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.

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.

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.

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 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
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 Sifflet support real-time data lineage and observability?
Sifflet provides automated, field-level data lineage integrated with real-time alerts and anomaly detection. It maps how data flows across your stack, enabling quick root cause analysis and impact assessments. With features like data drift detection, schema change tracking, and pipeline error alerting, Sifflet helps teams stay ahead of issues and maintain data reliability.
Why is full-stack visibility important in data pipelines?
Full-stack visibility is key to understanding how data moves across your systems. With a data observability tool, you get data lineage tracking and metadata insights, which help you pinpoint bottlenecks, track dependencies, and ensure your data is accurate from source to destination.
Is Sifflet Insights easy to set up with my existing tools?
Yes, onboarding is seamless. You can quickly integrate Sifflet Insights with your existing BI tools and start receiving real-time metrics and alerts. It’s designed to enhance efficiency and support incident response automation without disrupting your current workflows.
Can I customize how sensitive the alerts are in Sifflet’s Freshness Monitor?
Absolutely! Sifflet lets you adjust the sensitivity of your freshness alerts based on your specific needs. Whether you're monitoring ML pipelines or business-critical dashboards, you can fine-tune how strict the system is about detecting anomalies to ensure you're only alerted when it really matters. This is a great way to optimize your incident response automation.
How do organizations monitor the success of their data governance programs?
Successful data governance is measured through KPIs that tie directly to business outcomes. This includes metrics like how quickly teams can find data, how often data quality issues are caught before reaching production, and how well teams follow access protocols. Observability tools help track these indicators by providing real-time metrics and alerting on governance-related issues.
How does data observability improve the value of a data catalog?
Data observability enhances a data catalog by adding continuous monitoring, data lineage tracking, and real-time alerts. This means organizations can not only find their data but also trust its accuracy, freshness, and consistency. By integrating observability tools, a catalog becomes part of a dynamic system that supports SLA compliance and proactive data governance.
Why should companies invest in data pipeline monitoring?
Data pipeline monitoring helps teams stay on top of ingestion latency, schema changes, and unexpected drops in data freshness. Without it, issues can go unnoticed and lead to broken dashboards or faulty decisions. With tools like Sifflet, you can set up real-time alerts and reduce downtime through proactive monitoring.
Why should organizations shift from firefighting to fire prevention in their data operations?
Shifting to fire prevention means proactively addressing data health issues before they impact users. By leveraging data lineage and observability tools, teams can perform impact assessments, monitor data quality, and implement preventive strategies that reduce downtime and improve SLA compliance.
Still have questions?

Want to try Sifflet on your Snowflake Stack?

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