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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.

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
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Frequently asked questions

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.
How does Sifflet support real-time metrics and alerting within a data platform?
Sifflet collects and monitors real-time metrics like data freshness, schema changes, and volume anomalies. With dynamic thresholding and real-time alerts via Slack or email, teams can respond quickly and keep their analytics platform running smoothly.
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.
How does Sifflet use AI to improve data classification?
Sifflet leverages machine learning to provide AI Suggestions for classification tags, helping teams automatically identify and label key data characteristics like PII or low cardinality. This not only streamlines data management but also enhances data quality monitoring by reducing manual effort and human error.
What exactly is data quality, and why should teams care about it?
Data quality refers to how accurate, complete, consistent, and timely your data is. It's essential because poor data quality can lead to unreliable analytics, missed business opportunities, and even financial losses. Investing in data quality monitoring helps teams regain trust in their data and make confident, data-driven decisions.
What’s coming next for the Sifflet AI Assistant?
We’re excited about what’s ahead. Soon, the Sifflet AI Assistant will allow non-technical users to create monitors using natural language, expand monitoring coverage automatically, and provide deeper insights into resource utilization and capacity planning to support scalable data observability.
Why is a user-friendly interface important in an observability tool?
A user-friendly interface boosts adoption across teams and makes it easier to navigate complex datasets. For observability tools, especially those focused on data cataloging and data discovery, a clean UI enables faster insights and more efficient collaboration.
Can Sifflet help with root cause analysis when data issues arise?
Absolutely! Sifflet’s field-level data lineage tracking lets you trace data issues from BI dashboards all the way back to source systems. Its AI agent, Sage, even recalls past incidents to suggest likely causes, making root cause analysis faster and more accurate for data engineers and analysts alike.

Want to try Sifflet on your Snowflake Stack?

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