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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
Still have a question in mind ?
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Frequently asked questions

Can data lineage help with regulatory compliance such as GDPR?
Absolutely. Data lineage supports data governance by mapping data flows and access rights, which is essential for compliance with regulations like GDPR. Features like automated PII propagation help teams monitor sensitive data and enforce security observability best practices.
What does 'observability culture' mean at Adaptavist?
For Adaptavist, observability culture means going beyond tools. It's about clear ownership of alerts, integrating data quality monitoring into sprints, and giving stakeholders ways to provide feedback directly in dashboards. They even track observability metrics to continuously improve their own observability practices.
Why is investing in data observability important for business leaders?
Great question! Investing in data observability helps organizations proactively monitor the health of their data, reduce the risk of bad data incidents, and ensure data quality across pipelines. It also supports better decision-making, improves SLA compliance, and helps maintain trust in analytics. Ultimately, it’s a strategic move that protects your business from costly mistakes and missed opportunities.
What role does real-time data play in modern analytics pipelines?
Real-time data is becoming a game-changer for analytics, especially in use cases like fraud detection and personalized recommendations. Streaming data monitoring and real-time metrics collection are essential to harness this data effectively, ensuring that insights are both timely and actionable.
What’s next for Sifflet’s metrics observability capabilities?
We’re expanding support to more BI and transformation tools beyond Looker, and enhancing our ML-based monitoring to group business metrics by domain. This will improve consistency and make it even easier for users to explore metrics across the semantic layer.
What kind of visibility does Sifflet provide for Airflow DAGs?
Sifflet offers a clear view of DAG run statuses and their potential impact on the rest of your data pipeline. Combined with data lineage tracking, it gives you full transparency, making root cause analysis and incident response much easier.
What are some of the latest technologies integrated into Sifflet's observability tools?
We've been exploring and integrating a variety of cutting-edge technologies, including dynamic thresholding for anomaly detection, data profiling tools, and telemetry instrumentation. These tools help enhance our pipeline health dashboard and improve transparency in data pipelines.
How does Sifflet’s dbt Impact Analysis improve data pipeline monitoring?
By surfacing impacted tables, dashboards, and other assets directly in GitHub or GitLab, Sifflet’s dbt Impact Analysis gives teams real-time visibility into how changes affect the broader data pipeline. This supports better data pipeline monitoring and helps maintain data reliability.

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