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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

Which ingestion tools work best with cloud data observability platforms?
Popular ingestion tools like Fivetran, Stitch, and Apache Kafka integrate well with cloud data observability platforms. They offer strong support for telemetry instrumentation, real-time ingestion, and schema registry integration. Pairing them with observability tools ensures your data stays reliable and actionable across your entire stack.
How does Flow Stopper support root cause analysis and incident prevention?
Flow Stopper enables early anomaly detection and integrates with your orchestrator to halt execution when issues are found. This makes it easier to perform root cause analysis before problems escalate and helps prevent incidents that could affect business-critical dashboards or KPIs.
Can observability tools help with GDPR-related incident response?
Absolutely! Observability tools can support GDPR compliance by enabling faster incident response automation. If there's a data breach, you need to notify users and authorities within 72 hours. Real-time alerts, telemetry instrumentation, and logs management help your team detect issues quickly, understand the impact, and take action to stay compliant.
How did Adaptavist reduce data downtime with Sifflet?
Adaptavist used Sifflet’s observability platform to map the blast radius of changes, alert users before issues occurred, and validate results pre-production. This proactive approach to data pipeline monitoring helped them eliminate downtime during a major refactor and shift from 'merge and pray' to a risk-aware, observability-first workflow.
How does Sifflet help reduce alert fatigue in data teams?
Sifflet's observability tools are built with smart alerting in mind. By combining dynamic thresholding, impact-aware triage, and anomaly scoring, we help teams focus on what really matters. This reduces noise and ensures that alerts are actionable, leading to faster resolution and better SLA compliance.
Why is data categorization important for data governance and compliance?
Effective data categorization is essential for data governance and compliance because it helps identify sensitive data like PII, ensuring the correct protection policies are applied. With Sifflet’s classification tags, governance teams can easily locate and safeguard sensitive information, supporting GDPR data monitoring and overall data security compliance.
How can I detect silent failures in my data pipelines before they cause damage?
Silent failures are tricky, but with the right data observability tools, you can catch them early. Look for platforms that support real-time alerts, schema registry integration, and dynamic thresholding. These features help you monitor for unexpected changes, missing data, or drift in your pipelines. Sifflet, for example, offers anomaly detection and root cause analysis that help you uncover and fix issues before they impact your business.
How does Sifflet support real-time metrics and proactive monitoring?
Sifflet’s observability platform is designed to provide real-time metrics and proactive monitoring through advanced data quality checks, anomaly detection, and custom health scores. This helps data teams catch issues before they escalate, ensuring your data products stay healthy and consistent.

Want to try Sifflet on your Snowflake Stack?

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