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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

Why is data quality management so important for growing organizations?
Great question! Data quality management helps ensure that your data remains accurate, complete, and aligned with business goals as your organization scales. Without strong data quality practices, teams waste time troubleshooting issues, decision-makers lose trust in reports, and systems make poor choices. With proper data quality monitoring in place, you can move faster, automate confidently, and build a competitive edge.
How can I monitor the health of my ETL or ELT pipelines?
Monitoring pipeline health is essential for maintaining data reliability. You can use tools that offer data pipeline monitoring features such as real-time metrics, ingestion latency tracking, and pipeline error alerting. Sifflet’s pipeline health dashboard gives you full visibility into your ETL and ELT processes, helping you catch issues early and keep your data flowing smoothly.
Why is data lineage important for GDPR compliance?
Data lineage is essential for GDPR because it helps you trace personal data from source to destination. This means you can see where PII is stored, how it flows through your data pipelines, and which reports or applications use it. With this visibility, you can manage deletion requests, audit data usage, and ensure data governance policies are enforced consistently.
Can non-technical users benefit from Sifflet’s data observability platform?
Absolutely. Sifflet is designed to be accessible to everyone. With an intuitive UI and our AI Assistant, even non-technical users can set up data quality monitors, track real-time metrics, and contribute to data governance without writing a line of code.
Will dbt Impact Analysis be available for other version control tools?
Yes! While it currently supports GitHub and GitLab, Sifflet is actively working on bringing dbt Impact Analysis to Bitbucket. This expansion ensures broader coverage and supports more teams in achieving better data governance and observability.
What kind of integrations does Sifflet offer for data pipeline monitoring?
Sifflet integrates with cloud data warehouses like Snowflake, Redshift, and BigQuery, as well as tools like dbt, Airflow, Kafka, and Tableau. These integrations support comprehensive data pipeline monitoring and ensure observability tools are embedded across your entire stack.
How does the new Fivetran integration enhance data observability in Sifflet?
Great question! With our new Fivetran integration, Sifflet now provides visibility into your data's journey even before it reaches your data platform. This means you can track data from its source through Fivetran connectors all the way downstream, offering truly end-to-end data observability.
Why is data lineage a pillar of Full Data Stack Observability?
At Sifflet, we consider data lineage a core part of Full Data Stack Observability because it connects data quality monitoring with data discovery. By mapping data dependencies, teams can detect anomalies faster, perform accurate root cause analysis, and maintain trust in their data pipelines.

Want to try Sifflet on your Snowflake Stack?

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