See the Whole Picture with Sifflet and Snowflake
Sifflet is the control plane for Data and AI — enhanced by business context, run by humans and agents. Built for Snowflake from the ground up.
One control plane. The whole stack.
SEE EVERY ASSET, EVERY DEPENDENCY. Column-level lineage from your sources, through Snowflake, into every dashboard, model, and AI system that consumes the data.
THREE AGENTS ON YOUR DATA TEAM. Sentinel finds what's broken before your business does. Sage explains why — tracing root cause across Snowflake, dbt, Airflow, and your BI tools in seconds. Forge fixes it with human approval at every step.
BUILT FOR THE REGULATED ENTERPRISE. Metadata-only architecture. No data egress. SOC 2 Type II and ISO 27001. Deploy fully managed or in your VPC — or buy directly with your Snowflake committed spend.
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. Sentinel auto-generates and tunes monitors from your Snowflake metadata, query patterns, and lineage — no manual rule-writing. 50+ templates covering freshness, volume, schema, distribution, format, and referential integrity.
Map Lineage with Business Logic
See exactly how data flows from your sources, through Snowflake, into every dashboard and AI model that depends on it. Column-level, field-level, end-to-end. Sifflet's Declarative Assets & Lineage API tells you not just what broke, but what decision is now at risk and who needs to know.
Root Cause in Seconds, Not Hours
When a Looker dashboard breaks, Sage traces it back through dbt, Airflow, and Snowflake to the actual upstream change — in seconds. Uses Snowflake Time Travel for point-in-time root cause and drift detection.
Fix It with Confidence
Forge proposes and executes remediation — schema fixes, monitor updates, alert routing, data backfills — with human-in-the-loop approval at every step. And the Sifflet MCP Server lets you query your observability layer from Claude, Cursor, or any MCP client, so your AI tools know what data is safe to use, in real time.
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 Engineers — stop firefighting. Sentinel monitors automatically, Sage finds root cause fast.
- Data Leaders — full visibility from sources to AI models. Know which decisions are at risk before the business does.
- Governance & Risk Teams — metadata-only architecture, SOC 2 Type II, ISO 27001, no data egress.
- AI & ML Teams — your AI is only as good as the data feeding it. Sifflet is the trust layer between your Snowflake data and your models.
Go Further
Data Observability for your data stack with Snowflake
Discover the Core Capabilities That Ensure Data Quality in Snowflake
Do You need Data Observability?
Every data-driven team dreams of creating "fireworks" with their insights. But without control, the "fire" of the modern data stack can spark chaos.Discover if you're fighting fires or igniting fireworks:
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.
"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. "
"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."
" 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"
"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."
"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."
Frequently asked questions
Yes! While smaller organizations may have fewer data pipelines, ensuring data quality and reliability is equally important for making accurate decisions and scaling effectively. What really matters is the data stack maturity and volume of data. Take our test here to find out if you really need data observability.
-p-500.png)
