Launch Enterprise-Grade Observability on Snowflake Now
The Snowflake Observability Imperative
Snowflake teams are scaling workloads, AI initiatives, and governance programs - but unreliable data creates blind spots that slow delivery and erode trust.
Incidents Cascade Faster than Teams Can Respond
One failed task can ripple across domains and downstream workflows,
delaying insights, increasing MTTR, and draining engineering cycles.
Hidden Blind Spots Stall AI Readiness
Snowflake customers often have quality issues buried in tables, tasks, and pipelines but without visibility, issues surface only when dashboards or models break.
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Procurement Delays Are the Costliest Risk of All
By the time observability is approved, incidents and trust debt have already accumulated. What Snowflake teams need is a way to deploy observability now, not in Q2.
Why Sifflet stands out on Snowflake
If you want reliability, visibility and control inside Snowflake, these use cases show exactly where Sifflet gives you an edge.
Detect issues inside Snowflake before they spread
The challenge: Data issues inside Snowflake often surface too late, after they hit dashboards or AI workloads. Teams refresh, guess, and lose hours hunting for the source.
The Sifflet edge: Sifflet monitors freshness, volume, schema and key metrics directly on Snowflake, flags anomalies fast, and ranks them by business impact so teams fix what matters first.
Understand your full data flow across Snowflake
The challenge: Multiple pipelines feed Snowflake, which makes it hard to see how tables are connected, what depends on what, and what breaks downstream when an upstream job fails.
The Sifflet edge: Sifflet maps lineage across sources, transformations and consumers with table and field level detail, giving clear visibility on upstream and downstream impact in one place.
Cut the noise and reduce alert fatigue
The challenge: Teams drown under alerts from schema changes, volume drops and late tables, and end up ignoring everything. Critical issues slip through.
The Sifflet edge: Sifflet scores alerts using context from usage and business priority, filters noise, and highlights only the issues that deserve attention so teams stay focused and efficient.
Native Reliability Inside Snowflake
Monitor tables, tasks, streams, and pipelines directly where your data lives, detecting issues before they become business problems.
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Seamless Integration with Your Modern Data Stack
Sifflet connects to Snowflake metadata, query logs, Time Travel, lineage, and downstream tools — eliminating silos and accelerating RCA by up to 70%.
Built for Teams Across Data, Engineering & Business
Unlimited users means everyone from analysts and engineers to executives sees trust signals and impact context to make faster, better decisions.
Looking for more?
How Carrefour Transformed Data Quality & Efficiency Across 8 Countries with Sifflet
Discover how Carrefour managed to cover over 800+ data assets, see 80% efficiency gains, and streamlined their operations with Sifflet.
Retailers Are Deploying Data Observability to Avoid Revenue Loss.
For retailers, getting an accurate picture of inventory and sales performance has become harder than ever. With millions of dollars in revenue at stake, more and more of them are turning to data observability to help match customer demand to stock.
Hypebeast x Sifflet: Revolutionizing Fashion with Data Observability
How Hypebeast improved data quality, improved efficiency and strengthened collaboration by implementing Sifflet.
Ready to unlock real trust in your Snowflake data?
If you want fewer surprises and more clarity across your pipelines, talk to us. We can show you exactly how Sifflet strengthens your Snowflake setup end to end.
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.
"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. "
"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."
"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 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."
"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.
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