DATA OBSERVABILITY ON SNOWFLAKE

Launch Enterprise-Grade Observability on Snowflake Now

‍

Claim Offer See How It Works

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.

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.

USE CASE #1

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.

USE CASE #2

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.

USE CASE #3

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.

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.

How they do it

Hypebeast x Sifflet: Revolutionizing Fashion with Data Observability

How Hypebeast improved data quality, improved efficiency and strengthened collaboration by implementing Sifflet.

Tell me more

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.

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

Frequently asked questions

How does the Sifflet AI Assistant improve data observability at scale?

The Sifflet AI Assistant enhances data observability by automatically fine-tuning your monitoring setup using machine learning and dynamic thresholds. It continuously adapts to changes in your data pipelines, reducing false positives and ensuring accurate anomaly detection, even as your data scales globally.

What makes Sifflet's architecture unique for secure data pipeline monitoring?

Sifflet uses a cell-based architecture that isolates each customer’s instance and database. This ensures that even under heavy usage or a potential breach, your data pipeline monitoring remains secure, reliable, and unaffected by other customers’ activities.

What does the Sifflet and Google Cloud partnership mean for users?

Great question! This partnership allows Google Cloud users to integrate Sifflet’s data observability platform directly within their private cloud environment. That means better visibility, reliability, and trust in your data from ingestion all the way to analytics.

How can data observability support a strong data governance strategy?

Data observability complements data governance by continuously monitoring data pipelines for issues like data drift, freshness problems, or anomalies. With an observability platform like Sifflet, teams can proactively detect and resolve data quality issues, enforce data validation rules, and gain visibility into pipeline health. This real-time insight helps governance policies work in practice, not just on paper.

How can I better manage stakeholder expectations for the data team?

Setting clear priorities and using a centralized pipeline orchestration visibility tool can help manage expectations across the organization. When stakeholders understand what the team can deliver and when, it builds trust and reduces pressure on your team, leading to a healthier and happier work environment.

Why is semantic quality monitoring important for AI applications?

Semantic quality monitoring ensures that the data feeding into your AI models is contextually accurate and production-ready. At Sifflet, we're making this process seamless with tools that check for data drift, validate schema, and maintain high data quality without manual intervention.

How does the shift to poly cloud impact observability platforms?

The move toward poly cloud environments increases the complexity of monitoring, but observability platforms are evolving to unify insights across multiple cloud providers. This helps teams maintain SLA compliance, monitor ingestion latency, and ensure data reliability regardless of where workloads are running.

Why is a data catalog essential for modern data teams?

A data catalog is critical because it helps teams find, understand, and trust their data. It centralizes metadata, making data assets searchable and understandable, which reduces duplication, speeds up analytics, and supports data governance. When paired with data observability tools, it becomes a powerful foundation for proactive data management.

Read all FAQs

Still have questions?