DATA OBSERVABILITY ON SNOWFLAKE

Launch Enterprise-Grade Observability on Snowflake Now

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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.

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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.

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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

What’s the best way to prevent bad data from impacting our business decisions?

Preventing bad data starts with proactive data quality monitoring. That includes data profiling, defining clear KPIs, assigning ownership, and using observability tools that provide real-time metrics and alerts. Integrating data lineage tracking also helps you quickly identify where issues originate in your data pipelines.

Why do traditional data contracts often fail in dynamic environments?

Traditional data contracts struggle because they’re static by nature, while modern data systems are constantly evolving. As AI and real-time workloads become more common, these contracts can’t keep up with schema changes, data drift, or business logic updates. That’s why many teams are turning to data observability platforms like Sifflet to bring context, real-time metrics, and trust into the equation.

Why is data observability becoming a business imperative in industries like finance and logistics?

In sectors like financial services, insurance, and logistics, data reliability isn't just a technical concern, it's a compliance and operational necessity. A single data incident can lead to regulatory risks or business disruption. That's why data observability platforms like Sifflet are being adopted to ensure data quality, monitor pipelines in real time, and maintain SLA compliance.

How does data observability improve data contract enforcement?

Data observability adds critical context that static contracts lack, such as data lineage tracking, real-time usage patterns, and anomaly detection. With observability tools, teams can proactively monitor contract compliance, detect schema drift early, and ensure SLA compliance before issues impact downstream systems. It transforms contracts from documentation into enforceable, living agreements.

How does data lineage tracking help when something breaks?

Data lineage tracking is a lifesaver when you’re dealing with broken dashboards or bad reports. It maps your data’s journey from source to consumption, so when something goes wrong, you can quickly see what downstream assets are affected. This is key for fast root cause analysis and helps you notify the right business stakeholders. A good observability platform will give you both technical and business lineage, making it easier to trace issues back to their source.

What role does technology play in supporting data team well-being?

The right technology can make a big difference. Adopting observability tools that offer features like data lineage tracking, data freshness checks, and pipeline health dashboards can reduce manual firefighting and help your team work more autonomously. This not only improves productivity but also makes day-to-day work more enjoyable.

What made data observability such a hot topic in 2021?

Great question! Data observability really took off in 2021 because it became clear that reliable data is critical for driving business decisions. As data pipelines became more complex, teams needed better ways to monitor data quality, freshness, and lineage. That’s where data observability platforms came in, helping companies ensure trust in their data by making it fully observable end-to-end.

How does Sifflet maintain visual and interaction consistency across its observability platform?

We use a reusable component library based on atomic design principles, along with UX writing guidelines to ensure consistent terminology. This helps users quickly understand telemetry instrumentation, metrics collection, and incident response workflows without needing to relearn interactions across different parts of the platform.

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