DATA OBSERVABILITY ON SNOWFLAKE

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.

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.

Sifflet troubleshoot illustration
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.

Sifflet driving illustration
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.

sifflet datacatalog

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.

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 are the main trade-offs of using Datadog for data pipeline monitoring?
The main trade-offs of using Datadog for data pipeline monitoring include high costs, especially in high-cardinality environments, and limited visibility into the actual data content. While Datadog is great for real-time metrics and infrastructure observability, it doesn't provide deep data validation rules or business-aware anomaly detection. Teams needing those capabilities may want to pair it with a more focused data observability solution.
What’s coming next for the Sifflet AI Assistant?
We’re excited about what’s ahead. Soon, the Sifflet AI Assistant will allow non-technical users to create monitors using natural language, expand monitoring coverage automatically, and provide deeper insights into resource utilization and capacity planning to support scalable data observability.
How does aligning data observability with business objectives improve outcomes?
Aligning data observability with business goals transforms data from a technical asset into a strategic one. By setting clear KPIs and linking data quality monitoring to business impact, teams can make smarter decisions, improve SLA compliance, and drive real value from their data investments.
How does Sifflet support data lineage tracking across tools like Snowflake and dbt?
Sifflet provides end-to-end data lineage tracking that connects your tables to dbt models, semantic layers, and BI dashboards. This visibility helps you understand the full impact of any metadata change, ensuring data quality monitoring and reducing the risk of breaking critical business KPIs.
How does data observability fit into the modern data stack?
Data observability integrates across your existing data stack, from ingestion tools like Airflow and AWS Glue to storage solutions like Snowflake and Redshift. It acts as a monitoring layer that provides real-time insights and alerts across each stage, helping teams maintain pipeline health and ensure data freshness checks are always in place.
How did implementing a data observability platform impact Hypebeast’s operations?
After adopting Sifflet’s observability platform, Hypebeast saw a 204% improvement in data quality, a 178% increase in data product delivery, and a 75% boost in ad hoc request speed. These gains translated into faster, more reliable insights and better collaboration across departments.
Can open-source ETL tools support data observability needs?
Yes, many open-source ETL tools like Airbyte or Talend can be extended to support observability features. By integrating them with a cloud data observability platform like Sifflet, you can add layers of telemetry instrumentation, anomaly detection, and alerting. This ensures your open-source stack remains robust, reliable, and ready for scale.
What types of data lineage should I know about?
There are four main types: technical lineage, business lineage, cross-system lineage, and governance lineage. Each serves a different purpose, from debugging pipelines to supporting compliance. Tools like Sifflet offer field-level lineage for deeper insights, helping teams across engineering, analytics, and compliance understand and trust their data.
Still have questions?