Solution by Use Case

Monitoring at Scale

Coverage without compromise.

Grow monitoring coverage intelligently as your stack scales and do more with less resources thanks to tooling that reduces maintenance burden, improves signal-to-noise, and helps you understand impact across interconnected systems.

Take a Tour

Don’t Let Scale Stop You

As your stack and data assets scale, so do monitors. Keeping rules updated becomes a full-time job, and tribal knowledge about monitors gets scattered, so teams struggle to sunset obsolete monitors while adding new ones. No more with Sifflet.

Optimize monitoring coverage and minimize noise levels with AI-powered suggestions and supervision that adapt dynamically
Implement programmatic monitoring set up and maintenance with Data Quality as Code (DQaC)
Automated monitor creation and updates based on data changes
Centralized monitor management reduces maintenance overhead

‍

Get Clear and Consistent

Maintaining consistent monitoring practices across tools, platforms, and internal teams that work across different parts of the stack isn’t easy. Sifflet makes it a breeze.

Set up consistent alerting and response workflows
Benefit from unified monitoring across your platforms and tools
Use automated dependency mapping to show system relationships and benefit from end-to-end visibility across the entire data pipeline

‍

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

Discover more title goes here

Still have a question in mind ?

Frequently asked questions

What are Sentinel, Sage, and Forge, and how do they enhance data observability?

Sentinel, Sage, and Forge are Sifflet’s new AI agents designed to supercharge your data observability efforts. Sentinel proactively recommends monitoring strategies, Sage accelerates root cause analysis by remembering system history, and Forge guides your team with actionable fixes. Together, they help teams reduce alert fatigue and improve data reliability at scale.

Why is data observability important during the data integration process?

Data observability is key during data integration because it helps detect issues like schema changes or broken APIs early on. Without it, bad data can flow downstream, impacting analytics and decision-making. At Sifflet, we believe observability should start at the source to ensure data reliability across the whole pipeline.

How is data volume different from data variety?

Great question! Data volume is about how much data you're receiving, while data variety refers to the different types and formats of data sources. For example, a sudden drop in appointment data is a volume issue, while a new file format causing schema mismatches is a variety issue. Observability tools help you monitor both dimensions to maintain healthy pipelines.

Is Sifflet planning to offer native support for Airbyte in the future?

Yes, we're excited to share that a native Airbyte connector is in the works! This will make it even easier to integrate and monitor Airbyte pipelines within our observability platform. Stay tuned as we continue to enhance our capabilities around data lineage, automated root cause analysis, and pipeline resilience.

What makes observability scalable across different teams and roles?

Scalable observability works for engineers, analysts, and business stakeholders alike. It supports telemetry instrumentation for developers, intuitive dashboards for analysts, and high-level confidence signals for executives. By adapting to each role without adding friction, observability becomes a shared language across the organization.

What is data observability and why is it important for modern data teams?

Data observability is the ability to monitor and understand the health of your data across the entire data stack. As data pipelines become more complex, having real-time visibility into where and why data issues occur helps teams maintain data reliability and trust. At Sifflet, we believe data observability is essential for proactive data quality monitoring and faster root cause analysis.

Can I use custom dbt metadata for data governance in Sifflet?

Absolutely! Our new dbt tab surfaces custom metadata defined in your dbt models, which you can leverage for better data governance and data profiling. It’s all about giving you the flexibility to manage your data assets exactly the way you need.

How does data profiling support GDPR compliance efforts?

Data profiling helps by automatically identifying and tagging personal data across your systems. This is vital for GDPR, where you need to know exactly what PII you have and where it's stored. Combined with data quality monitoring and metadata discovery, profiling makes it easier to manage consent, enforce data contracts, and ensure data security compliance.

Read all FAQs