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.

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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

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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

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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

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Frequently asked questions

What should I look for in a data quality monitoring solution?

You’ll want a solution that goes beyond basic checks like null values and schema validation. The best data quality monitoring tools use intelligent anomaly detection, dynamic thresholding, and auto-generated rules based on data profiling. They adapt as your data evolves and scale effortlessly across thousands of tables. This way, your team can confidently trust the data without spending hours writing manual validation rules.

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.

How can I avoid breaking reports and dashboards during migration?

To prevent disruptions, it's essential to use data lineage tracking. This gives you visibility into how data flows through your systems, so you can assess downstream impacts before making changes. It’s a key part of data pipeline monitoring and helps maintain trust in your analytics.

What role does data lineage tracking play in data observability?

Data lineage tracking is a key part of data observability because it helps you understand where your data comes from and how it changes over time. With clear lineage, teams can perform faster root cause analysis and collaborate better across business and engineering, which is exactly what platforms like Sifflet enable.

What is dbt Impact Analysis and how does it help with data observability?

dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.

How does reverse ETL improve data reliability and reduce manual data requests?

Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.

What is the Model Context Protocol (MCP), and why is it important for data observability?

The Model Context Protocol (MCP) is a new interface standard developed by Anthropic that allows large language models (LLMs) to interact with tools, retain memory, and access external context. At Sifflet, we're excited about MCP because it enables more intelligent agents that can help with data observability by diagnosing issues, triggering remediation tools, and maintaining context across long-running investigations.

How does data observability help detect data volume issues?

Data observability provides visibility into your pipelines by tracking key metrics like row counts, duplicates, and ingestion patterns. It acts as an early warning system, helping teams catch volume anomalies before they affect dashboards or ML models. By using a robust observability platform, you can ensure that your data is consistently complete and trustworthy.

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