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 role does data lineage tracking play in storage observability?

Data lineage tracking is essential for understanding how data flows from storage to dashboards. When something breaks, Sifflet helps you trace it back to the storage layer, whether it's a corrupted file in S3 or a schema drift in MongoDB. This visibility is critical for root cause analysis and ensuring data reliability across your pipelines.

How does Sifflet make setting up data quality monitoring easier?

Great question! With the launch of Data-Quality-as-Code v2, Sifflet has made it much easier to create and manage monitors at scale. Whether you prefer working programmatically or through the UI, our platform now offers smoother workflows and standardized threshold settings for more intuitive data quality monitoring.

What are the main challenges of implementing Data as a Product?

Some key challenges include ensuring data privacy and security, maintaining strong data governance, and investing in data optimization. These areas require robust monitoring and compliance tools. Leveraging an observability platform can help address these issues by providing visibility into data lineage, quality, and pipeline performance.

Can Sifflet’s dbt Impact Analysis help with root cause analysis?

Absolutely! By identifying all downstream assets affected by a dbt model change, Sifflet’s Impact Report makes it easier to trace issues back to their source, significantly speeding up root cause analysis and reducing incident resolution time.

Who should be responsible for data quality in an organization?

That's a great topic! While there's no one-size-fits-all answer, the best data quality programs are collaborative. Everyone from data engineers to business users should play a role. Some organizations adopt data contracts or a Data Mesh approach, while others use centralized observability tools to enforce data validation rules and ensure SLA compliance.

How does SQL Table Tracer support different SQL dialects for data lineage tracking?

SQL Table Tracer uses Antlr4 and a unified grammar with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This ensures accurate data lineage tracking across diverse systems without needing separate parsers for each dialect.

How can poor data distribution impact machine learning models?

When data distribution shifts unexpectedly, it can throw off the assumptions your ML models are trained on. For example, if a new payment processor causes 70% of transactions to fall under $5, a fraud detection model might start flagging legitimate behavior as suspicious. That's why real-time metrics and anomaly detection are so crucial for ML model monitoring within a good data observability framework.

Why is metadata observability so important in an Open Data Stack?

In an Open Data Stack, metadata acts as the new control plane, guiding how different engines interpret and interact with your data. Without active metadata observability, you're at risk of schema drift, catalog mismatches, and invisible data errors. Sifflet helps you stay ahead by continuously monitoring metadata changes and ensuring data reliability across your stack.

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