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

Can Sifflet help me monitor data drift and anomalies beyond what dbt offers?

Absolutely! While dbt is fantastic for defining tests, Sifflet takes it further with advanced data drift detection and anomaly detection. Our platform uses intelligent monitoring templates that adapt to your data’s behavior, so you can spot unexpected changes like missing rows or unusual values without setting manual thresholds.

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.

Can Sifflet support real-time metrics and monitoring for AI pipelines?

Absolutely! While Sifflet’s monitors are typically scheduled, you can run them on demand using our API. This means you can integrate real-time data quality checks into your AI pipelines, ensuring your models are making decisions based on the freshest and most accurate data available. It's a powerful way to keep your AI systems responsive and reliable.

What is data ingestion and why is it so important for modern businesses?

Data ingestion is the process of collecting and loading data from various sources into a central system like a data lake or warehouse. It's the first step in your data pipeline and is critical for enabling real-time metrics, analytics, and operational decision-making. Without reliable ingestion, your downstream analytics and data observability efforts can quickly fall apart.

How can data observability support a Data as a Product (DaaP) strategy?

Data observability plays a crucial role in a DaaP strategy by ensuring that data is accurate, fresh, and trustworthy. With tools like Sifflet, businesses can monitor data pipelines in real time, detect anomalies, and perform root cause analysis to maintain high data quality. This helps build reliable data products that users can trust.

What does 'agentic observability' mean and why does it matter?

Agentic observability is our vision for the future — where observability platforms don’t just monitor, they act. Think of it as moving from real-time alerts to intelligent copilots. With features like auto-remediation, dynamic thresholding, and incident response automation, Sifflet is building systems that can detect issues, assess impact, and even resolve known problems on their own. It’s a huge step toward self-healing pipelines and truly proactive data operations.

How do declared assets improve data quality monitoring?

Declared assets appear in your Data Catalog just like built-in assets, with full metadata and business context. This improves data quality monitoring by making it easier to track data lineage, perform data freshness checks, and ensure SLA compliance across your entire pipeline.

What’s the difference between data distribution and data lineage tracking?

Great distinction! Data distribution shows you how values are spread across a dataset, while data lineage tracking helps you trace where that data came from and how it’s moved through your pipeline. Both are essential for root cause analysis, but they solve different parts of the puzzle in a robust observability platform.

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