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Get ahead of business issues before they become business catastrophes.

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

"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

"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

"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 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

"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

Show Your Stack Who’s Boss

Unified data observability that packs a three-in-one punch. From data discovery to integrated monitoring and troubleshooting capabilities, you’ll be the one in charge.

Seamlessly connect with all your favorite data tools to centralize insights and unlock the full potential of your data ecosystem.

Join the ranks of happy customers who’ve made Sifflet a G2 leader, trusted for its innovation and impact

Stay ahead of issues with real-time alerts that keep you informed and in control of your data health

Organize, discover, and leverage your data assets effortlessly with a smart, searchable catalog built for modern teams.

Harness the power of AI-driven suggestions to improve efficiency, accuracy, and decision-making across your workflows.

Empower your team with tailored access, enabling secure collaboration that drives smarter decisions.

Frequently asked questions

What trends in data observability should we watch for in 2025?

In 2025, expect to see more focus on AI-driven anomaly detection, dynamic thresholding, and predictive analytics monitoring. Staying ahead means experimenting with new observability tools, engaging with peers, and continuously aligning your data strategy with evolving business needs.

What makes Etam’s data strategy resilient in a fast-changing retail landscape?

Etam’s data strategy is built on clear business alignment, strong data quality monitoring, and a focus on delivering ROI across short, mid, and long-term horizons. With the help of an observability platform, they can adapt quickly, maintain data reliability, and support strategic decision-making even in uncertain conditions.

Can MCP help with data pipeline monitoring and incident response?

Absolutely! MCP allows LLMs to remember past interactions and call diagnostic tools, which is a game-changer for data pipeline monitoring. It supports multi-turn conversations and structured tool use, making incident response faster and more contextual. This means less time spent digging through logs and more time resolving issues efficiently.

What’s new with the Distribution Change monitor and how does it improve anomaly detection?

The upgraded Distribution Change monitor now focuses on tracking volume shifts between specific categories, like product lines or customer segments. This makes anomaly detection more precise by reducing noise and highlighting only the changes that truly matter. It's a smarter way to stay on top of data drift and ensure your metrics reflect reality.

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.

How does data observability support AI and machine learning initiatives?

AI models are only as good as the data they’re trained on. With data observability, you can ensure data quality, detect data drift, and enforce validation rules, all of which are critical for reliable AI outcomes. Sifflet helps you maintain trust in your data so you can confidently scale your ML and predictive analytics efforts.

Can non-technical users benefit from Sifflet’s Data Catalog?

Yes, definitely! Sifflet is designed to be user-friendly for both technical and business users. With features like AI-driven description recommendations and easy-to-navigate asset pages, even non-technical users can confidently explore and understand the data they need.

Why is combining data catalogs with data observability tools the future of data management?

Combining data catalogs with data observability tools creates a holistic approach to managing data assets. While catalogs help users discover and understand data, observability tools ensure that data is accurate, timely, and reliable. This integration supports better decision-making, improves data reliability, and strengthens overall data governance.

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