Book a Demo

Request a demo

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 kind of monitoring should I set up after migrating to the cloud?

After migration, continuous data quality monitoring is a must. Set up real-time alerts for data freshness checks, schema changes, and ingestion latency. These observability tools help you catch issues early and keep your data pipelines running smoothly.

How does Sifflet help with anomaly detection in data pipelines?

Sifflet uses machine learning to power anomaly detection across your data ecosystem. Instead of relying on static rules, it learns your data’s patterns and flags unusual behavior—like a sudden drop in transaction volume. This helps teams catch issues early, avoid alert fatigue, and focus on incidents that actually impact business outcomes. It’s data quality monitoring with real intelligence.

What makes Monte Carlo a good fit for modern cloud analytics teams?

Monte Carlo shines when you're working with a modern cloud stack and need fast, low-effort data observability. Its ML-driven anomaly detection and metadata-focused monitoring make it easy to catch data issues without writing custom rules. If your team wants to improve dashboard reliability quickly, Monte Carlo is a strong option.

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.

How does Sifflet’s dbt Impact Analysis improve data pipeline monitoring?

By surfacing impacted tables, dashboards, and other assets directly in GitHub or GitLab, Sifflet’s dbt Impact Analysis gives teams real-time visibility into how changes affect the broader data pipeline. This supports better data pipeline monitoring and helps maintain data reliability.

What role does data observability play in preventing freshness incidents?

Data observability gives you the visibility to detect freshness problems before they impact the business. By combining metrics like data age, expected vs. actual arrival time, and pipeline health dashboards, observability tools help teams catch delays early, trace where things broke down, and maintain trust in real-time metrics.

How does this integration help with root cause analysis?

By including Fivetran connectors and source assets in the lineage graph, Sifflet gives you full visibility into where data issues originate. This makes it much easier to perform root cause analysis and resolve incidents faster, improving overall data reliability.

What role does data lineage tracking play in observability?

Data lineage tracking is a key part of any robust data observability framework. It helps you understand where your data comes from, how it’s transformed, and where it flows. This visibility is essential for debugging issues, ensuring compliance, and building trust in your data pipelines. It's especially useful when paired with real-time data pipeline monitoring tools.

Read all FAQs

Still have questions?

Data Observability is Now

Make Data Observability Everyone’s Business Now