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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 role did data observability play in improving Meero's data reliability?

Data observability was key to Meero's success in maintaining reliable data pipelines. By using Sifflet’s observability platform, they could monitor data freshness, schema changes, and volume anomalies, ensuring their data remained trustworthy and accurate for business decision-making.

Can observability tools help with GDPR-related incident response?

Absolutely! Observability tools can support GDPR compliance by enabling faster incident response automation. If there's a data breach, you need to notify users and authorities within 72 hours. Real-time alerts, telemetry instrumentation, and logs management help your team detect issues quickly, understand the impact, and take action to stay compliant.

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 MCP play in improving incident response automation?

MCP is a game-changer for incident response automation. By allowing LLMs to interact with telemetry data, call remediation tools, and maintain context over time, MCP enables proactive monitoring and faster resolution. This aligns perfectly with Sifflet’s mission to reduce downtime and improve pipeline resilience.

How is AI shaping the future of data observability?

AI enhances data observability with advanced anomaly detection, predictive analytics, and automated root cause analysis. This helps teams identify and resolve issues faster while reducing manual effort. Have a look at how Sifflet is leveraging AI for better data observability here

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 is data observability and why is it important for modern data teams?

Data observability is the practice of monitoring data as it moves through your pipelines to detect, understand, and resolve issues proactively. It’s crucial because it helps data teams ensure data reliability, improve decision-making, and reduce the time spent firefighting data issues. With the growing complexity of data systems, having a robust observability platform is key to maintaining trust in your data.

How do organizations monitor the success of their data governance programs?

Successful data governance is measured through KPIs that tie directly to business outcomes. This includes metrics like how quickly teams can find data, how often data quality issues are caught before reaching production, and how well teams follow access protocols. Observability tools help track these indicators by providing real-time metrics and alerting on governance-related issues.

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