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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

How does data observability help ensure SLA compliance for data products?

Data observability plays a big role in SLA compliance by continuously monitoring data freshness, quality, and availability. With tools like Sifflet, teams can set alerts and track metrics that align with their SLAs, ensuring data products meet business expectations consistently.

How did Sifflet support Meero’s incident management and root cause analysis efforts?

Sifflet provided Meero with powerful tools for root cause analysis and incident management. With features like data lineage tracking and automated alerts, the team could quickly trace issues back to their source and take action before they impacted business users.

How does Sifflet help with root cause analysis in data pipelines?

Sifflet uses AI-powered agents that continuously analyze metadata and behavioral patterns across your stack. When issues arise, these agents perform root cause analysis by tracing data lineage and identifying where problems originated, making it easier for teams to resolve incidents quickly and confidently.

How does Sifflet use AI to enhance data observability?

Sifflet uses AI not just for buzzwords, but to genuinely improve your workflows. From AI-powered metadata generation to dynamic thresholding and intelligent anomaly detection, Sifflet helps teams automate data quality monitoring and make faster, smarter decisions based on real-time insights.

Can reverse ETL help with data quality monitoring?

Absolutely. By integrating reverse ETL with a strong observability platform like Sifflet, you can implement data quality monitoring throughout the pipeline. This includes real-time alerts for sync issues, data freshness checks, and anomaly detection to ensure your operational data remains trustworthy and accurate.

How does Sifflet help with SLA compliance for business metrics?

By combining real-time metrics monitoring with proactive alerts and incident management workflows, Sifflet helps teams stay on top of SLA compliance. Users can track metrics freshness, detect anomalies, and take action before SLA breaches occur.

Why did Shippeo decide to invest in a data observability solution like Sifflet?

As Shippeo scaled, they faced silent data leaks, inconsistent metrics, and data quality issues that impacted billing and reporting. By adopting Sifflet, they gained visibility into their data pipelines and could proactively detect and fix problems before they reached end users.

How does Sifflet help scale dbt environments without compromising data quality?

Great question! Sifflet enhances your dbt environment by adding a robust data observability layer that enforces standards, monitors key metrics, and ensures data quality monitoring across thousands of models. With centralized metadata, automated monitors, and lineage tracking, Sifflet helps teams avoid the usual pitfalls of scaling like ownership ambiguity and technical debt.

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