Solution by Use Case

Incident Response Optimization

A Seriously Smart Upgrade.

Prevent, detect and resolve incidents faster than ever before. No matter what your data stack throws at you, your data quality will reach new levels of performance.

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No More Over Reacting

Sifflet takes you from reactive to proactive, with real-time detection and alerts that help you to catch data disruptions, before they happen. Watch your mean time to detection fall rapidly. On even the most complex data stacks.

Advanced capabilities such as multidimensional monitoring help you seize complex data quality issues, even before breaks
ML-based monitors shield your most business-critical data, so essential KPIs are protected and you get notified before there is business impact
OOTB and customizable monitors give you comprehensive, end-to-end coverage and AI helps them get smarter as they go, reducing your reactivity even more.

Resolutions in Record Time

Get to the root cause of incidents and resolve them in record time.

Quickly understand the scope and impact of an incident thanks to detailed system visibility
Trace data flow through your system, identify the start point of issues, and pinpoint downstream dependencies to enable a seamless experience for business users, all thanks to data lineage
Halt the propagation of data quality anomalies with Sifflet’s Flow Stopper

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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

How does data observability improve the value of a data catalog?

Data observability enhances a data catalog by adding continuous monitoring, data lineage tracking, and real-time alerts. This means organizations can not only find their data but also trust its accuracy, freshness, and consistency. By integrating observability tools, a catalog becomes part of a dynamic system that supports SLA compliance and proactive data governance.

Why is data observability important in a modern data stack?

Data observability is crucial because it ensures your data is reliable, trustworthy, and ready for decision-making. It sits at the top of the modern data stack and helps teams detect issues like data drift, schema changes, or freshness problems before they impact downstream analytics. A strong observability platform like Sifflet gives you peace of mind and helps maintain data quality across all layers.

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.

What kind of insights can I gain by integrating Airbyte with Sifflet?

By integrating Airbyte with Sifflet, you unlock real-time insights into your data pipelines, including data freshness checks, anomaly detection, and complete data lineage tracking. This helps improve SLA compliance, reduces troubleshooting time, and boosts your confidence in data quality and pipeline health.

How does Sentinel help reduce alert fatigue in modern data environments?

Sentinel intelligently analyzes metadata like data lineage and schema changes to recommend what really needs monitoring. By focusing on high-impact areas, it cuts down on noise and helps teams manage alert fatigue while optimizing monitoring costs.

How does aligning data observability with business objectives improve outcomes?

Aligning data observability with business goals transforms data from a technical asset into a strategic one. By setting clear KPIs and linking data quality monitoring to business impact, teams can make smarter decisions, improve SLA compliance, and drive real value from their data investments.

How does Sifflet maintain visual and interaction consistency across its observability platform?

We use a reusable component library based on atomic design principles, along with UX writing guidelines to ensure consistent terminology. This helps users quickly understand telemetry instrumentation, metrics collection, and incident response workflows without needing to relearn interactions across different parts of the platform.

How does Sifflet support real-time data lineage and observability?

Sifflet provides automated, field-level data lineage integrated with real-time alerts and anomaly detection. It maps how data flows across your stack, enabling quick root cause analysis and impact assessments. With features like data drift detection, schema change tracking, and pipeline error alerting, Sifflet helps teams stay ahead of issues and maintain data reliability.

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