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

Is Forge able to automatically fix data issues in my pipelines?

Forge doesn’t take action on its own, but it does provide smart, contextual guidance based on past fixes. It helps teams resolve issues faster while keeping you in full control of the resolution process, which is key for maintaining SLA compliance and data quality monitoring.

What role does real-time data play in modern analytics pipelines?

Real-time data is becoming a game-changer for analytics, especially in use cases like fraud detection and personalized recommendations. Streaming data monitoring and real-time metrics collection are essential to harness this data effectively, ensuring that insights are both timely and actionable.

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.

How does Sifflet make data observability more accessible to BI users?

Great question! At Sifflet, we're committed to making data observability insights available right where you work. That’s why we’ve expanded beyond our Chrome extension to integrate directly with popular Data Catalogs like Atlan, Alation, Castor, and Data Galaxy. This means BI users can access real-time metrics and data quality insights without ever leaving their workflow.

How can decision-makers ensure the data they receive is actionable and easy to understand?

It's all about presentation and relevance. Whether you're using Tableau dashboards or traditional slide decks, your data should be tailored to the decision-maker's needs. This is where data observability dashboards and metrics aggregation come in handy, helping to surface the most impactful insights clearly and quickly so leaders can act with confidence.

How did jobvalley improve data visibility across their teams?

jobvalley enhanced data visibility by implementing Sifflet’s observability platform, which included a powerful data catalog. This centralized hub made it easier for teams to discover and access the data they needed, fostering better collaboration and transparency across departments.

What role did data observability play in Carrefour’s customer engagement strategy?

Data observability was crucial in maintaining high data quality for loyalty programs and marketing campaigns. With real-time metrics and anomaly detection in place, Carrefour was able to improve customer satisfaction and retention through more accurate and timely insights.

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.

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