Upholding the SLA of your monetized data products

Turn data trust into a competitive advantage by ensuring your external data products meet the highest standards of reliability.

Customer-Facing Data Quality SLAs

Provide irrefutable proof of data reliability to your paying customers, turning "Trust" into a competitive advantage for your data product.

  • Expose real-time data health scores directly to your consumers to build confidence and differentiate your product.
  • Monitor critical external data feeds against strict business SLAs, not just technical thresholds.
  • Transition from reactive apologies to proactive assurances by guaranteeing the data you sell is accurate, fresh, and complete.

Proactive Incident Communication

Detect issues in your external data feeds and notify your clients before they find the error themselves, protecting your brand reputation.

  • Identify anomalies and schema drift in monetized datasets before they are delivered to partners or hit production APIs.
  • Automatically route external-facing incidents to the right domain owners with full business context for immediate triage.
  • Protect your brand equity by eliminating the "silent failures" that erode customer trust and cause churn.

End-to-End Lineage for Data Audits

Maintain a clear, audit-ready trail of where your monetized data came from and how it was transformed to ensure compliance and accuracy.

  • Visually trace data from source systems all the way to external delivery endpoints.
  • Provide automated evidence of compliance for strict regulatory audits, eliminating the need for manual spot-checks.
  • Ensure the integrity of third-party feeds by catching upstream ingestion errors before they impact downstream revenue.

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
Euronext
Dailymotion
Saint-Gobain
ShopBack
Servier
Penguin Random House
Adaptavist
Mollie
Hypebeast
Deuna
BBC Studios
Carrefour
Etam
Auchan
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Frequently asked questions

Why are traditional data catalogs no longer enough for modern data teams?
Traditional data catalogs focus mainly on metadata management, but they don't actively assess data quality or track changes in real time. As data environments grow more complex, teams need more than just an inventory. They need data observability tools that provide real-time metrics, anomaly detection, and data quality monitoring to ensure reliable decision-making.
What should a solid data quality monitoring framework include?
A strong data quality monitoring framework should be scalable, rule-based and powered by AI for anomaly detection. It should support multiple data sources and provide actionable insights, not just alerts. Tools that enable data drift detection, schema validation and real-time alerts can make a huge difference in maintaining data integrity across your pipelines.
How does the shift to poly cloud impact observability platforms?
The move toward poly cloud environments increases the complexity of monitoring, but observability platforms are evolving to unify insights across multiple cloud providers. This helps teams maintain SLA compliance, monitor ingestion latency, and ensure data reliability regardless of where workloads are running.
Is Sifflet planning to offer native support for Airbyte in the future?
Yes, we're excited to share that a native Airbyte connector is in the works! This will make it even easier to integrate and monitor Airbyte pipelines within our observability platform. Stay tuned as we continue to enhance our capabilities around data lineage, automated root cause analysis, and pipeline resilience.
How does Sifflet support root cause analysis when a deviation is detected?
Sifflet combines distribution deviation monitoring with field-level data lineage tracking. This means when an anomaly is detected, you can quickly trace it back to the source and resolve it efficiently. It’s a huge time-saver for teams managing complex data pipeline monitoring.
What best practices should I follow when planning for data quality monitoring?
Start by defining data validation rules and ownership early in your architecture. Use observability tools that support proactive monitoring, anomaly detection, and root cause analysis to catch issues before they affect downstream systems or business decisions.
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.
How does Sifflet make it easier to manage data volume at scale?
Sifflet simplifies data volume monitoring with plug-and-play integrations, AI-powered baselining, and unified observability dashboards. It automatically detects anomalies, connects them to business impact, and provides real-time alerts. Whether you're using Snowflake, BigQuery, or Kafka, Sifflet helps you stay ahead of data reliability issues with proactive monitoring and alerting.