Cost-efficient data pipelines
Pinpoint cost inefficiencies and anomalies thanks to full-stack data observability.


Data asset optimization
- Leverage lineage and Data Catalog to pinpoint underutilized assets
- Get alerted on unexpected behaviors in data consumption patterns

Proactive data pipeline management
Proactively prevent pipelines from running in case a data quality anomaly is detected


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Frequently asked questions
What made data observability such a hot topic in 2021?
Great question! Data observability really took off in 2021 because it became clear that reliable data is critical for driving business decisions. As data pipelines became more complex, teams needed better ways to monitor data quality, freshness, and lineage. That’s where data observability platforms came in, helping companies ensure trust in their data by making it fully observable end-to-end.
Is data governance more about culture or tools?
It's a mix of both, but culture plays a big role. As Dan Power puts it, 'culture eats strategy for breakfast.' Even the best observability tools won't succeed without enterprise-wide data literacy and buy-in. That’s why training, user-friendly platforms, and fostering collaboration are just as important as the technology stack you choose.
How does Sifflet help detect and prevent data drift in AI models?
Sifflet is designed to monitor subtle changes in data distributions, which is key for data drift detection. This helps teams catch shifts in data that could negatively impact AI model performance. By continuously analyzing incoming data and comparing it to historical patterns, Sifflet ensures your models stay aligned with the most relevant and reliable inputs.
What should I look for in terms of integrations when choosing a data observability platform?
Great question! When evaluating a data observability platform, it's important to check how well it integrates with your existing data stack. The more integrations it supports, the more visibility you’ll have across your pipelines. This is key to achieving comprehensive data pipeline monitoring and ensuring smooth observability across your entire data ecosystem.
What are some best practices Hypebeast followed for successful data observability implementation?
Hypebeast focused on phased deployment of observability tools, continuous training for all data users, and a strong emphasis on data quality monitoring. These strategies helped ensure smooth adoption and long-term success with their observability platform.
What can I expect from Sifflet at Big Data Paris 2024?
We're so excited to welcome you at Booth #D15 on October 15 and 16! You’ll get to experience live demos of our latest data observability features, hear real client stories like Saint-Gobain’s, and explore how Sifflet helps improve data reliability and streamline data pipeline monitoring.
How does data quality monitoring help improve data reliability?
Data quality monitoring is essential for maintaining trust in your data. A strong observability platform should offer features like anomaly detection, data profiling, and data validation rules. These tools help identify issues early, so you can fix them before they impact downstream analytics. It’s all about making sure your data is accurate, timely, and reliable.
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.