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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.
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Join the ranks of happy customers who’ve made Sifflet a G2 leader, trusted for its innovation and impact
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Stay ahead of issues with real-time alerts that keep you informed and in control of your data health
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Organize, discover, and leverage your data assets effortlessly with a smart, searchable catalog built for modern teams.
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Harness the power of AI-driven suggestions to improve efficiency, accuracy, and decision-making across your workflows.
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Empower your team with tailored access, enabling secure collaboration that drives smarter decisions.

Frequently asked questions

What is dbt Impact Analysis and how does it help with data observability?
dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.
Can Sifflet integrate with our existing data tools and platforms?
Absolutely! Sifflet is designed to integrate seamlessly with your current stack. We support a wide range of tools including Airflow, Snowflake, AWS Glue, and more. Our goal is to provide complete pipeline orchestration visibility and data freshness checks, all from one intuitive interface.
How does data observability support MLOps and AI initiatives at Hypebeast?
Data observability plays a key role in Hypebeast’s MLOps strategy by monitoring data quality from ML models before it reaches dashboards or decision systems. This ensures that AI-driven insights are trustworthy and aligned with business goals.
How does Sifflet help improve data discovery across my organization?
Sifflet consolidates metadata from your entire data stack into a centralized Data Catalog, making it easier for data stakeholders to discover, understand, and trust data. With features like enriched metadata, Snowflake tags, and BigQuery labels, data discovery becomes faster and more intuitive, reducing time spent searching for the right assets.
What benefits did jobvalley experience from using Sifflet’s data observability platform?
By using Sifflet’s data observability platform, jobvalley improved data reliability, streamlined data discovery, and enhanced collaboration across teams. These improvements supported better decision-making and helped the company maintain a strong competitive edge in the HR tech space.
How do declared assets improve data quality monitoring?
Declared assets appear in your Data Catalog just like built-in assets, with full metadata and business context. This improves data quality monitoring by making it easier to track data lineage, perform data freshness checks, and ensure SLA compliance across your entire pipeline.
What role does Sifflet’s data catalog play in observability?
Sifflet’s data catalog acts as the central hub for your data ecosystem, enriched with metadata and classification tags. This foundation supports cloud data observability by giving teams full visibility into their assets, enabling better data lineage tracking, telemetry instrumentation, and overall observability platform performance.
What non-quantifiable benefits can data observability bring to my organization?
Besides measurable improvements, data observability also boosts trust in data, enhances decision-making, and improves the overall satisfaction of your data team. When your team spends less time debugging and more time driving value, it fosters a healthier data culture and supports long-term business growth.
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