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Get ahead of business issues before they become business catastrophes.

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.

Join the ranks of happy customers who’ve made Sifflet a G2 leader, trusted for its innovation and impact

Stay ahead of issues with real-time alerts that keep you informed and in control of your data health

Organize, discover, and leverage your data assets effortlessly with a smart, searchable catalog built for modern teams.

Harness the power of AI-driven suggestions to improve efficiency, accuracy, and decision-making across your workflows.

Empower your team with tailored access, enabling secure collaboration that drives smarter decisions.

Frequently asked questions

Why is this integration important for data pipeline monitoring?

Bringing Sifflet’s observability tools into Apache Airflow allows for proactive data pipeline monitoring. You get real-time metrics, anomaly detection, and data freshness checks that help you catch issues early and keep your pipelines healthy.

What are the main trade-offs of using Datadog for data pipeline monitoring?

The main trade-offs of using Datadog for data pipeline monitoring include high costs, especially in high-cardinality environments, and limited visibility into the actual data content. While Datadog is great for real-time metrics and infrastructure observability, it doesn't provide deep data validation rules or business-aware anomaly detection. Teams needing those capabilities may want to pair it with a more focused data observability solution.

How does Sifflet support data pipeline monitoring at Carrefour?

Sifflet enables comprehensive data pipeline monitoring through features like monitoring-as-code and seamless integration with data lineage tracking and governance tools. This gives Carrefour full visibility into their pipeline health and helps ensure SLA compliance.

Why is data observability a crucial part of the modern data stack?

Data observability is essential because it ensures data reliability across your entire stack. As data pipelines grow more complex, having visibility into data freshness, quality, and lineage helps prevent issues before they impact the business. Tools like Sifflet offer real-time metrics, anomaly detection, and root cause analysis so teams can stay ahead of data problems and maintain trust in their analytics.

How does Sifflet help with data discovery across different tools like Snowflake and BigQuery?

Great question! Sifflet acts as a unified observability platform that consolidates metadata from tools like Snowflake and BigQuery into one centralized Data Catalog. By surfacing tags, labels, and schema details, it makes data discovery and governance much easier for all stakeholders.

How does Sifflet automate data quality monitoring?

Sifflet uses Sentinel, an AI-powered agent, to automate data quality monitoring. It scans your metadata and data samples to suggest monitors for data freshness checks, schema validation, and more. This means you get proactive monitoring with minimal manual setup, making it easier to scale your observability efforts.

How can enterprise data teams benefit from implementing a data observability platform?

Great question! A data observability platform helps enterprise teams monitor data quality, detect anomalies in real time, and reduce incident response time. This leads to better decision-making, improved SLA compliance, and optimized cloud costs. Companies like Etam and Nextbite have seen major improvements in reliability and efficiency after adopting observability tools.

What role does root cause analysis play in metadata observability?

Root cause analysis is essential for resolving complex data issues quickly. Sifflet uses AI-driven root cause analysis to identify the exact source of problems across your metadata layer, whether it's schema incompatibility or catalog drift. This proactive approach helps maintain SLA compliance and reduces incident resolution time.

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