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Sifflet seamlessly integrates into your data sources and preferred tools, and can run on AWS, Google Cloud Platform, and Microsoft Azure.

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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

How does the shift from ETL to ELT impact data pipeline monitoring?

The move from ETL to ELT allows organizations to load raw data into the warehouse first and transform it later, making pipeline management more flexible and cost-effective. However, it also increases the need for data pipeline monitoring to ensure that transformations happen correctly and on time. Observability tools help track ingestion latency, transformation success, and data drift detection to keep your pipelines healthy.

What practical steps can companies take to build a data-driven culture?

To build a data-driven culture, start by investing in data literacy, aligning goals across teams, and adopting observability tools that support proactive monitoring. Platforms with features like metrics collection, telemetry instrumentation, and real-time alerts can help ensure data reliability and build trust in your analytics.

Why is technology critical to scaling data governance across teams?

Technology automates key governance tasks such as data classification, access control, and telemetry instrumentation. With the right tools, like a data observability platform, organizations can enforce policies at scale, detect anomalies automatically, and integrate governance into daily workflows. This reduces manual effort and ensures governance grows with the business.

How does SQL Table Tracer handle complex SQL features like CTEs and subqueries?

SQL Table Tracer uses a Monoid-based design to handle complex SQL structures like Common Table Expressions (CTEs) and subqueries. This approach allows it to incrementally and safely compose lineage information, ensuring accurate root cause analysis and data drift detection.

How does integrating a data catalog with observability tools improve pipeline monitoring?

When integrated with observability tools, a data catalog becomes more than documentation. It provides real-time metrics, data freshness checks, and anomaly detection, allowing teams to proactively monitor pipeline health and quickly respond to issues. This integration enables faster root cause analysis and more reliable data delivery.

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.

What makes Sifflet's approach to data pipeline monitoring unique?

We take a holistic, end-to-end approach to data pipeline monitoring. By collecting telemetry across the entire data stack and automatically tracking field-level data lineage, we empower teams to quickly identify issues and understand their downstream impact, making incident response and resolution much more efficient.

What role does real-time monitoring play in Sifflet’s platform?

Real-time metrics are essential for proactive data pipeline monitoring. Sifflet’s observability tools provide real-time alerts and anomaly detection, helping teams quickly identify and resolve issues before they impact downstream systems or violate SLA compliance.

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