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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

How does Sifflet's integration with dbt Core improve data observability?

Great question! By integrating with dbt Core, Sifflet enhances data observability across your entire data stack. It helps you monitor dbt test coverage, map tests to downstream dependencies using data lineage tracking, and consolidate metadata like tags and descriptions, all in one place.

What are the main differences between ETL and ELT for data integration?

ETL (Extract, Transform, Load) transforms data before storing it, while ELT (Extract, Load, Transform) loads raw data first, then transforms it. With modern cloud storage, ELT is often preferred for its flexibility and scalability. Whichever method you choose, pairing it with strong data pipeline monitoring ensures smooth operations.

How does data lineage enhance data observability?

Data lineage adds context to data observability by linking alerts to their root cause. For example, if a metric suddenly drops, lineage helps trace it back to a delayed ingestion or schema change. This speeds up incident resolution and strengthens anomaly detection. Platforms like Sifflet combine lineage with real-time metrics and data freshness checks to provide a complete view of pipeline health.

What new dbt metadata can I now see in Sifflet?

You’ll now find key dbt metadata like the last execution timestamp and status directly within the dataset catalog and asset pages. This makes real-time metrics and pipeline health monitoring more accessible and actionable across your observability platform.

Will dbt Impact Analysis be available for other version control tools?

Yes! While it currently supports GitHub and GitLab, Sifflet is actively working on bringing dbt Impact Analysis to Bitbucket. This expansion ensures broader coverage and supports more teams in achieving better data governance and observability.

How does Sifflet support AI readiness within enterprises?

Sifflet reinforces AI-powered capabilities through features like data freshness checks, data profiling, and anomaly scoring. These tools ensure your data is accurate and trustworthy, which is crucial for training reliable machine learning models and enabling predictive analytics monitoring.

How do modern storage platforms like Snowflake and S3 support observability tools?

Modern platforms like Snowflake and Amazon S3 expose rich metadata and access patterns that observability tools can monitor. For example, Sifflet integrates with Snowflake to track schema changes, data freshness, and query patterns, while S3 integration enables us to monitor ingestion latency and file structure changes. These capabilities are key for real-time metrics and data quality monitoring.

Is Sifflet available for VPC deployment on Google Cloud?

Yes it is! You can deploy Sifflet’s observability platform within your own private Google Cloud environment using VPC deployment, giving you full control over data governance and security.

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