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Integrates with your %%modern data stack%%

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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The Sifflet team is always working hard on incorporating more integrations into our product. Get in touch if you want us to keep you updated!

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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

Can Sifflet integrate with my existing data stack for seamless data pipeline monitoring?

Absolutely! One of Sifflet’s strengths is its seamless integration across your existing data stack. Whether you're working with tools like Airflow, Snowflake, or Kafka, Sifflet helps you monitor your data pipelines without needing to overhaul your infrastructure.

How does data observability differ from traditional data quality monitoring?

Great question! Traditional data quality monitoring focuses on pre-defined rules and tests, but it often falls short when unexpected issues arise. Data observability, on the other hand, provides end-to-end visibility using telemetry instrumentation like metrics, metadata, and lineage. This makes it possible to detect anomalies in real time and troubleshoot issues faster, even in complex data environments.

What does it mean to treat data as a product?

Treating data as a product means managing data with the same care and strategy as a traditional product. It involves packaging, maintaining, and delivering high-quality data that serves a specific purpose or audience. This approach improves data reliability and makes it easier to monetize or use for strategic decision-making.

What’s the main difference between ETL and ELT?

Great question! While both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration methods, the key difference lies in the order of operations. ETL transforms data before loading it into a data warehouse, whereas ELT loads raw data first and transforms it inside the warehouse. ELT has become more popular with the rise of cloud data warehouses like Snowflake and BigQuery, which offer scalable storage and computing power. If you're working with large volumes of data, ELT might be the better fit for your data pipeline monitoring strategy.

What exactly is data observability, and how is it different from traditional data monitoring?

Great question! Data observability goes beyond traditional data monitoring by not only detecting when something breaks in your data pipelines, but also understanding why it matters. While monitoring might tell you a pipeline failed, data observability connects that failure to business impact—like whether your CFO’s dashboard is now showing outdated numbers. It's about trust, context, and actionability.

What’s the difference between a data schema and a database schema?

Great question! A data schema defines structure across your entire data ecosystem, including pipelines, APIs, and ingestion tools. A database schema, on the other hand, is specific to one system, like PostgreSQL or BigQuery, and focuses on tables, columns, and relationships. Both are essential for effective data governance and observability.

How does Sifflet support both technical and business teams?

Sifflet is designed to bridge the gap between data engineers and business users. It combines powerful features like automated anomaly detection, data lineage, and context-rich alerting with a no-code interface that’s accessible to non-technical teams. This means everyone—from analysts to execs—can get real-time metrics and insights about data reliability without needing to dig through logs or write SQL. It’s observability that works across the org, not just for the data team.

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.

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