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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

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Frequently asked questions

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.

What kind of monitoring capabilities does Sifflet offer out of the box?

Sifflet comes with a powerful library of pre-built monitors for data profiling, data freshness checks, metrics health, and more. These templates are easily customizable, supporting both batch data observability and streaming data monitoring, so you can tailor them to your specific data pipelines.

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.

What kind of data quality monitoring features does Sifflet Insights offer?

Sifflet Insights offers features like real-time alerts, incident tracking, and access to metadata through your Data Catalog. These capabilities support proactive data quality monitoring and streamline root cause analysis when issues arise.

Can historical data access really boost data consumer confidence?

Absolutely! When data consumers can see historical performance through data observability dashboards, it builds transparency and trust. They’re more likely to rely on your data if they know it’s been consistently accurate and well-maintained over time.

How is data freshness different from latency or timeliness?

Great question! While these terms are often used interchangeably, they each mean something different. Data freshness is about how up-to-date your data is. Latency measures the delay from data generation to availability, and timeliness refers to whether that data arrives within expected time windows. Understanding these differences is key to effective data pipeline monitoring and SLA compliance.

How does MCP improve root cause analysis in modern data systems?

MCP empowers LLMs to use structured inputs like logs and pipeline metadata, making it easier to trace issues across multiple steps. This structured interaction helps streamline root cause analysis, especially in complex environments where traditional observability tools might fall short. At Sifflet, we’re integrating MCP to enhance how our platform surfaces and explains data incidents.

What’s on the horizon for data observability as AI and regulations evolve?

The future of data observability is all about scale and responsibility. With AI adoption growing and regulations tightening, businesses need observability tools that can handle unstructured data, ensure SLA compliance, and support security observability. At Sifflet, we're already helping customers monitor ML models and enforce data contracts, and we're excited about building self-healing pipelines and extending observability to new data types.

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