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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

Who benefits from implementing a data observability platform like Sifflet?

Honestly, anyone who relies on data to make decisions—so pretty much everyone. Data engineers, BI teams, data scientists, RevOps, finance, and even executives all benefit. With Sifflet, teams get proactive alerts, root cause analysis, and cross-functional visibility. That means fewer surprises, faster resolutions, and more trust in the data that powers your business.

What does a modern data stack look like and why does it matter?

A modern data stack typically includes tools for ingestion, warehousing, transformation and business intelligence. For example, you might use Fivetran for ingestion, Snowflake for warehousing, dbt for transformation and Looker for analytics. Investing in the right observability tools across this stack is key to maintaining data reliability and enabling real-time metrics that support smart, data-driven decisions.

What’s coming next for the Sifflet AI Assistant?

We’re excited about what’s ahead. Soon, the Sifflet AI Assistant will allow non-technical users to create monitors using natural language, expand monitoring coverage automatically, and provide deeper insights into resource utilization and capacity planning to support scalable data observability.

Is Datadog a good fit for teams focused on data reliability and governance?

Datadog is a strong choice for infrastructure and system observability, but it may not be the best fit for teams focused on data reliability and data governance. While it offers some data quality monitoring through Metaplane, it lacks the business context and advanced data lineage tracking needed to ensure trust in your analytics. For those priorities, a dedicated data observability platform like Sifflet is better equipped.

What is data observability and why is it important for modern data teams?

Data observability is the ability to monitor, understand, and troubleshoot data health across the entire data stack. It's essential for modern data teams because it helps ensure data reliability, improves trust in analytics, and prevents costly issues caused by broken data pipelines or inaccurate dashboards. With the rise of complex infrastructures and real-time data usage, having a strong observability platform in place is no longer optional.

How can organizations balance the need for data accuracy with the cost of achieving it?

That's a smart consideration! While 100% accuracy sounds ideal, it's often costly and unrealistic. A better approach is to define acceptable thresholds through data validation rules and data profiling. By using observability platforms that support threshold-based alerts and dynamic thresholding, teams can focus on what matters most without over-investing in perfection.

How does Sifflet help teams improve data accessibility across the organization?

Great question! Sifflet makes data accessibility a breeze by offering intuitive search features and AI-generated metadata, so both technical and non-technical users can easily find and understand the data they need. This helps break down silos and supports better collaboration, which is a key component of effective data observability.

How can data observability help with SLA compliance and incident management?

Data observability plays a huge role in SLA compliance by enabling real-time alerts and proactive monitoring of data freshness, completeness, and accuracy. When issues occur, observability tools help teams quickly perform root cause analysis and understand downstream impacts, speeding up incident response and reducing downtime. This makes it easier to meet service level agreements and maintain stakeholder trust.

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