From Detection to %%Decision%%

Sifflet is the business-aware data observability platform that connects data quality issues to real business impact.
Know exactly what to fix first, and why.

The premier %%virtual summit%% on data reliability, observability, and the future of trustworthy AI.

What Our Customers Say

See Sifflet in action!

Curious about how Sifflet can transform the way your team works with data?

Join our 30-min biweekly demo to see how data leaders, engineers, and platform teams use Sifflet to detect, resolve, and prevent issues—before they impact the business.

Your pipelines are monitored. Your alerts are firing. So why does every incident still feel like a scramble?

You don't have a detection problem. You have a decision problem.

Prioritize by Business Risk, Not Just Technical Severity

Treating all anomalies equally creates alert fatigue. Sifflet answers "Does this matter, and to whom?" by enriching every data quality check with lineage, downstream BI usage, and ownership. Focus your engineering effort on incidents with real business consequences.

Faster Triage Through Context-Enriched Investigation

Stop playing detective. When an incident occurs, Sifflet centralizes the context you usually have to hunt for: upstream and downstream lineage, recent schema changes, and historical behavior. Cut MTTR from hours to minutes.

One Reliability Layer, End-to-End

Business impact shows up downstream. Sifflet provides a unified observability layer across your entire modern data stack, from warehouses (Snowflake, BigQuery) and orchestrators (Airflow, dbt) directly to your BI platforms (Tableau, Looker).

TRACEABLE

Improve productivity and collaboration between engineers and data consumers

For everyone, working with and finding data becomes intuitive with a simple and automated UI, data discovery is simplified with a data catalog, and it is easy to connect with coding workflows.

Sifflet dashboard features overview
Sifflet dashboard features overview
Data Lineage

Troubleshoot

When data breaks, take charge. Use Sifflet’s robust tracing capabilities to map your data upstream, downstream and across data layers. You’ll gain insight into your data across the entire lifecycle and see rapid improvements to data quality that benefit the entire company.

Data quality monitoring

Monitor

Monitor it all. And more.  Sifflet offers both out of the box and custom monitoring capability, so your teams can keep an eye on assets you know need observation…and even those you don’t.  Our AI optimizes your coverage and minimizes noise, getting smarter as it goes.  Your data’s reliability is reinforced, helping to grow confidence in your numbers. Now that’s performance. 

Data reliability is a team sport

Tailored context for the people who build, govern, and consume data.

Data Leaders

Drive innovation and enable AI. With Sifflet, you can transform your data strategy, governance, and team productivity while ensuring efficient and scalable data infrastructure.

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Data Engineers

Boost your productivity. Sifflet gives you end-to-end visibility into your architecture, assets, and pipelines. Advanced monitoring ensures you get the right alerts and lineage helps you get to resolution faster.

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Data Users

No more data discrepancies. Sifflet ensures the highest levels of data quality. Your teams can make the best possible decisions for your company, unlocking new levels of performance that help you compete in the age of AI.

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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
Still have a question in mind ?
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Frequently asked questions

How does the new Fivetran integration enhance data observability in Sifflet?
Great question! With our new Fivetran integration, Sifflet now provides visibility into your data's journey even before it reaches your data platform. This means you can track data from its source through Fivetran connectors all the way downstream, offering truly end-to-end data observability.
How does aligning data observability with business objectives improve outcomes?
Aligning data observability with business goals transforms data from a technical asset into a strategic one. By setting clear KPIs and linking data quality monitoring to business impact, teams can make smarter decisions, improve SLA compliance, and drive real value from their data investments.
What makes a metadata catalog different from a traditional data catalog?
Great question! A metadata catalog goes beyond just listing data assets. It enriches technical metadata with business context like ownership, definitions, and data quality scores. This makes it easier for users to trust what they find, and it supports advanced features like data lineage tracking, data freshness checks, and automated impact analysis. It's a big leap forward in data discovery and governance.
What are the main challenges of implementing Data as a Product?
Some key challenges include ensuring data privacy and security, maintaining strong data governance, and investing in data optimization. These areas require robust monitoring and compliance tools. Leveraging an observability platform can help address these issues by providing visibility into data lineage, quality, and pipeline performance.
How do real-time alerts support SLA compliance?
Real-time alerts are crucial for staying on top of potential issues before they escalate. By setting up threshold-based alerts and receiving notifications through channels like Slack or email, teams can act quickly to resolve problems. This proactive approach helps maintain SLA compliance and keeps your data operations running smoothly.
Why is data observability becoming more important in 2024?
Great question! As AI and real-time data products become more widespread, data observability is crucial for ensuring data reliability, privacy, and performance. A strong observability platform helps reduce data chaos by monitoring pipeline health, identifying anomalies, and maintaining SLA compliance across increasingly complex data ecosystems.
Is data observability relevant for small businesses?

Yes! While smaller organizations may have fewer data pipelines, ensuring data quality and reliability is equally important for making accurate decisions and scaling effectively. What really matters is the data stack maturity and volume of data. Take our test here to find out if you really need data observability.

What can I expect from Sifflet at Big Data Paris 2024?
We're so excited to welcome you at Booth #D15 on October 15 and 16! You’ll get to experience live demos of our latest data observability features, hear real client stories like Saint-Gobain’s, and explore how Sifflet helps improve data reliability and streamline data pipeline monitoring.

More data. %%Less Chaos.%%

If you want a smoother running stack,
let’s talk about what Sifflet can do for you. 

Contact Us