The Control Plane for %%Data and AI%%

We catch data issues before they reach the business, show exactly why they happened, and how to fix them. So the data behind every decision is one you can trust.

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.

Dynex Capital
Euronext
Dailymotion
Saint-Gobain
ShopBack
Servier
Penguin Random House
Adaptavist
Mollie
Hypebeast
Deuna
BBC Studios
Carrefour
Etam
Auchan

Your pipelines are monitored. Your alerts are firing. %%So why does bad data keep reaching the business?%%

Detection is table stakes. What matters is what happens next: why it broke, what it affects, and how to fix it.

Know What Actually Matters

Not all alerts are equal. Sifflet enriches every issue with lineage, downstream usage, and ownership — so you stop treating schema drift and a broken exec dashboard the same way. Focus on what has real business consequences.

Stop Playing Detective

When something breaks, the context you need is already there: upstream lineage, recent schema changes, historical behavior. The root cause you'd spend hours hunting, surfaced in minutes.

One Control Layer Across Your Full Stack

Incidents don't respect tool boundaries. Sifflet covers the whole chain — warehouses, orchestrators, BI — so nothing falls through the gap between Snowflake and the dashboard your CFO opens on Monday morning.

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, trace it. Map any issue upstream, downstream, and across layers — field by field. Know exactly where a number came from, what it affects, and how to fix it. A lineage gap is a trust gap. Sifflet closes it.

Data quality monitoring

Monitor

Monitor everything. Miss nothing. Out-of-the-box and custom monitoring across every asset — including the ones you didn't know to watch. AI reduces noise as your stack grows, so your team stays focused on signals that matter, not the ones that don't.

Data reliability is a team sport

The right view for everyone in the buying center: the people who build it, the people who govern it, and the people who depend on it.

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
Dynex Capital
Euronext
Dailymotion
Saint-Gobain
ShopBack
Servier
Penguin Random House
Adaptavist
Mollie
Hypebeast
Deuna
BBC Studios
Carrefour
Etam
Auchan
Still have a question in mind ?
Contact Us

Frequently asked questions

What are the key components of an end-to-end data platform?
An end-to-end data platform includes layers for ingestion, storage, transformation, orchestration, governance, observability, and analytics. Each part plays a role in making data reliable and actionable. For example, data lineage tracking and real-time metrics collection help ensure transparency and performance across the pipeline.
Can MCP help with root cause analysis in data systems?
Absolutely. MCP gives LLMs the ability to retain memory across multi-step interactions and call external tools, which is incredibly useful for root cause analysis. At Sifflet, we use this to build agents that can pinpoint anomalies, trace data lineage, and surface relevant logs automatically.
Can data lineage help with regulatory compliance such as GDPR?
Absolutely. Data lineage supports data governance by mapping data flows and access rights, which is essential for compliance with regulations like GDPR. Features like automated PII propagation help teams monitor sensitive data and enforce security observability best practices.
Why is table-level lineage important for data quality monitoring and governance?
Table-level lineage helps you understand how data flows through your systems, which is essential for data quality monitoring and data governance. It supports impact analysis, pipeline debugging, and compliance by showing how changes in upstream tables affect downstream assets.
How does Sifflet help with compliance monitoring and audit logging?
Sifflet is ISO 27001 certified and SOC 2 compliant, and we use a separate secret manager to handle credentials securely. This setup ensures a strong audit trail and tight access control, making compliance monitoring and audit logging seamless for your data teams.
How does Sentinel help reduce alert fatigue in modern data environments?
Sentinel intelligently analyzes metadata like data lineage and schema changes to recommend what really needs monitoring. By focusing on high-impact areas, it cuts down on noise and helps teams manage alert fatigue while optimizing monitoring costs.
Why is having a metadata strategy important for using a metadata catalog effectively?
A metadata catalog is powerful, but without a clear metadata strategy, it can become just another long list of tables. A good strategy includes classifying data by business criticality, assigning ownership, and defining consistent terminology. This helps automation scale efficiently while human oversight ensures context and trust, which is key for proactive monitoring and data governance.
Why is data observability important for monetizing data products?
When you're selling data, trust is everything. Data observability ensures your data is accurate, fresh, and traceable, which builds client confidence. Carrefour, for example, used observability to monitor over 800 assets and enforce data quality across 8 countries, making their data products reliable and revenue-generating at scale.

More data. %%Less Chaos.%%

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

Contact Us