Make Data %%Observability%% Everyone’s Business

Sifflet is an AI-augmented data observability platform built for data teams with business users in mind.

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.

See Data Breakthroughs

Sifflet helps you remove the obstacles that stand in the way of superior insights, value, and products from data.

Supercharge Productivity 

Data engineers spend up 50% of their time on mundane reliability tasks and data analysts between 40 to 80% of their time vetting data quality. Sifflet augments your team’s capabilities and supercharges their productivity. 

Uplevel Data Reliability
and Quality 

See next-level improvements to data reliability and quality thanks to tools that make it easier and faster than ever to find and fix your data. 

Empower Ownership, Enable Self-Serve 

Sifflet ensures that your colleagues always know the health status of data, can give input to monitors, and take ownership of their data assets. Collaboration with data teams improves and it’s easier to enable data-mesh and self-serve.

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 data monitoring
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.

Sifllet dashboard data quality monitoring
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. 

Built for %%Everyone%%

Sifflet helps you remove the obstacles that stand in the way of superior insights, value, and products from 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

What role does data quality monitoring play in a data catalog?
Data quality monitoring ensures your data is accurate, complete, and consistent. A good data catalog should include profiling and validation tools that help teams assess data quality, which is crucial for maintaining SLA compliance and enabling proactive monitoring.
Why is technology critical to scaling data governance across teams?
Technology automates key governance tasks such as data classification, access control, and telemetry instrumentation. With the right tools, like a data observability platform, organizations can enforce policies at scale, detect anomalies automatically, and integrate governance into daily workflows. This reduces manual effort and ensures governance grows with the business.
Is Forge able to automatically fix data issues in my pipelines?
Forge doesn’t take action on its own, but it does provide smart, contextual guidance based on past fixes. It helps teams resolve issues faster while keeping you in full control of the resolution process, which is key for maintaining SLA compliance and data quality monitoring.
What is reverse ETL and why is it important in the modern data stack?
Reverse ETL is the process of moving data from your data warehouse into external systems like CRMs or marketing platforms. It plays a crucial role in the modern data stack by enabling operational analytics, allowing business teams to act on real-time metrics and make data-driven decisions directly within their everyday tools.
Why is investing in data observability important for business leaders?
Great question! Investing in data observability helps organizations proactively monitor the health of their data, reduce the risk of bad data incidents, and ensure data quality across pipelines. It also supports better decision-making, improves SLA compliance, and helps maintain trust in analytics. Ultimately, it’s a strategic move that protects your business from costly mistakes and missed opportunities.
How did Sifflet help Meero reduce the time spent on troubleshooting data issues?
Sifflet significantly cut down Meero's troubleshooting time by enabling faster root cause analysis. With real-time alerts and automated anomaly detection, the data team was able to identify and resolve issues in minutes instead of hours, saving up to 50% of their time.
What role did data quality monitoring play in jobvalley’s success?
Data quality monitoring was key to jobvalley’s success. By using Sifflet’s data observability tools, they were able to validate the accuracy of business-critical tables, helping build trust in their data and supporting confident, data-driven decision-making.
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.

More data. %%Less Chaos.%%

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

Contact Us