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.

Read more

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.

Read more

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.

Read more

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 ?
Contact Us

Frequently asked questions

How can data observability help improve the happiness of my data team?
Great question! A strong data observability platform helps reduce uncertainty in your data pipelines by providing transparency, real-time metrics, and proactive anomaly detection. When your team can trust the data and quickly identify issues, they feel more confident, empowered, and less stressed, which directly boosts team morale and satisfaction.
How does Sifflet enhance data observability compared to traditional monitoring tools?
Sifflet takes data observability to the next level by combining metadata with AI-powered features like automated root cause analysis, anomaly detection, and impact mapping. Unlike basic monitoring tools, our observability platform doesn't just alert you—it explains what happened and guides you toward resolution, helping teams respond faster and with more confidence.
Why is data observability important for data transformation pipelines?
Great question! Data observability is essential for transformation pipelines because it gives teams visibility into data quality, pipeline performance, and transformation accuracy. Without it, errors can go unnoticed and create downstream issues in analytics and reporting. With a solid observability platform, you can detect anomalies, track data freshness, and ensure your transformations are aligned with business goals.
Who should be responsible for managing data quality in an organization?
Data quality management works best when it's a shared responsibility. Data stewards often lead the charge by bridging business needs with technical implementation. Governance teams define standards and policies, engineering teams build the monitoring infrastructure, and business users provide critical domain expertise. This cross-functional collaboration ensures that quality issues are caught early and resolved in ways that truly support business outcomes.
How can data observability help prevent missed SLAs and unreliable dashboards?
Data observability plays a key role in SLA compliance by detecting issues like ingestion latency, schema changes, or data drift before they impact downstream users. With proper data quality monitoring and real-time metrics, you can catch problems early and keep your dashboards and reports reliable.
What role does real-time monitoring play in Sifflet’s platform?
Real-time metrics are essential for proactive data pipeline monitoring. Sifflet’s observability tools provide real-time alerts and anomaly detection, helping teams quickly identify and resolve issues before they impact downstream systems or violate SLA compliance.
How does Sifflet support SLA compliance and proactive monitoring?
With real-time metrics and intelligent alerting, Sifflet helps ensure SLA compliance by detecting issues early and offering root cause analysis. Its proactive monitoring features, like dynamic thresholding and auto-remediation suggestions, keep your data pipelines healthy and responsive.
How does Sifflet support AI-ready data for enterprises?
Sifflet is designed to ensure data quality and reliability, which are critical for AI initiatives. Our observability platform includes features like data freshness checks, anomaly detection, and root cause analysis, making it easier for teams to maintain high standards and trust in their analytics and AI models.

More data. %%Less Chaos.%%

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

Contact Us