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.

Customize to Your Heart’s Content

Sifflet offers both a robust library of out of the box monitors and customization capability. Your teams decide what needs monitoring and how to set it up.

Customize to Your Heart’s Content

Sifflet offers both a robust library of out of the box monitors and customization capability. Your teams decide what needs monitoring and how to set it up.

Customize to Your Heart’s Content

Sifflet offers both a robust library of out of the box monitors and customization capability. Your teams decide what needs monitoring and how to set it up.

TACABLE

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

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.

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
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Frequently asked questions

How does Sifflet handle cross-engine compatibility issues?
Sifflet provides real-time metadata observability that detects when different engines, like Spark and Trino, interpret table schemas differently. By tracking atomic commits and metadata snapshots, Sifflet flags compatibility issues early, preventing downstream failures in dashboards or analytics tools.
What makes Sifflet a strong alternative to Monte Carlo for data observability?
Sifflet stands out as a modern data observability platform that combines AI-powered monitoring with business context. Unlike Monte Carlo, Sifflet offers no-code monitor creation, dynamic alerting with impact insights, and real-time data lineage tracking. It's designed for both technical and business users, making it easier for teams to collaborate and maintain data reliability across the organization.
How can enterprise data teams benefit from implementing a data observability platform?
Great question! A data observability platform helps enterprise teams monitor data quality, detect anomalies in real time, and reduce incident response time. This leads to better decision-making, improved SLA compliance, and optimized cloud costs. Companies like Etam and Nextbite have seen major improvements in reliability and efficiency after adopting observability tools.
Who should be the first hire on a new data team?
If you're just starting out, look for someone with 'Full Data Stack' capabilities, like a Data Analyst with strong SQL and business acumen or a Data Engineer with analytics skills. This person can work closely with other teams to build initial pipelines and help shape your data platform. As your needs evolve, you can grow your team with more specialized roles.
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.
How does Sifflet support real-time data lineage and observability?
Sifflet provides automated, field-level data lineage integrated with real-time alerts and anomaly detection. It maps how data flows across your stack, enabling quick root cause analysis and impact assessments. With features like data drift detection, schema change tracking, and pipeline error alerting, Sifflet helps teams stay ahead of issues and maintain data reliability.
How can I keep passive metadata accurate and useful over time?
To maintain high-quality passive metadata, Sifflet recommends a mix of automated ingestion and manual curation. Connect your data sources, standardize tagging, build a business glossary, and schedule regular reviews. This helps ensure your data profiling and data validation rules stay aligned with evolving business needs.
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.

More data. %%Less Chaos.%%

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

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