A first look at Sifflet, the data observability platform for data producers and users

In this simple demo, you'll find out how to easily spot an incident, solve it and save your business sanity!

Show Your Stack Who’s Boss

Unified data observability that packs a three-in-one punch. From data discovery to integrated monitoring and troubleshooting capabilities, you’ll be the one in charge.

Seamlessly connect with all your favorite data tools to centralize insights and unlock the full potential of your data ecosystem.
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Join the ranks of happy customers who’ve made Sifflet a G2 leader, trusted for its innovation and impact
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Stay ahead of issues with real-time alerts that keep you informed and in control of your data health
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Organize, discover, and leverage your data assets effortlessly with a smart, searchable catalog built for modern teams.
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Harness the power of AI-driven suggestions to improve efficiency, accuracy, and decision-making across your workflows.
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Empower your team with tailored access, enabling secure collaboration that drives smarter decisions.
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.

Register for our biweekly demo now!
ACHIEVE

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.

TEAMS

Built for

Built for Everyone

Everyone

Data Leaders

Data Engineers

Data Users

Everyone

Sifflet allows everyone to own and build confidence in data thanks to features that make it easy to access, understand and contribute to data reliability and quality.

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.

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.

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.

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

Frequently asked questions

What role does containerization play in data observability?
Containerization enhances data observability by enabling consistent and isolated environments, which simplifies telemetry instrumentation and anomaly detection. It also supports better root cause analysis when issues arise in distributed systems or microservices architectures.
How can business teams benefit from using Sifflet Insights?
Business teams can access data quality insights directly within their BI dashboards, reducing their reliance on data engineers. This democratizes data observability and empowers teams to make confident, data-driven decisions with full transparency into data lineage and reliability.
How does Sifflet support local development workflows for data teams?
Sifflet is integrating deeply with local development tools like dbt and the Sifflet CLI. Soon, you'll be able to define monitors directly in dbt YAML files and run them locally, enabling real-time metrics checks and anomaly detection before deployment, all from your development environment.
Can Sifflet help with root cause analysis in complex data systems?
Absolutely! In early 2025, we're rolling out advanced root cause analysis tools designed to help you detect subtle anomalies and trace them back to their source. Whether the issue lies in your code, data, or pipelines, our observability platform will help you get to the bottom of it faster.
Why is data observability important when using ETL or ELT tools?
Data observability is crucial no matter which integration method you use. With ETL or ELT, you're moving and transforming data across multiple systems, which can introduce errors or delays. An observability platform like Sifflet helps you track data freshness, detect anomalies, and ensure SLA compliance across your pipelines. This means fewer surprises, faster root cause analysis, and more reliable data for your business teams.
How does Sifflet's Data Sharing feature help with enforcing data governance policies?
Great question! Sifflet's Data Sharing provides access to rich metadata about your data assets, including tags, owners, and monitor configurations. By making this available in your own data warehouse, you can set up automated checks to ensure compliance with your governance standards. It's a powerful way to implement scalable data governance and reduce manual audits using our observability platform.
What features should we look for in scalable data observability tools?
When evaluating observability tools, scalability is key. Look for features like real-time metrics, automated anomaly detection, incident response automation, and support for both batch data observability and streaming data monitoring. These capabilities help teams stay efficient as data volumes grow.
How does SQL Table Tracer handle different SQL dialects?
SQL Table Tracer uses Antlr4 with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This flexible parsing approach ensures accurate lineage extraction across diverse environments, which is essential for data pipeline monitoring and distributed systems observability.
Still have questions?

More Data. Less Chaos.

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