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Data Leader

Transform your data and analytics strategy and pave the way for AI by upleveling data quality, trust, reliability and overall team efficiency.

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Data Quality and Trust

Sifflet makes it possible to establish trust in data across your organization thanks to real time monitoring of data quality, completeness, and accuracy.

Operational Efficiency

Increase your team’s operational efficiency. Sifflet reduces the time your data teams spend on manual quality checks and troubleshooting. It also enables proactive issue resolution before problems cause downstream systems.

Risk and Compliance Management

Manage data risk and compliance. Sifflet helps you document and monitor data access patterns and potential security risks.

Drive Innovation and Enable AI

Sifflet’s data observability platform delivers the performance you need to keep data quality and reliability at peak, paving the way for game-changing digital capabilities and products.

Augment Your Team’s Productivity and Effectiveness

Data engineers, data analysts and data scientists are critical to your business’s most strategic work. Sifflet augments their productivity by giving them back hundreds of hours spent on mundane reliability or accuracy tasks. Everyone’s more effective with data observability.

See Value From Day One

Sifflet connects to hundreds of tools already in your stack and offers out of the box monitors and tooling so you can start seeing value from day one.

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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Data Governance - A Thought Leader's Perspective

A Guide to Assessing your Need for Data Observability

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Frequently asked questions

Why are data consumers becoming more involved in observability decisions?

We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.

How do JOIN strategies affect query execution and data observability?

JOINs can be very resource-intensive if not used correctly. Choosing the right JOIN type and placing conditions in the ON clause helps reduce unnecessary data processing, which is key for effective data observability and real-time metrics tracking.

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.

Why is data observability important for business outcomes?

Data observability helps align technical metrics with strategic business goals. By monitoring real-time metrics and enabling root cause analysis, teams can quickly detect and resolve data issues, reducing downtime and improving decision-making. It’s not just about the data, it’s about the impact that data has on your business.

Is there a way to use Sifflet with Terraform for better data governance?

Yes! Sifflet now offers an officially-supported Terraform provider that allows you to manage your observability setup as code. This includes configuring monitors and other Sifflet objects, which helps enforce data contracts, improve reproducibility, and strengthen data governance.

How does Sifflet support both technical and business teams?

Sifflet is designed to bridge the gap between data engineers and business users. It combines powerful features like automated anomaly detection, data lineage, and context-rich alerting with a no-code interface that’s accessible to non-technical teams. This means everyone—from analysts to execs—can get real-time metrics and insights about data reliability without needing to dig through logs or write SQL. It’s observability that works across the org, not just for the data team.

How does data observability improve incident response and SLA compliance?

With data observability, teams get real-time metrics and deep context around data issues. This means faster incident response and better SLA compliance. Sifflet’s observability platform helps you pinpoint root causes quickly, reducing downtime and giving stakeholders confidence in the reliability of your data.

Why is the new join feature in the monitor UI a game changer for data quality monitoring?

The ability to define joins directly in the monitor setup interface means you can now monitor relationships across datasets without writing custom SQL. This is crucial for data quality monitoring because many issues arise from inconsistencies between related tables. Now, you can catch those problems early and ensure better data reliability across your pipelines.

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