Solution by Use Case

Data governance

Proactive access, quality and control

Empower data teams to detect and address issues proactively by providing them with tools to ensure data availability, usability, integrity, and security.

Take a Tour

De-risked data discovery

Ensure proactive data quality thanks to a large library of OOTB monitors and a built-in notification system
Gain visibility over assets’ documentation and health status on the Data Catalog for safe data discovery
Establish the official source of truth for key business concepts using the Business Glossary
Leverage custom tagging to classify assets

Structured data observability platform

Tailor data visibility for teams by grouping assets in domains that align with the company’s structure
Define data ownership to improve accountability and smooth collaboration across teams

Secured data management

Safeguard PII data securely through ML-based PII detection

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

Discover more title goes here

Still have a question in mind ?

Frequently asked questions

Can Sifflet integrate with my existing data stack for seamless data pipeline monitoring?

Absolutely! One of Sifflet’s strengths is its seamless integration across your existing data stack. Whether you're working with tools like Airflow, Snowflake, or Kafka, Sifflet helps you monitor your data pipelines without needing to overhaul your infrastructure.

Why does query formatting matter in modern data operations?

Well-formatted queries are easier to debug, share, and maintain. This aligns with DataOps best practices and supports transparency in data pipelines, which is essential for consistent SLA compliance and proactive monitoring.

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.

What exactly is the modern data stack, and why is it so popular now?

The modern data stack is a collection of cloud-native tools that help organizations transform raw data into actionable insights. It's popular because it simplifies data infrastructure, supports scalability, and enables faster, more accessible analytics across teams. With tools like Snowflake, dbt, and Airflow, teams can build robust pipelines while maintaining visibility through data observability platforms like Sifflet.

Why is data observability important during cloud migration?

Great question! Data observability helps you monitor the health and integrity of your data as it moves to the cloud. By using an observability platform, you can track data lineage, detect anomalies, and validate consistency between environments, which reduces the risk of disruptions and broken pipelines.

How does Sifflet help with root cause analysis and incident resolution?

Sifflet provides advanced root cause analysis through complete data lineage and AI-powered anomaly detection. This means teams can quickly trace issues across pipelines and transformations, assess business impact, and resolve incidents faster with smart, context-aware alerts.

How does Sifflet support data quality monitoring for large organizations?

Sifflet is built to scale. It supports automated data quality monitoring across hundreds of assets, as seen with Carrefour Links monitoring over 800 data assets in 8+ countries. With dynamic thresholding, schema change detection, and real-time metrics, Sifflet ensures SLA compliance and consistent data reliability across complex ecosystems.

How do Service Level Indicators (SLIs) help improve data product reliability?

SLIs are a fantastic way to measure the health and performance of your data products. By tracking metrics like data freshness, anomaly detection, and real-time alerts, you can ensure your data meets expectations and stays aligned with your team’s SLA compliance goals.

Read all FAQs

Proactive access, quality and control