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.


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


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Frequently asked questions
What kind of real-time alerts can I expect with Sifflet and dbt together?
With Sifflet and dbt working together, you get real-time alerts delivered straight to your favorite tools like Slack, Microsoft Teams, or email. Whether a dbt test fails or a data anomaly is detected, your team will be notified immediately, helping you respond quickly and maintain data quality monitoring at all times.
Does Sifflet support AI-driven use cases?
Yes, Sifflet leverages AI to enhance data observability with features like anomaly detection and predictive insights. This ensures your data systems remain resilient and can support advanced analytics and AI-driven initiatives. Have a look at how Sifflet is leveraging AI for better data observability here
What is SQL Table Tracer and how does it help with data observability?
SQL Table Tracer (STT) is a lightweight library that extracts table-level lineage from SQL queries. It plays a key role in data observability by identifying upstream and downstream tables, making it easier to understand data dependencies and track changes across your data pipelines.
Will Sifflet cover any upcoming trends in data observability?
For sure! Our CEO, Salma Bakouk, will be speaking about the top data trends to watch in 2025, including how GenAI and advanced anomaly detection are shaping the future of observability platforms. You’ll walk away with actionable insights for your data strategy.
Can I use Sifflet to detect bad-quality data in my Airflow pipelines?
Absolutely! With Sifflet’s data quality monitoring integrated into Airflow DAGs, you can detect and isolate bad-quality data before it impacts downstream processes. This helps maintain high data reliability and supports SLA compliance.
Why is data categorization important for data governance and compliance?
Effective data categorization is essential for data governance and compliance because it helps identify sensitive data like PII, ensuring the correct protection policies are applied. With Sifflet’s classification tags, governance teams can easily locate and safeguard sensitive information, supporting GDPR data monitoring and overall data security compliance.
What should I look for in a data lineage tool?
When choosing a data lineage tool, look for easy integration with your data stack, a user-friendly interface for both technical and non-technical users, and complete visibility from data sources to storage. These features ensure effective data observability and support your broader data governance efforts.
How does Sifflet support enterprises with data pipeline monitoring?
Sifflet provides a comprehensive observability platform that monitors the health of data pipelines through features like pipeline error alerting, data freshness checks, and ingestion latency tracking. This helps teams identify issues early and maintain SLA compliance across their data workflows.



















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