Databricks
Integrating Sifflet with Databricks enables end-to-end lineage, enriched metadata, and actionable insights to optimize your data observability strategy.
Used by
No items found.
Catalog all your Databricks assets
Sifflet retrieves metadata for all of your Databricks assets and enriches them with Sifflet-generated insights


End-to-end lineage
Have a complete understanding of how data flows through your platform via Sifflet's end-to-end lineage for Databricks.
Optimized monitors
Sifflet leverages Databricks capabilities like partition pruning to minimize the cost of monitors and increase efficiency.


Frequently asked questions
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 help with data lineage tracking?
Sifflet offers detailed data lineage tracking at both the table and field level. You can easily trace data upstream and downstream, which helps avoid unexpected issues when making changes. This transparency is key for data governance and ensuring trust in your analytics pipeline.
How do real-time alerts support SLA compliance?
Real-time alerts are crucial for staying on top of potential issues before they escalate. By setting up threshold-based alerts and receiving notifications through channels like Slack or email, teams can act quickly to resolve problems. This proactive approach helps maintain SLA compliance and keeps your data operations running smoothly.
How does data lineage support compliance with data privacy regulations?
Data lineage plays a key role in compliance monitoring by providing transparency into where data comes from, how it's processed, and where it ends up. This is crucial for meeting regulations like GDPR and HIPAA, and for maintaining strong data governance practices across the organization.
Can Sifflet help with data quality monitoring directly from the Data Catalog?
Absolutely! Sifflet integrates data quality monitoring into its Data Catalog, allowing users to define and view data quality checks right alongside asset metadata. This gives teams real-time insights into data reliability and helps build trust in the assets they’re using for decision-making.
Can non-technical users benefit from Sifflet’s data observability platform?
Absolutely. Sifflet is designed to be accessible to everyone. With an intuitive UI and our AI Assistant, even non-technical users can set up data quality monitors, track real-time metrics, and contribute to data governance without writing a line of code.
How does data observability differ from traditional data quality monitoring?
Great question! While data quality monitoring focuses on alerting teams when data deviates from expected parameters, data observability goes further by providing context through data lineage tracking, real-time metrics, and root cause analysis. This holistic view helps teams not only detect issues but also understand and fix them faster, making it a more proactive approach.
What is the difference between data monitoring and data observability?
Great question! Data monitoring is like your car's dashboard—it alerts you when something goes wrong, like a failed pipeline or a missing dataset. Data observability, on the other hand, is like being the driver. It gives you a full understanding of how your data behaves, where it comes from, and how issues impact downstream systems. At Sifflet, we believe in going beyond alerts to deliver true data observability across your entire stack.
Want to try Sifflet on your Databricks Stack?
Get in touch now!