Databricks
Integrating Sifflet with Databricks enables end-to-end lineage, enriched metadata, and actionable insights to optimize your data observability strategy.
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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
How does reverse ETL improve data reliability and reduce manual data requests?
Reverse ETL automates the syncing of data from your warehouse to business apps, helping reduce the number of manual data requests across teams. This improves data reliability by ensuring consistent, up-to-date information is available where it’s needed most, while also supporting SLA compliance and data automation efforts.
What’s coming next for dbt integration in Sifflet?
We’re just getting started! Soon, you’ll be able to monitor dbt run performance and resource utilization, define monitors in your dbt YAML files, and use custom metadata even more dynamically. These updates will further enhance your cloud data observability and make your workflows even more efficient.
What makes Sifflet’s data lineage tracking stand out?
Sifflet offers one of the most advanced data lineage tracking capabilities out there. Think of it like a GPS for your data pipelines—it gives you full traceability, helps identify bottlenecks, and supports better pipeline orchestration visibility. It's a game-changer for data governance and optimization.
Can historical data access really boost data consumer confidence?
Absolutely! When data consumers can see historical performance through data observability dashboards, it builds transparency and trust. They’re more likely to rely on your data if they know it’s been consistently accurate and well-maintained over time.
When should companies start implementing data quality monitoring tools?
Ideally, data quality monitoring should begin as early as possible in your data journey. As Dan Power shared during Entropy, fixing issues at the source is far more efficient than tracking down errors later. Early adoption of observability tools helps you proactively catch problems, reduce manual fixes, and improve overall data reliability from day one.
What role does data lineage tracking play in observability?
Data lineage tracking is a key part of any robust data observability framework. It helps you understand where your data comes from, how it’s transformed, and where it flows. This visibility is essential for debugging issues, ensuring compliance, and building trust in your data pipelines. It's especially useful when paired with real-time data pipeline monitoring tools.
How does SQL Table Tracer support different SQL dialects for data lineage tracking?
SQL Table Tracer uses Antlr4 and a unified grammar with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This ensures accurate data lineage tracking across diverse systems without needing separate parsers for each dialect.
Why is combining data catalogs with data observability tools the future of data management?
Combining data catalogs with data observability tools creates a holistic approach to managing data assets. While catalogs help users discover and understand data, observability tools ensure that data is accurate, timely, and reliable. This integration supports better decision-making, improves data reliability, and strengthens overall data governance.
Want to try Sifflet on your Databricks Stack?
Get in touch now!