Stream Real-Time Data Directly into Databricks with the Solace Zerobus Connector
Zero Lag and Zero Friction for Zerobus
Organizations are investing heavily in AI, analytics, and lakehouse architectures—but those systems are only as valuable as the data feeding them.
That’s why we’re excited to introduce a new Solace-to-Databricks micro-integration powered by the Databricks Zerobus Ingest API.
What are Micro-Integrations?
Micro-Integrations (MIs) are lightweight, purpose-built integrations designed to connect systems, move data, and transform information in real time—without the complexity of traditional integration platforms. Rather than building large, monolithic integrations that are difficult to maintain and scale, MIs break integration into smaller, reusable components that are easier to build, deploy, and evolve.
About the Databricks Zerobus Integration
This new MI streams events directly from Solace queues into Databricks Unity Catalog Delta tables, making operational data available for analytics, AI, and machine learning the moment it is generated.
Built for enterprise-scale reliability, the integration supports OAuth 2.0 authentication, offset-based acknowledgements, batch processing, and dead message queue (DMQ) error handling to ensure durable, governed data ingestion. It also supports both static and dynamic table destinations, giving teams the flexibility to route event streams wherever they’re needed across the lakehouse.
A key capability of this integration is built-in data transformation
Transformations allow teams to reshape, enrich, and map event data between systems so information arrives in exactly the format downstream applications need. This helps eliminate the complexity of integrating systems with different schemas, payload structures, or data models—reducing manual coding while accelerating development.
Even complex transformations become easier with Solace’s visual mapping experience. Users can work with nested payloads in a hierarchical view while leveraging a simple table-based interface to define source-to-target mappings. This makes it significantly easier to understand complex data structures, map fields with confidence, and quickly build integrations without writing custom transformation logic.
The result: faster onboarding, reduced complexity, fewer mapping errors, and faster time to production.
Ultimately, this creates a simpler path to turning real-time business events into actionable insights—helping organizations bridge the gap between operational systems and AI-ready data in Databricks.