Real-time data processing.

Cassandra is purpose-built for high-frequency writes + time-series data at scale. It handles massive write volumes without performance degradation, scales horizontally, replicates automatically across nodes + is optimised for the query patterns required by real-time operational data. SQL databases are used alongside Cassandra for structured relational records - the two work together in Rayven's hybrid architecture. 

Immediately. Workflow execution is event-triggered - the moment data arrives from an integration, the connected workflow fires. There is no batching delay or polling interval on the processing side. End-to-end, from data arrival to storage + action completion, runs in milliseconds.

Each entity in Rayven has a unique identifier (UID) - an asset, a device, a customer, a transaction. Iterative workflows execute independently for each UID. A workflow configured to run per-UID across 5,000 assets fires 5,000 independent instances simultaneously, each evaluating its own data. No asset waits on another. 

Yes. Three processing modes are available: per-UID (every entity runs independently), per-Label (grouped by a category such as site or region), or once per trigger event (a single execution regardless of how many records exist). The appropriate mode is configured in the workflow trigger node. 

Cassandra writes all incoming data in parallel across distributed nodes. Its architecture is designed for exactly this scenario - thousands of concurrent writes from different sources without queue contention or write conflicts. Automatic partitioning distributes related data across nodes to optimise both write performance + read speed. 

Yes. Streaming event-driven processing + scheduled batch processing can run within the same workflow or as separate workflows operating on the same data. A single pipeline can combine real-time event triggers with scheduled aggregation jobs - for example, processing each sensor reading as it arrives while also generating hourly summary statistics. 

Dashboards auto-refresh every 30 seconds by default. Data written to Cassandra is immediately queryable, so the next refresh cycle after a workflow execution will display the updated value. For use cases requiring sub-30-second display, contact us about configurable refresh intervals. 

Yes. Workflow nodes can combine data from multiple sources within the same processing chain - for example, joining a sensor reading with a record from a Primary Table, enriching it with an API response, then evaluating the combined result against a rule. All within a single workflow execution.

Workflow execution failures are logged with full payload context. Cassandra's replication across multiple nodes means ingested data is not lost if a processing node fails. Alerting can be configured to notify on workflow execution errors, and the Inspect Data tab gives full visibility of payload state at any step in the workflow. 

Yes. Cassandra-stored time-series data is directly available as a training dataset for Rayven's Python ML modeller. Real-time workflow execution can pass processed data to an AI/LLM node or ML model in the same chain - enabling real-time inference, anomaly detection + predictive scoring at the point of processing.