Parallel Data Flow in Workflows

1. Workflow Structure:
   • Workflows in Axonator are composed of a series of connected steps.
   • These steps are linked by data flow lines, typically allowing for sequential processing.
2. Parallel Execution Capability:
   • To enhance productivity, Axonator allows for parallel execution of workflow steps.
   • This means multiple steps can be processed simultaneously rather than strictly sequentially.
3. Workflow Design:
   • The parallel workflow design is implemented using the Automata drag-and-drop tool.
   • Users can customize workflows to include parallel processing paths.
4. Types of Steps:
   • Workflows can include both system steps and user steps.
   • System steps are automated and execute without user intervention.
   • User steps require interaction and trigger push notifications.
5. Examples of Parallel Steps:
   • Sending emails
   • Filling forms
   • Sending data to third-party APIs
   • Pushing or pulling data from external systems via REST API calls
6. Benefits of Parallel Processing:
   • Increased efficiency in data processing
   • Reduced overall workflow execution time
   • Ability to handle complex, multi-faceted tasks simultaneously
7. Integration with Data Sources:
   • Parallel steps can interact with various data sources concurrently.
   • This includes the Axonator cloud, local databases, and third-party systems.
8. Flexibility in Workflow Design:
   • Parallel processing allows for more complex and efficient workflow designs.
   • It enables handling of multiple independent or semi-dependent tasks at once.

Parallel data flow in workflows significantly enhances the capabilities of the Axonator system. It allows for more sophisticated data processing scenarios, enabling users to design workflows that can handle multiple tasks or data streams simultaneously. This feature is particularly useful for complex business processes that require interaction with multiple systems or perform various actions concurrently, thereby improving overall efficiency and reducing processing time.