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  • Edge Deployment: Allow advanced deployment options, e.g. to assign pipeline elements (standalone) to individual nodes
  • StreamPipes Client: Define/createpipelinesfrom code, which are automatically deployed in StreamPipes
  • Fault Tolerance: Better support for failure handling/resiliency/ state management and state recovery
  • Pipeline Monitoring/Statistics: Inspect current pipeline execution state, receive statistics (e.g., processed messages) and see errors
  • Unified Data Visualization: Use common API for live + historic data visualization (e.g. only using time-series DB for historical data and polling to retrieve near realtime event)
  • Event/Configuration Preview: View current events in Pipeline Editor with application logic applied based on current configuration of static properties of pipeline elements up to this point
  • Pipeline adaptation: Manipulate pipeline configurations at runtime, e.g. static properties etc, either by an API or via trigger in visualizations (Visual Analytics), e.g. slider to set new active threshold value
  • Pipeline Triggers: A pipeline can either be started manually or via a trigger. The latter can be based on a user-defined scheduled time/day, e.g. no production during night shift or based on an API call.
  • ..Virtual Streams - replace data lake and dashboard sinks with a "virtual stream" that is also shown in the "data streams" section. Dashboard and data lake would then simply operate on the notion of streams instead of pipelines.