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  • Interactive Flink based data exploration in Jupyter
  • Plaform libraries support for common operations
  • Savepoints 
    • Triggering, Listing & Resuming & Disposing savepoints from Deployment Manager for a given application.
  • Example applications
    • Illustrate how to build and deploy Flink applications with PNDA (in Java, Scala & Python)
    • Illustrate how to use Flink Accumulators, application metrics
    • Use stateful continuous processing (non-batch mode) making it a different case from Spark streaming examples
  • Metrics 
    • configure Graphite as a default metrics reporter in flink.