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kafka-streams

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This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.

  • Updated Apr 23, 2020
  • Java
krriteshgupta
krriteshgupta commented Jun 6, 2019

This will be handy in case of disaster management. Few examples where this would be a helpful feature.
Suppose there are multiple stream routes in an actor .
1.) One of the routes in this actor starts messing up the data and we want to stop that immediately.

2.) One of the routes in the actor which is not business critical ends up doing some heavy data base operation because of which the pr

Real Time Big Data / IoT Machine Learning (Model Training and Inference) with HiveMQ (MQTT), TensorFlow IO and Apache Kafka - no additional data store like S3, HDFS or Spark required

  • Updated Apr 6, 2020
  • Jupyter Notebook
azkarra-streams
fhussonnois
fhussonnois commented May 12, 2020

Currenlty a StreamsExecutionEnvironment instance can be registed directly through the AzkarraContext#addExecutionEnvironment method and from the configuration.

The following example shows how to define an environment using configuration :

azkarra {
  // Create a environment for running the WordCountTopology
  environments = [
    {
      name: "dev"
      config = {}
      jo

Scalable stream processing platform for advanced realtime analytics on top of Kafka and Spark. LogIsland also supports MQTT and Kafka Streams (Flink being in the roadmap). The platform does complex event processing and is suitable for time series analysis. A large set of valuable ready to use processors, data sources and sinks are available.

  • Updated Sep 1, 2020
  • Java

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