This session discusses the technical concepts of stream processing / streaming analytics and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The focus of the session lies on comparing
- different open source frameworks such as Apache Apex, Apache Flink or Apache Spark Streaming
- engines from software vendors such as IBM InfoSphere Streams, TIBCO StreamBase
- cloud offerings such as AWS Kinesis.
- real time streaming UIs such as Striim, Zoomdata or TIBCO Live Datamart.
Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Apache Hadoop frameworks (such as MapReduce, Hive, Pig or Impala) and machine learning (such as R, Spark ML or H2O.ai).