See how stream processing / streaming analytics frameworks (e.g. Apache Spark, Apache Flink, Amazon Kinesis) and products (e.g. TIBCO StreamBase, Software AG’s Apama, IBM InfoSphere Streams) are categorized and compared. Besides, understand how stream processing is related to Big Data platforms such as Apache Hadoop and machine learning (e.g. R, SAS, MATLAB).
In 2015, the middleware world focuses on two buzzwords: Docker and Microservices. Software vendors still sell products such as an Enterprise Service Bus (ESB) or Complex Event Processing (CEP) engines. How is this related? This session discusses the requirements, best practices and challenges for creating a good Microservices architecture, and if this spells the end of the Enterprise Service Bus (ESB).
Challenges, requirements and best practices for creating a good Microservicess architecture, and what role an Enterprise Service Bus (ESB) plays in this game.
Internet of things, cloud and mobile are the major drivers for stream processing. Use cases are network monitoring, intelligent surveillance, but also less technical things such as inventory management or fraud detection. The book helps a lot to get a basic understanding about history, concepts and patterns of the stream processing paradigm.
Slides from my talk “Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about Real Time?”…