Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Services

In November 2016, I am at Big Data Spain in Madrid for the first time. A great conference with many awesome speakers and sessions about very hot topics such as Apache Hadoop, Spark Spark, Streaming Processing / Streaming Analytics and Machine Learning. If you are interested in big data, then this conference is for you! My two talks:

• “How to Apply Machine Learning to Real Time Processing” (see slides and video recording from a similar conference talk).
• “Comparison of Streaming Analytics Options” (the reason for this blog post; an updated version of my talk from JavaOne 2015)

Here I wanna share the slides and a video recording of the latter one…

Abstract: Comparison of Stream Processing Options

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).

Slides – Alternatives for Streaming Analytics

The following slide deck is a more extensive version of the talk at Big Data Spain (as the conference talks were only 30 minutes):

Video Recording: Apache Storm, Flink, Apex, Spark, StreamBase, Striim, et al

The video recording walks you through the above slide deck:

As always, I appreciate any comments, questions or other feedback.