Data Preparation: Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects.
Build intelligent Microservices by applying Machine Learning and Advanced Analytics. Leverage Apache Hadoop / Spark with Visual Analytics and Stream Processing.
Log Analytics is the right framework or tool to monitor for Distributed Microservices. Comparison of Open source, SaaS and Enteprrise Products. Plus relation to big data components such as Apache Hadoop / Spark.
Several tools are available on the market for Visual Analytics and Data Discovery. Three of the most well known options are Tableau, Qlik and TIBCO Spotfire. This post shows important characteristics to compare and evaluate these tools.
Closed Big Data Loop: 1) Finding Insights with R, H20, Apache Spark MLlib, PMML and TIBCO Spotfire. 2) Putting Analytic Models into Action via Event Processing and Streaming Analytics.
Slide deck from OOP 2016: Comparison of Frameworks and Products for Big Data Log Analytics and ITOA, e.g. Open Source ELK, TIBCO LogLogic / Unity, Splunk, Papertrail; Relation to Hadoop is also discussed.
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).
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.