Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms.
The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes:
- A deep discussion of streaming data systems and architectures
- Instructions for analyzing, storing, and delivering streaming data
- Tips on aggregating data and working with sets
- Information on data warehousing options and techniques
Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.
Keywords: Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data; Byron Ellis; Justin Langseth; real-time data; real-time analysis; data mining; analyze data streams; visualize streaming data; streaming data systems; streaming data architecture; storing streaming data; data warehouse; data warehousing; aggregating data; data sets; data analytics platform; Big Data; Big Data streaming; website analytics; mobile data; operational data flows