Présentation de l'éditeur
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.
Quatrième de couverture
A COMPLETE SOLUTION FOR DYNAMIC ANALYSIS OF STREAMING DATA
Real–Time Analytics provides a complete end–to–end solution for cost–effective analysis and visualization of streaming data. Beginning with a description of the required analytics ecosystem, the book builds upon that foundation with practical guidance toward the tools and techniques that get targeted results. Outlining best practices for each specific application throughout the discovery life cycle, the approach provides easy–to–follow instructions for implementing the presented tools and techniques. Examples taken from real–world applications highlight the usage of various aspects of data processing from tabulation to visualization and forecasting. Readers will:
- Understand the components of streaming data systems, including their full capabilities and characteristics
- Learn the relevant architecture and best practices for analysis and storage of streaming data
- Develop a system for data aggregation, delivery, and warehousing using open source and commercial tools
- Learn the implementation and application of advanced algorithms and data structures to streaming applications
Decreasing data acquisition costs and increasing connectivity are enabling ever more efficient methods of continuous collection, so why do analysis platforms remain largely batch–based? The tools do exist to efficiently handle streaming data analysis and visualization feasibly in terms of time, maintenance and hardware. This book guides readers through the construction of a robust, cost–efficient system with clear, expert instruction.