Analyze company data quickly and easily using Microsoft’s powerful data tools. Learn to build scalable and robust data models, clean and combine different data sources effectively, and create compelling and professional visuals.
Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits.
This book introduces two basic big data processing paradigms for batch data and streaming data. Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies.
The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing.
Build a custom BimlExpress framework that generates dozens of SQL Server Integration Services (SSIS) packages in minutes. Use this framework to execute related SSIS packages in a single command. You will learn to configure SSIS catalog projects, manage catalog deployments, and monitor SSIS catalog execution and history.
Hadoop offers distributed processing of large datasets across clusters and is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. It enables computing solutions that are scalable, cost-effective, flexible, and fault tolerant to back up very large data sets from hardware failures.
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