Big Data with Hadoop MapReduce

This book provides a clear understanding of big data and MapReduce through over 100 illustrations and examples, covering essential concepts, installation, and practical applications, preparing readers for certification exams and other tools.

Big Data with Hadoop MapReduce: A Classroom Approach is designed to help readers grasp the complexities of big data and the MapReduce framework through clear explanations and visual aids. The authors utilize over 100 illustrations and numerous worked-out examples to clarify fundamental terminologies and concepts, making the material accessible to learners at various levels. This approach aims to demystify the principles behind big data processing and the operational mechanics of MapReduce.

The book covers essential topics, including the installation of Hadoop on both physical and virtual machines, as well as the differences between single-node and multi-node setups. It provides comprehensive information that aligns with the requirements of many online certification exams, ensuring that readers are well-prepared. By the end of the book, readers will not only have a solid understanding of MapReduce but will also be equipped to explore other big data processing tools, such as Spark and Storm, enhancing their overall data processing skills.

Readers can expect to achieve several key outcomes, including a clear understanding of what constitutes big data and its influencing factors, insights into the functionality of MapReduce, and the ability to set up Hadoop clusters effectively. Additionally, the book guides readers in creating virtual machines on AWS and writing MapReduce programs using Eclipse, all while fostering an understanding of various big data tools and their applications in real-world scenarios.