Big Data Simplified blends technology with strategy and delves into applications of Big Data in specialized areas, such as recommendation engines, data science and Internet of Things (IoT) and enables a practitioner to make the right technology choice. The steps to strategize a big data implementation are also discussed in detail. This book presents a holistic approach to the topic, covering a wide landscape of Big Data technologies like Hadoop 2.0 and package implementations, such as Cloudera. In-depth discussion of associated technologies, such as MapReduce, Hive, Pig, Oozie, Apache Zookeeper, Flume, Kafka, Spark, Python and NoSQL databases like Cassandra, MongoDB, graphs, etc., is also included. Features: 1. Important concepts are backed by code snippets enabling step-by-step practical implementation. 2. Includes case study with complete code and detailing the concepts. 3. Numerous objective and subjective-type questions added for readers to evaluate their learning

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1) A closer look at data Chapter 2) Introducing Big Data Chapter 3) Introducing Hadoop Chapter 4) Introducing MapReduce Chapter 5) Introducing NoSQL Chapter 6) Introducing Spark and Kafka Chapter 7) Other Big Data tools and technologies Chapter 8) Working with Big Data in R Chapter 9) Working with Big Data in Python Chapter 10) Big Data Applied Chapter 11) Big Data Strategy Chapter 12) Case Study: Retail Near Real-Time-Analytics