Big data simplified

By: Mukherjee, Sourabh Contributor(s): Das, Amit Kumar | Goswami, Sayan Material type: Text

TextPublication details: Noida Pearson India Education Services Pvt. Ltd. 2019 Description: xxiv, 326 pISBN: 9789353435110Subject(s): Big Data | Internet of ThingsDDC classification: 005.5 Summary: Big Data Simplified blends technology with strategy and delves into applications of big data in specialised 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 strategise 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, ApacheZookeeper, Flume, Kafka, Spark, Python and NoSQL databases like Cassandra, MongoDB, GraphDB, etc., is also included "

List(s) this item appears in: IT & Decision Sciences | Public Policy & General Management

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	005.5 MUK (Browse shelf(Opens below))	1	Available		002921

Table of Content
"Chapter 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 BigData 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"

Big Data Simplified blends technology with strategy and delves into applications of big data in specialised 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 strategise 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, ApacheZookeeper, Flume, Kafka, Spark, Python and NoSQL databases like Cassandra, MongoDB, GraphDB, etc., is also included "

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