- eBook:Big dаta: Concepts, Technology, and Architecture
- Author:Balamurugan Balusamy, Nandhini Abirami R, Seifedine Kadry, Amir H. Gandomi
- Edition:1 edition
- Data:April 13, 2021
- Pages:368 pages
Big dаta: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data.
You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work.
Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software.
Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include:
- The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns
- Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases
- Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization
- Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive
- The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization
2. Big Data Storage Concepts
3. NoSQL Database
4. Processing, Management Concepts, and Cloud Computing
5. Driving Big Data with Hadoop Tools and Technologies
6. Big Data Analytics
7. Big Data Analytics with Machine Learning
8. Mining Data Streams and Frequent Itemset
9. Cluster Analysis
10. Big Data Visualization
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