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16.8 MB
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1E73504B7621C442CF89139737C7CF2E4AA17B8E
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June 25, 2025, 1:44 p.m.
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(Last updated: June 25, 2025, 1:45 p.m.)
| File | Size |
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| ['Jugulum R. Big Data Management and Analytics. Concepts, Tools, and Apps 2025.pdf'] | 0 bytes |
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16.8 MB
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2025-06-25
| Uploaded by andryold1 | Size 16.8 MB | Health [ 32 /30 ] | Added 2025-06-25 |
NOTE
SOURCE: Jugulum R. Big Data Management and Analytics. Concepts, Tools, and Apps 2025
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COVER
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MEDIAINFO
Textbook in PDF format As more companies go digital and conduct their business online, this book provides practical examples of how they can better manage their data and use it to generate maximum value. It offers an integrated approach by treating data as an asset and discusses how to preserve and protect it just like any other corporate asset. Big Data Management and Analytics: Concepts, Tools, and Applications illustrates effective strategies for managing, governing, and analyzing Big Data to gain a competitive edge for companies utilizing Big Data and analytics. It offers a comprehensive guide on methods, tools, and concepts to efficiently manage and analyze Big Data in order to make informed decisions. Additionally, this book explores the significance of Artificial Intelligence (AI) and Machine Learning in leveraging Big Data and how they can be optimized in a well-structured environment. This book also emphasizes treating Big Data as a valuable asset and outlines strategies for preserving and safeguarding it like any other corporate asset. The inclusion of case studies ensures that the methodologies and concepts presented can be easily implemented in day-to-day operations. Hadoop was invented in 2002, when Big Data started to emerge, and Apache software created a new type of database to handle large unstructured data, which was called Hadoop. The name “Hadoop” comes from an Indian cartoon character, an elephant. The developer of Hadoop had a son who watched this cartoon, which is where the inspiration for the name came from. Unlike structured databases, Hadoop can handle and process large amounts of unstructured data, making it ideal for the data storage and processing requirements of the digital age. Many firms are now uploading data to the cloud but will keep their most sensitive data on premises. This is where Hadoop comes into play. With on premise infrastructure is not as powerful as cloud technology, Hadoop can process large amounts of unstructured data more efficiently and effectively. With such large amounts of data being processed, firms must take care to make it easier for analysts to understand which data is available. There are specific databases for unstructured data. One particular is the NoSQL database. As the name suggests, NSQL database do not depend on structured data and do not use SQL to enable access. If one desired a similar setup to a structured database, tools like Grok can be used. Grok is a filter tool within Logstash that is used to parse unstructured data into something structured and query able. It is used extensively for working with log files. Given the current significance of Big Data in the business world, this book equips readers with the necessary skills to effectively manage this valuable asset. It is tailored for practitioners, students, and professionals working in data mining, Big Data, and Machine Learning across various industries, including manufacturing. Preface The Management of BIG Data Overview Big Data, Hadoop Distro, Data Triad, and Enterprise Data Lakes The Data Supply Chain Data Quality – Measurement and Its Impact Analytics Landscape, Execution, and Evaluation Big Data and Cloud Solutions Structuring Unstructured Data and NoSQL Design and Analysis of Multivariate Diagnostic Systems Big Data and Artificial Intelligence (AI) Aligning Big Data and AI Strategy with Business Goals Facets of Responsible AI Appendix I Appendix II: Orthogonal Arrays (OAs)
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