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Big Data
This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complexdecision processes, giving special attention to Big Data Warehouses as a way ofefficiently storing and processing batch or streaming data for structured orsemi-structured analytical pro...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Aalborg :
River Publishers,
2020.
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| Colección: | River Publishers series in information science and technology.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Santos, Maribel Yasmina. | |
| 245 | 1 | 0 | |a Big Data |
| 260 | |a Aalborg : |b River Publishers, |c 2020. | ||
| 300 | |a 1 online resource (316 pages) | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Front Cover -- Half Title -- Series Page -- RIVER PUBLISHERS SERIES IN INFORMATION SCIENCE AND TECHNOLOGY -- Title Page -- Copyright Page -- CONTENTS -- List of Figures -- List of Tables -- The Authors -- Acknowledgments -- Foreword -- Notation -- 1. Introduction -- 1.1. Objectives of this Book -- 1.2. Intended Audience -- 1.3. Book Structure -- 2. Big Data Concepts, Techniques, and Technologies -- 2.1. Big Data Relevance -- 2.2. Big Data Characteristics -- 2.3. Big Data Challenges -- 2.3.1. Big Data General Dilemmas -- 2.3.2. Challenges in the Big Data Life Cycle | |
| 505 | 8 | |a 2.3.3. Big Data in Secure, Private, and Monitored Environments -- 2.3.4. Organizational Change -- 2.4. Techniques for Big Data Solutions -- 2.4.1. Big Data Life Cycle and Requirements -- 2.4.1.1. General Steps to Process and Analyze Big Data -- 2.4.1.2. Architectural and Infrastructural Requirements -- 2.4.2. The Lambda Architecture -- 2.4.3. Towards Standardization: the NIST Reference Architecture -- 2.5. Big Data Technologies -- 2.5.1. Hadoop and Related Projects -- 2.5.2. Landscape of Distributed SQL Engines -- 2.5.3. Other Technologies for Big Data Analytics | |
| 505 | 8 | |a 3. OLTP-oriented Databases for Big Data Environments -- 3.1. NoSQL and NewSQL: an Overview -- 3.2. NoSQL Databases -- 3.2.1. Key-value Databases -- 3.2.1.1. Overview -- 3.2.1.2. Redis -- 3.2.2. Column -- oriented Databases -- 3.2.2.1. Overview -- 3.2.2.2. HBase -- 3.2.2.3. From Relational Models to HBase Data Models -- 3.2.3. Document -- oriented Databases -- 3.2.3.1. Overview -- 3.2.3.2. MongoDB -- 3.2.4. Graph Databases -- 3.2.4.1. Overview -- 3.2.4.2. Neo4j -- 3.3. NewSQL Databases and Translytical Databases -- 4. OLAP-oriented Databases for Big Data Environments | |
| 505 | 8 | |a 4.1. Hive: the De Facto SQL-on-Hadoop Engine -- 4.1.1. Data Storage Formats -- 4.1.1.1. Text File -- 4.1.1.2. Sequence File -- 4.1.1.3. RCFile -- 4.1.1.4. ORC File -- 4.1.1.5. Avro File -- 4.1.1.6. Parquet -- 4.1.2. Partitions and Buckets -- 4.2. From Dimensional Models to Tabular Models -- 4.2.1. Primary Data Tables -- 4.2.2. Derived Data Tables -- 4.3. Optimizing OLAP workloads with Druid -- 5. Design and Implementation of Big Data Warehouses -- 5.1. Big Data Warehousing: an Overview -- 5.2. Model of Logical Components and Data Flows -- 5.2.1. Data Provider and Data Consumer | |
| 505 | 8 | |a 5.2.2. Big Data Application Provider -- 5.2.3. Big Data Framework Provider -- 5.2.3.1. Messaging/Communications, Resource Management, and Infrastructures -- 5.2.3.2. Processing -- 5.2.3.3. Storage: Data Organization and Distribution -- 5.2.4. System Orchestrator and Security, Privacy, and Management -- 5.3. Model of Technological Infrastructure -- 5.4. Method for Data Modeling -- 5.4.1. Analytical Objects and their Related Concepts -- 5.4.2. Joining, Uniting, and Materializing Analytical Objects -- 5.4.3. Dimensional Big Data with Outsourced Descriptive Families | |
| 500 | |a 5.4.4. Data Modeling Best Practices | ||
| 520 | |a This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complexdecision processes, giving special attention to Big Data Warehouses as a way ofefficiently storing and processing batch or streaming data for structured orsemi-structured analytical problems. | ||
| 545 | 0 | |a Maribel Yasmina Santos, Carlos Costa | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Big data. | |
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| 776 | 0 | 8 | |i Print version: |a Santos, Maribel Yasmina. |t Big Data: Concepts, Warehousing, and Analytics. |d Aalborg : River Publishers, ©2020 |z 9788770221849 |
| 830 | 0 | |a River Publishers series in information science and technology. | |
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