Big data : concepts, technology and architecture /

"This book offers comprehensive coverage of Big Data tools, terminologies and technologies for researchers, business professionals and graduates. This book begins with an overview of what Big Data is and emphasizes all the key concepts of big data end to end. Big Data concepts, technologies, te...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Balusamy, Balamurugan (Autor), R, Nandhini Abirami (Autor), Kadry, Seifedine, 1977- (Autor), Gandomi, Amir Hossein (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley and Sons, Inc., 2021.
Edición:First edition.
Temas:
Big data.
Data mining.
Data Mining
Données volumineuses.
Exploration de données (Informatique)
Big data
Data mining
Acceso en línea:Texto completo

MARC

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050 0 4 |a QA76.9.B45  |b B35 2021 
082 0 0 |a 005.7  |2 23 
049 |a UAMI 
100 1 |a Balusamy, Balamurugan,  |e author. 
245 1 0 |a Big data :  |b concepts, technology and architecture /  |c Balamurugan Balusamy, Nandhini Abirami. R, Seifedine Kadry, and Amir H. Gandomi. 
250 |a First edition. 
264 1 |a Hoboken, NJ :  |b John Wiley and Sons, Inc.,  |c 2021. 
264 4 |c ©2021 
300 |a 1 online resource (xii, 356 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a Includes index. 
505 0 |a <P>Big Data -- concepts, Technology and Architecture. 1</p> <p>Book Description.. 11</p> <p>1.1 Understanding Big Data. 13</p> <p>1.2 Evolution of Big Data. 14</p> <p>1.3 Failure of Traditional database in handling Big Data. 15</p> <p>1.3 (a) Data Mining Vs Big Data. 16</p> <p>1.4 3 V's of Big Data. 17</p> <p>1.4.1 Volume. 17</p> <p>1.4.2 Velocity. 18</p> <p>1.4.3 Variety. 19</p> <p>1.5 Sources of Big Data. 19</p> <p>1.6 Different Types of Data. 21</p> <p>1.6.1 Structured Data. 22</p> <p>1.6.2 Unstructured Data. 22</p> <p>1.6.3 Semi-Structured Data. 23</p> <p>1.7 Big Data Infrastructure. 24</p> <p>1.8 Big Data Life Cycle. 25</p> <p>1.8.1 Big Data Generation. 26</p> <p>1.8.2 Data Aggregation. 26</p> <p>1.8.3 Data Preprocessing. 27</p> <p>1.7.<i>3</i>Big Data Analytics. 31</p> <p>1.7.4 Visualizing Big Data. 32</p> <p>1.8 Big Data Technology. 32</p> <p>1.8.1 Challenges faced by Big Data technology. 34</p> <p>1.8.1 Heterogeneity and incompleteness. 34</p> <p>1.8.2 Volume and velocity of the Data. 35</p> <p>1.8.3 Data Storage. 35</p> <p>1.8.4 Data Privacy. 36</p> <p>1.9 Big Data Applications. 36</p> <p>1.10 Big Data Use Cases. 37</p> <p>1.9. 1 Healthcare. 37</p> <p>1.9.2 Telecom.. 38</p> <p>1.9.3 Financial Services. 39</p> <p>Chapter 1 refresher: 40</p> <p>Conceptual short Questions with answers. 43</p> <p>Frequently asked Interview questions. 45</p> <p>Chapter Objective. 46</p> <p>Big Data Storage Concepts. 46</p> <p>2.1 Cluster computing. 47</p> <p>2.1.1 Types of cluster. 49</p> <p>2.1.1.1 High availability cluster. 50</p> <p>2.1.1.2 Load balancing cluster. 50</p> <p>2.1.2 Cluster structure. 51</p> <p>2.3 Distribution Models. 53</p> <p>2.3.1 Sharding. 54</p> <p>2.3.2 Data Replication. 56</p> <p>2.3.2.1 Master-Slave model 57</p> <p>2.3.2.2 Peer-to-Peer model 58</p> <p>2.3.3 Sharding and Replication. 59</p> <p>2.4 Distributed file system.. 60</p> <p>2.5 Relational and Non Relational Databases. 61</p> <p>CoursesOffered. 62</p> <p>Figure 2.12 Data divided across multiple related tables. 62</p> <p>2.4.2 RDBMS Databases. 63</p> <p>2.4.3 NoSQL Databases. 63</p> <p>2.4.4 NewSQL Databases. 64</p> <p>2.5 Scaling Up and Scaling Out Storage. 65</p> <p>Chapter 2 refresher. 67</p> <p>Conceptual short questions with answers. 69</p> <p>Chapter Objective. 72</p> <p>3.1 Introduction to NoSQL. 72</p> <p>3.2 Why NoSQL. 72</p> <p>3.3 CAP theorem.. 73</p> <p>3.4 ACID.. 75</p> <p>3.5 BASE. 76</p> <p>3.6 Schemaless Database. 77</p> <p>3.7 NoSQL (Not Only SQL) 77</p> <p>3.7.1 NoSQL Vs RDBMS. 78</p> <p>3.7.2Features of NoSQL database. 79</p> <p>3.7.3Types of NoSQL Technologies. 80</p> <p>3.7.3.1 Key-Value store database. 81</p> <p>3.7.3.2 Column-store database. 82</p> <p>3.7.3.3 Document Oriented Database. 84</p> <p>3.7.3.4 Graph-oriented Database. 86</p> <p>3.7.4 NoSQL Operations. 93</p> <p>3.9 Migrating from RDBMS to NoSQL. 98</p> <p>Chapter 3 refresher. 99</p> <p>Conceptual short questions with answers. 102</p> <p>Chapter Objective. 104</p> <p>4.1 Data Processing. 104</p> <p>4.2 Shared Everything Architecture. 106</p> <p>4.2.1 Symmetric multiprocessing architecture. 107</p> <p>4.2.2 Distributed Shared memory. 108</p> <p>4.3 Shared nothing architecture. 109</p> <p>4.4 Batch Processing. 110</p> <p>4.5 Real-Time Data Processing. 111</p> <p>4.6 Parallel Computing. 112</p> <p>4.7 Distributed Computing. 113</p> <p>4.8 Big Data Virtualization. 113</p> <p>4.8.1 Attributes of Virtualization. 114</p> <p>4.8.1.1 Encapsulation. 115</p> <p>4.8.1.2 Partitioning. 115</p> <p>4.8.1.3 Isolation. 115</p> <p>4.8.2Big Data Server Virtualization. 116</p> <p>4.9 Introduction. 116</p> <p>4.10 Cloud computing types. 118</p> <p>4.11Cloud Services. 120</p> <p>4.12 Cloud Storage. 121</p> <p>4.12.1 Architecture of GFS. 121</p> <p>4.12.1.1 Master. 123</p> <p>4.12.1.2 Client. 123</p> <p>4.13 Cloud Architecture. 127</p> <p>Cloud Challenges. 129</p> <p>Chapter 4 Refresher. 130</p> <p>Conceptual short questions with answers. 133</p> <p>Chapter Objective. 139</p> <p>5.1 Apache Hadoop. 139</p> <p>5.1.1 Architecture of Apache Hadoop. 140</p> <p>5.1.2Hadoop Ecosystem Components Overview.. 140</p> <p>5.2 Hadoop Storage. 142</p> <p>5.2.1HDFS (Hadoop Distributed File System). 142</p> <p>5.2.2Why HDFS?. 143</p> <p>5.2.3HDFS Architecture. 143</p> <p>5.2.4HDFS Read/Write Operation. 146</p> <p>5.2.5Rack Awareness. 148</p> <p>5.2.6Features of HDFS. 149</p> <p>5.2.6.1Cost-effective. 149</p> <p>5.2.6.2Distributed storage. 149</p> <p>5.2.6.3Data Replication. 149</p> <p>5.3 Hadoop Computation. 149</p> <p>5.3.1MapReduce. 149</p> <p>5.3.1.1Mapper. 151</p> <p>5.3.1.2Combiner. 151</p> <p>5.3.1.3 Reducer. 152</p> <p>5.3.1.4 JobTracker and TaskTracker. 153</p> <p>5.3.2 MapReduce Input Formats. 154</p> <p>5.3.3 MapReduce Example. 156</p> <p>5.3.4 MapReduce Processing. 157</p> <p>5.3.5 MapReduce Algorithm.. 160</p> <p>5.3.6 Limitations of MapReduce. 161</p> <p>5.4Hadoop 2.0. 161</p> <p>5.4.1Hadoop 1.0 limitations. 162</p> <p>5.4.2 Features of Hadoop 2.0. 163</p> <p>5.4.3 Yet Another Resource Negotiator (YARN). 164</p> <p>5.4.3 Core components of YARN.. 165</p> <p>5.4.3.1 ResourceManager. 165</p> <p>5.4.3.2 NodeManager. 166</p> <p>5.4.4 YARN Scheduler. 169</p> <p>5.4.4.1 <i>FIFO scheduler</i>. 169</p> <p>5.4.4.2 <i>Capacity Scheduler</i>. 170</p> <p>5.4.4.3 <i>Fair Scheduler</i>. 170</p> <p>5.4.5 Failures in YARN.. 171</p> <p>5.4.5.1ResourceManager failure. 171</p> <p>5.4.5.2 ApplicationMaster failure. 172</p> <p>5.4.5.3 NodeManagerFailure. 172</p> <p>5.4.5.4 Container Failure. 172</p> <p>5.3 HBASE. 173</p> <p>5.4 Apache Cassandra. 176</p> <p>5.5 SQOOP. 177</p> <p>5.6 Flume. 179</p> <p>5.6.1 Flume Architecture. 179</p> <p>5.6.1.1 Event. 180</p> <p>5.6.1.2 Agent. 180</p> <p>5.7 Apache Avro. 181</p> <p>5.8 Apache Pig. 182</p> <p>5.9 Apache Mahout. 183</p> <p>5.10 Apache Oozie. 183</p> <p>5.10.1 Oozie Workflow.. 184</p> <p>5.10.2 Oozie Coordinators. 186</p> <p>5.10.3 Oozie Bundles. 187</p> <p>5.11 Apache Hive. 187</p> <p>5.11 Apache Hive. 187</p> <p>Hive Architecture. 189</p> <p>Hadoop Distributions. 190</p> <p>Chapter 5refresher. 191</p> <p>Conceptual short questions with answers. 194</p> <p>Frequently asked Interview Questions. 199</p> <p>Chapter Objective. 200</p> <p>6.1 Terminologies of Big Data Analytics. 201</p> <p><i>Data Warehouse</i>. 201</p> <p><i>Business Intelligence</i>. 201</p> <p><i>Analytics</i>. 202</p> <p>6.2 Big Data Analytics. 202</p> <p>6.2.1 Descriptive Analytics. 204</p> <p>6.2.2 Diagnostic Analytics. 205</p> <p>6.2.3 Predictive Analytics. 205</p> <p>6.2.4 Prescriptive Analytics. 205</p> <p>6.3 Data Analytics Lifecycle. 207</p> <p>6.3.1 Business case evaluation and Identify the source data. 208</p> <p>6.3.2 Data preparation. 209</p> <p>6.3.3 Data Extraction and Transformation. 210</p> <p>6.3.4 Data Analysis and visualization. 211</p> <p>6.3.5 Analytics application. 212</p> <p>6.4 Big Data Analytics Techniques. 212</p> <p>6.4.1 Quantitative Analysis. 212</p> <p>6.4.3 Statistical analysis. 214</p> <p>6.4.3.1 A/B testing. 214</p> <p>6.4.3.2 Correlation. 215</p> <p>6.4.3.3 Regression. 218</p> <p>6.5 Semantic Analysis. 220</p> <p>6.5.1 Natural Language Processing. 220</p> <p>6.5.2 Text Analytics. 221</p> <p>6.7 Big Data Business Intelligence. 222</p> <p>6.7.1 Online Transaction Processing (OLTP). 223</p> <p>6.7.2 Online Analytical Processing (OLAP). 223</p> <p>6.7.3 Real-Time Analytics Platform (RTAP). 224</p> <p>6.6Big Data Real Time Analytics Processing. 225</p> <p>6.7 Enterprise Data Warehouse. 227</p> <p>Chapter 6 Refresher. 228</p> <p>Concept 
520 |a "This book offers comprehensive coverage of Big Data tools, terminologies and technologies for researchers, business professionals and graduates. This book begins with an overview of what Big Data is and emphasizes all the key concepts of big data end to end. Big Data concepts, technologies, terminologies and storing, processing and analysis techniques and much more -- are all logically organized and reinforced by diagrams and case studies. This book refines readers' understanding of Big Data with in-depth analysis of key concepts. The case studies provided in this book give insight on key concepts. The initial chapters of the book shed light on various characteristics of Big Data that distinguish it from traditional Database Management systems. Big Data Analytics are covered in detail in a separate chapter. Hadoop, the heart of Big Data is handled in the Big Data processing chapter and a deep understanding of its concepts is provided"--  |c Provided by publisher. 
588 |a Description based on online resource; title from digital title page (viewed on July 12, 2021). 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
590 |a Knovel  |b ACADEMIC - Software Engineering 
650 0 |a Big data. 
650 0 |a Data mining. 
650 2 |a Data Mining 
650 6 |a Données volumineuses. 
650 6 |a Exploration de données (Informatique) 
650 7 |a Big data  |2 fast 
650 7 |a Data mining  |2 fast 
700 1 |a R, Nandhini Abirami,  |e author. 
700 1 |a Kadry, Seifedine,  |d 1977-  |e author. 
700 1 |a Gandomi, Amir Hossein,  |e author. 
758 |i has work:  |a Big data (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCGvgp79BfbqrcwTKXDYRqP  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Balusamy, Balamurugan.  |t Big data  |b First edition.  |d Hoboken, NJ : John Wiley and Sons, Inc., 2021.  |z 9781119701828  |w (DLC) 2020024528 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6509398  |z Texto completo 
938 |a Askews and Holts Library Services  |b ASKH  |n AH37193918 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL6509398 
938 |a YBP Library Services  |b YANK  |n 301971591 
938 |a EBSCOhost  |b EBSC  |n 2766968 
994 |a 92  |b IZTAP 

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