Big Data : Concepts, Technology, and Architecture.

Cover -- Title Page -- Copyright Page -- Contents -- Acknowledgments -- About the Author -- Chapter 1 Introduction to the World of Big Data -- 1.1 Understanding Big Data -- 1.2 Evolution of Big Data -- 1.3 Failure of Traditional Database in Handling Big Data -- 1.3.1 Data Mining vs. Big Data -- 1.4 3 Vs of Big Data -- 1.4.1 Volume -- 1.4.2 Velocity -- 1.4.3 Variety -- 1.5 Sources of Big Data -- 1.6 Different Types of Data -- 1.6.1 Structured Data -- 1.6.2 Unstructured Data -- 1.6.3 Semi-Structured Data -- 1.7 Big Data Infrastructure -- 1.8 Big Data Life Cycle -- 1.8.1 Big Data Generation -- 1.8.2 Data Aggregation -- 1.8.3 Data Preprocessing -- 1.8.4 Big Data Analytics -- 1.8.5 Visualizing Big Data -- 1.9 Big Data Technology -- 1.9.1 Challenges Faced by Big Data Technology -- 1.9.2 Heterogeneity and Incompleteness -- 1.9.3 Volume and Velocity of the Data -- 1.9.4 Data Storage -- 1.9.5 Data Privacy -- 1.10 Big Data Applications -- 1.11 Big Data Use Cases -- 1.11.1 Health Care -- 1.11.2 Telecom -- 1.11.3 Financial Services -- Chapter 1 Refresher -- Conceptual Short Questions with Answers -- Frequently Asked Interview Questions -- Chapter 2 Big Data Storage Concepts -- 2.1 Cluster Computing -- 2.1.1 Types of Cluster -- 2.1.2 Cluster Structure -- 2.2 Distribution Models -- 2.2.1 Sharding -- 2.2.2 Data Replication -- 2.2.3 Sharding and Replication -- 2.3 Distributed File System -- 2.4 Relational and Non-Relational Databases -- 2.4.1 RDBMS Databases -- 2.4.2 NoSQL Databases -- 2.4.3 NewSQL Databases -- 2.5 Scaling Up and Scaling Out Storage -- Chapter 2 Refresher -- Conceptual Short Questions with Answers -- Chapter 3 NoSQL Database -- 3.1 Introduction to NoSQL -- 3.2 Why NoSQL -- 3.3 CAP Theorem -- 3.4 ACID -- 3.5 BASE -- 3.6 Schemaless Databases -- 3.7 NoSQL (Not Only SQL) -- 3.7.1 NoSQL vs. RDBMS -- 3.7.2 Features of NoSQL Databases.

3.7.3 Types of NoSQL Technologies -- 3.7.4 NoSQL Operations -- 3.8 Migrating from RDBMS to NoSQL -- Chapter 3 Refresher -- Conceptual Short Questions with Answers -- Chapter 4 Processing, Management Concepts, and Cloud Computing -- 4.1 Data Processing -- 4.2 Shared Everything Architecture -- 4.2.1 Symmetric Multiprocessing Architecture -- 4.2.2 Distributed Shared Memory -- 4.3 Shared-Nothing Architecture -- 4.4 Batch Processing -- 4.5 Real-Time Data Processing -- 4.6 Parallel Computing -- 4.7 Distributed Computing -- 4.8 Big Data Virtualization -- 4.8.1 Attributes of Virtualization -- 4.8.2 Big Data Server Virtualization -- Part II: Managing and Processing Big Data in Cloud Computing -- 4.9 Introduction -- 4.10 Cloud Computing Types -- 4.11 Cloud Services -- 4.12 Cloud Storage -- 4.12.1 Architecture of GFS -- 4.13 Cloud Architecture -- 4.13.1 Cloud Challenges -- Chapter 4 Refresher -- Conceptual Short Questions with Answers -- Cloud Computing Interview Questions -- Chapter 5 Driving Big Data with Hadoop Tools and Technologies -- 5.1 Apache Hadoop -- 5.1.1 Architecture of Apache Hadoop -- 5.1.2 Hadoop Ecosystem Components Overview -- 5.2 Hadoop Storage -- 5.2.1 HDFS (Hadoop Distributed File System) -- 5.2.2 Why HDFS? -- 5.2.3 HDFS Architecture -- 5.2.4 HDFS Read/Write Operation -- 5.2.5 Rack Awareness -- 5.2.6 Features of HDFS -- 5.3 Hadoop Computation -- 5.3.1 MapReduce -- 5.3.2 MapReduce Input Formats -- 5.3.3 MapReduce Example -- 5.3.4 MapReduce Processing -- 5.3.5 MapReduce Algorithm -- 5.3.6 Limitations of MapReduce -- 5.4 Hadoop 2.0 -- 5.4.1 Hadoop 1.0 Limitations -- 5.4.2 Features of Hadoop 2.0 -- 5.4.3 Yet Another Resource Negotiator (YARN) -- 5.4.4 Core Components of YARN -- 5.4.5 YARN Scheduler -- 5.4.6 Failures in YARN -- 5.5 HBASE -- 5.5.1 Features of HBase -- 5.6 Apache Cassandra -- 5.7 SQOOP -- 5.8 Flume -- 5.8.1 Flume Architecture.

5.9 Apache Avro -- 5.10 Apache Pig -- 5.11 Apache Mahout -- 5.12 Apache Oozie -- 5.12.1 Oozie Workflow -- 5.12.2 Oozie Coordinators -- 5.12.3 Oozie Bundles -- 5.13 Apache Hive -- 5.14 Hive Architecture -- 5.15 Hadoop Distributions -- Chapter 5 Refresher -- Conceptual Short Questions with Answers -- Frequently Asked Interview Questions -- Chapter 6 Big Data Analytics -- 6.1 Terminology of Big Data Analytics -- 6.1.1 Data Warehouse -- 6.1.2 Business Intelligence -- 6.1.3 Analytics -- 6.2 Big Data Analytics -- 6.2.1 Descriptive Analytics -- 6.2.2 Diagnostic Analytics -- 6.2.3 Predictive Analytics -- 6.2.4 Prescriptive Analytics -- 6.3 Data Analytics Life Cycle -- 6.3.1 Business Case Evaluation and Identification of the Source Data -- 6.3.2 Data Preparation -- 6.3.3 Data Extraction and Transformation -- 6.3.4 Data Analysis and Visualization -- 6.3.5 Analytics Application -- 6.4 Big Data Analytics Techniques -- 6.4.1 Quantitative Analysis -- 6.4.2 Qualitative Analysis -- 6.4.3 Statistical Analysis -- 6.5 Semantic Analysis -- 6.5.1 Natural Language Processing -- 6.5.2 Text Analytics -- 6.5.3 Sentiment Analysis -- 6.6 Visual analysis -- 6.7 Big Data Business Intelligence -- 6.7.1 Online Transaction Processing (OLTP) -- 6.7.2 Online Analytical Processing (OLAP) -- 6.7.3 Real-Time Analytics Platform (RTAP) -- 6.8 Big Data Real-Time Analytics Processing -- 6.9 Enterprise Data Warehouse -- Chapter 6 Refresher -- Conceptual Short Questions with Answers -- Chapter 7 Big Data Analytics with Machine Learning -- 7.1 Introduction to Machine Learning -- 7.2 Machine Learning Use Cases -- 7.3 Types of Machine Learning -- 7.3.1 Supervised Machine Learning Algorithm -- 7.3.2 Support Vector Machines (SVM) -- 7.3.3 Unsupervised Machine Learning -- 7.3.4 Clustering -- Chapter 7 Refresher -- Conceptual Short Questions with Answers.

Chapter 8 Mining Data Streams and Frequent Itemset -- 8.1 Itemset Mining -- 8.2 Association Rules -- 8.3 Frequent Itemset Generation -- 8.4 Itemset Mining Algorithms -- 8.4.1 Apriori Algorithm -- 8.4.2 The Eclat Algorithm-Equivalence Class Transformation Algorithm -- 8.4.3 The FP Growth Algorithm -- 8.5 Maximal and Closed Frequent Itemset -- 8.6 Mining Maximal Frequent Itemsets: the GenMax Algorithm -- 8.7 Mining Closed Frequent Itemsets: the Charm Algorithm -- 8.8 CHARM Algorithm Implementation -- 8.9 Data Mining Methods -- 8.10 Prediction -- 8.10.1 Classification Techniques -- 8.11 Important Terms Used in Bayesian Network -- 8.11.1 Random Variable -- 8.11.2 Probability Distribution -- 8.11.3 Joint Probability Distribution -- 8.11.4 Conditional Probability -- 8.11.5 Independence -- 8.11.6 Bayes Rule -- 8.12 Density Based Clustering Algorithm -- 8.13 DBSCAN -- 8.14 Kernel Density Estimation -- 8.14.1 Artificial Neural Network -- 8.14.2 The Biological Neural Network -- 8.15 Mining Data Streams -- 8.16 Time Series Forecasting -- Chapter 9 Cluster Analysis -- 9.1 Clustering -- 9.2 Distance Measurement Techniques -- 9.3 Hierarchical Clustering -- 9.3.1 Application of Hierarchical Methods -- 9.4 Analysis of Protein Patterns in the Human Cancer-Associated Liver -- 9.5 Recognition Using Biometrics of Hands -- 9.5.1 Partitional Clustering -- 9.5.2 K-Means Algorithm -- 9.5.3 Kernel K-Means Clustering -- 9.6 Expectation Maximization Clustering Algorithm -- 9.7 Representative-Based Clustering -- 9.8 Methods of Determining the Number of Clusters -- 9.8.1 Outlier Detection -- 9.8.2 Types of Outliers -- 9.8.3 Outlier Detection Techniques -- 9.8.4 Training Dataset-Based Outlier Detection -- 9.8.5 Assumption-Based Outlier Detection -- 9.8.6 Applications of Outlier Detection -- 9.9 Optimization Algorithm -- 9.10 Choosing the Number of Clusters.

9.11 Bayesian Analysis of Mixtures -- 9.12 Fuzzy Clustering -- 9.13 Fuzzy C-Means Clustering -- Chapter 10 Big Data Visualization -- 10.1 Big Data Visualization -- 10.2 Conventional Data Visualization Techniques -- 10.2.1 Line Chart -- 10.2.2 Bar Chart -- 10.2.3 Pie Chart -- 10.2.4 Scatterplot -- 10.2.5 Bubble Plot -- 10.3 Tableau -- 10.3.1 Connecting to Data -- 10.3.2 Connecting to Data in the Cloud -- 10.3.3 Connect to a File -- 10.3.4 Scatterplot in Tableau -- 10.3.5 Histogram Using Tableau -- 10.4 Bar Chart in Tableau -- 10.5 Line Chart -- 10.6 Pie Chart -- 10.7 Bubble Chart -- 10.8 Box Plot -- 10.9 Tableau Use Cases -- 10.9.1 Airlines -- 10.9.2 Office Supplies -- 10.9.3 Sports -- 10.9.4 Science - Earthquake Analysis -- 10.10 Installing R and Getting Ready -- 10.10.1 R Basic Commands -- 10.10.2 Assigning Value to a Variable -- 10.11 Data Structures in R -- 10.11.1 Vector -- 10.11.2 Coercion -- 10.11.3 Length, Mean, and Median -- 10.11.4 Matrix -- 10.11.5 Arrays -- 10.11.6 Naming the Arrays -- 10.11.7 Data Frames -- 10.11.8 Lists -- 10.12 Importing Data from a File -- 10.13 Importing Data from a Delimited Text File -- 10.14 Control Structures in R -- 10.14.1 If-else -- 10.14.2 Nested if-Else -- 10.14.3 For Loops -- 10.14.4 While Loops -- 10.14.5 Break -- 10.15 Basic Graphs in R -- 10.15.1 Pie Charts -- 10.15.2 3D - Pie Charts -- 10.15.3 Bar Charts -- 10.15.4 Boxplots -- 10.15.5 Histograms -- 10.15.6 Line Charts -- 10.15.7 Scatterplots -- Index -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.