Читать книгу Big Data - Seifedine Kadry - Страница 2
Table of Contents
Оглавление1 Cover
7 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 1.4 3 Vs of Big Data 1.5 Sources of Big Data 1.6 Different Types of Data 1.7 Big Data Infrastructure 1.8 Big Data Life Cycle 1.9 Big Data Technology 1.10 Big Data Applications 1.11 Big Data Use Cases Chapter 1 Refresher Conceptual Short Questions with Answers Frequently Asked Interview Questions
8 2 Big Data Storage Concepts 2.1 Cluster Computing 2.2 Distribution Models 2.3 Distributed File System 2.4 Relational and Non‐Relational Databases 2.5 Scaling Up and Scaling Out Storage Conceptual Short Questions with Answers
9 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) 3.8 Migrating from RDBMS to NoSQL Chapter 3 Refresher Conceptual Short Questions with Answers
10 4 Processing, Management Concepts, and Cloud Computing 4.1 Data Processing 4.2 Shared Everything Architecture 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 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 4.13 Cloud Architecture Chapter 4 Refresher Conceptual Short Questions with Answers Cloud Computing Interview Questions
11 Chapter 5: Driving Big Data with Hadoop Tools and Technologies 5.1 Apache Hadoop 5.2 Hadoop Storage 5.3 Hadoop Computation 5.4 Hadoop 2.0 5.5 HBASE 5.6 Apache Cassandra 5.7 SQOOP 5.8 Flume 5.9 Apache Avro 5.10 Apache Pig 5.11 Apache Mahout 5.12 Apache Oozie 5.13 Apache Hive 5.14 Hive Architecture 5.15 Hadoop Distributions Chapter 5 Refresher Conceptual Short Questions with Answers Frequently Asked Interview Questions
12 6 Big Data Analytics 6.1 Terminology of Big Data Analytics 6.2 Big Data Analytics 6.3 Data Analytics Life Cycle 6.4 Big Data Analytics Techniques 6.5 Semantic Analysis 6.6 Visual analysis 6.7 Big Data Business Intelligence 6.8 Big Data Real‐Time Analytics Processing 6.9 Enterprise Data Warehouse Conceptual Short Questions with Answers
13 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 Chapter 7 Refresher Conceptual Short Questions with Answers
14 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 8.5 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 8.11 Important Terms Used in Bayesian Network 8.12 Density Based Clustering Algorithm 8.13 DBSCAN 8.14 Kernel Density Estimation 8.15 Mining Data Streams 8.16 Time Series Forecasting
15 9 Cluster Analysis 9.1 Clustering 9.2 Distance Measurement Techniques 9.3 Hierarchical Clustering 9.4 Analysis of Protein Patterns in the Human Cancer‐Associated Liver 9.5 Recognition Using Biometrics of Hands 9.6 Expectation Maximization Clustering Algorithm 9.7 Representative‐Based Clustering 9.8 Methods of Determining the Number of Clusters 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
16 10 Big Data Visualization 10.1 Big Data Visualization 10.2 Conventional Data Visualization Techniques 10.3 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 10.10 Installing R and Getting Ready 10.11 Data Structures in R 10.12 Importing Data from a File 10.13 Importing Data from a Delimited Text File 10.14 Control Structures in R 10.15 Basic Graphs in R
17 Index
