Big Data: Concepts, Technology, and Architecture

(BIG-DATA.AE1)/ISBN:978-1-64459-299-1

This course includes
Lessons
TestPrep
Hands-On Labs
AI Tutor (Add-on)

Get hands-on experience in Big Data tools, terminology, and technology with the Big Data: Concepts, Technology, and Architecture course and lab. The course provides a vivid introduction to the Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students with clear and approachable lesson flowcharts, and other tools. It illustrates how to look after challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns. 

Lessons

10+ Lessons | 74+ Exercises | 106+ Quizzes | 142+ Flashcards | 142+ Glossary of terms

TestPrep

75+ Pre Assessment Questions | 75+ Post Assessment Questions |

Hands-On Labs

28+ LiveLab | 9+ Video tutorials | 16+ Minutes

Here's what you will learn

Download Course Outline

Lessons 1: Introduction to the World of Big Data

  • Understanding Big Data
  • Evolution of Big Data
  • Failure of Traditional Database in Handling Big Data
  • 3 Vs of Big Data
  • Sources of Big Data
  • Different Types of Data
  • Big Data Infrastructure
  • Big Data Life Cycle
  • Big Data Technology
  • Big Data Applications
  • Big Data Use Cases

Lessons 2: Big Data Storage Concepts

  • Cluster Computing
  • Distribution Models
  • Distributed File System
  • Relational and Non‐Relational Databases
  • Scaling Up and Scaling Out Storage

Lessons 3: NoSQL Database

  • Introduction to NoSQL
  • Why NoSQL
  • CAP Theorem
  • ACID
  • BASE
  • Schemaless Databases
  • NoSQL (Not Only SQL)
  • Migrating from RDBMS to NoSQL

Lessons 4: Big Data Processing, Management, and Cloud Computing

  • Part I: Big Data Processing and Management Conce...essing, Management Concepts, and Cloud Computing
  • Data Processing
  • Shared Everything Architecture
  • Shared‐Nothing Architecture
  • Batch Processing
  • Real‐Time Data Processing
  • Parallel Computing
  • Distributed Computing
  • Big Data Virtualization
  • Part II: Managing and Processing Big Data in Clo...essing, Management Concepts, and Cloud Computing
  • Introduction
  • Cloud Computing Types
  • Cloud Services
  • Cloud Storage
  • Cloud Architecture

Lessons 5: Driving Big Data with Hadoop Tools and Technologies

  • Apache Hadoop
  • Hadoop Storage
  • Hadoop Computation
  • Hadoop 2.0
  • HBASE
  • Apache Cassandra
  • SQOOP
  • Flume
  • Apache Avro
  • Apache Pig
  • Apache Mahout
  • Apache Oozie
  • Apache Hive
  • Hive Architecture
  • Hadoop Distributions

Lessons 6: Big Data Analytics

  • Terminology of Big Data Analytics
  • Big Data Analytics
  • Data Analytics Life Cycle
  • Big Data Analytics Techniques
  • Semantic Analysis
  • Visual analysis
  • Big Data Business Intelligence
  • Big Data Real‐Time Analytics Processing
  • Enterprise Data Warehouse

Lessons 7: Big Data Analytics with Machine Learning

  • Introduction to Machine Learning
  • Machine Learning Use Cases
  • Types of Machine Learning

Lessons 8: Mining Data Streams and Frequent Itemset

  • Itemset Mining
  • Association Rules
  • Frequent Itemset Generation
  • Itemset Mining Algorithms
  • Maximal and Closed Frequent Itemset
  • Mining Maximal Frequent Itemsets: the GenMax Algorithm
  • Mining Closed Frequent Itemsets: the Charm Algorithm
  • CHARM Algorithm Implementation
  • Data Mining Methods
  • Prediction
  • Important Terms Used in Bayesian Network
  • Density-Based Clustering Algorithm
  • DBSCAN
  • Kernel Density Estimation
  • Mining Data Streams
  • Time Series Forecasting

Lessons 9: Cluster Analysis

  • Clustering
  • Distance Measurement Techniques
  • Hierarchical Clustering
  • Analysis of Protein Patterns in the Human Cancer‐Associated Liver
  • Recognition Using Biometrics of Hands
  • Expectation Maximization Clustering Algorithm
  • Representative‐Based Clustering
  • Methods of Determining the Number of Clusters
  • Optimization Algorithm
  • Choosing the Number of Clusters
  • Bayesian Analysis of Mixtures
  • Fuzzy Clustering
  • Fuzzy C‐Means Clustering

Lessons 10: Big Data Visualization

  • Big Data Visualization
  • Conventional Data Visualization Techniques
  • Tableau
  • Bar Chart in Tableau
  • Line Chart
  • Pie Chart
  • Bubble Chart
  • Box Plot
  • Tableau Use Cases
  • Installing R and Getting Ready
  • Data Structures in R
  • Importing Data from a File
  • Importing Data from a Delimited Text File
  • Control Structures in R
  • Basic Graphs in R

Hands-on LAB Activities

Introduction to the World of Big Data

  • Discussing Big Data Characteristics
  • Discussing Big Data

Big Data Storage Concepts

  • Discussing Big Data Storage

NoSQL Database

  • Discussing the NoSQL Database

Big Data Processing, Management, and Cloud Computing

  • Implementing the Data Processing Cycle
  • Discussing Big Data Processing and Management Concepts - Part I
  • Discussing Big Data Processing and Management Concepts - Part II

Driving Big Data with Hadoop Tools and Technologies

  • Discussing Components of Hadoop
  • Discussing Big Data Using Hadoop Tools and Technologies

Big Data Analytics

  • Discussing Big Data Analytics

Big Data Analytics with Machine Learning

  • Discussing Machine Learning

Mining Data Streams and Frequent Itemset

  • Implementing Frequent Itemset Mining Using R
  • Determining the Support Count and Confidence Count
  • Implementing the Eclat Algorithm Using R
  • Implementing Apriori Algorithm Using R

Cluster Analysis

  • Implementing K-Means Clustering

Big Data Visualization

  • Creating a Connection in a New Workbook
  • Creating a Bar Chart
  • Creating a Line Chart
  • Creating a Pie Chart
  • Creating a Bubble Chart
  • Creating a Box Plot
  • Assigning Value to a Variable
  • Using the length(), mean(), and median() Functions
  • Using the matrix() Function
  • Using the if-else Statement
  • Using the for Loop
  • Using the while Loop
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