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	Scrum For Dummies by Mark C. Layton
	Price : Rs 699.00
	Your Price : Rs 594.15

Scrum For Dummies is an easy to use guide to managing the tricky transition from a traditional project management methodology to the new and most popular agile framework. As the most efficient, successful methodology for team project management, Scrum relies on transparency, flexibility and fluidity to deliver a final product that fulfills the needs of all stakeholders. Written in easy-to-read Dummies style, this book walks you through the core principles of Scrum and provides a roadmap for tangible implementation.

Introduction Book Preview

Chapter 1: Getting an Overview of Big Data
What is Big Data?
History of Data Management – Evolution of Big Data
Structuring Big Data
Types of Data
Elements of Big Data
Volume
Velocity
Variety
Veracity
Big Data Analytics
Advantages of Big Data Analytics
Careers in Big Data
Skills Required
Future of Big Data
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 2: Exploring the Use of Big Data in Business Context
Use of Big Data in Social Networking
Business Intelligence
Marketing
Product Design and Development
Use of Big Data in Preventing Fraudulent Activities
Preventing Fraud Using Big Data Analytics
Use of Big Data in Detecting Fraudulent Activities in Insurance Sector
Fraud Detection Methods
Use of Big Data in Retail Industry
Use of RFID Data in Retail
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 3: Introducing Technologies for Handling Big Data
Distributed and Parallel Computing for Big Data
Introducing Hadoop
How does Hadoop Function?
Cloud Computing and Big Data
Features of Cloud Computing
Cloud Deployment Models
Cloud Delivery Models
Cloud Services for Big Data
Cloud Providers in Big Data Market
In-Memory Computing Technology for Big Data
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 4: Understanding Hadoop Ecosystem
Hadoop Ecosystem
Hadoop Distributed File System
HDFS Architecture
Features of HDFS
MapReduce
Features of MapReduce
Hadoop YARN
HBase
Features of HBase
Hive
Pig and Pig Latin
Sqoop
ZooKeeper
Flume
Oozie
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 5: Understanding MapReduce Fundamentals and HBase
The MapReduce Framework
Exploring the Features of MapReduce
Working of MapReduce
Exploring Map and Reduce Functions
Techniques to Optimize MapReduce Jobs
Hardware/Network Topology
Synchronization
File System
Uses of MapReduce
Role of HBase in Big Data Processing
Characteristics of HBase
Installation of HBase
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 6: Understanding Big Data Technology Foundations
Exploring the Big Data Stack
Data Sources Layer
Ingestion Layer
Storage Layer
Physical Infrastructure Layer
Platform Management Layer
Security Layer
Monitoring Layer
Analytics Engine
Visualization Layer
Big Data Applications
Virtualization and Big Data
Virtualization Approaches
Server Virtualization
Application Virtualization
Network Virtualization
Processor and Memory Virtualization
Data and Storage Virtualization
Managing Virtualization with Hypervisor
Implementing Virtualization to Work with Big Data
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 7: Storing Data in Databases and Data Warehouses
RDBMS and Big Data
Issues with the Relational Model
Non-Relational Database
Issues with the Non-Relational Model
Polyglot Persistence
Integrating Big Data with Traditional Data Warehouses
Big Data Analysis and Data Warehouse
Changing Deployment Models in Big Data Era
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 8: Storing Data in Hadoop
Introducing HDFS
HDFS Architecture
Using HDFS Files
Hadoop-Specific File System Types
HDFS Commands
The org.apache.hadoop.io package
HDF
HDFS High Availability
Introducing HBase
HBase Architecture
Storing Big Data with HBase
Interacting with the Hadoop Ecosystem
HBase in Operation – Programming with HBase
Installation
Combining HBase and HDFS
Selecting the Suitable Hadoop Data Organization for Applications
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 9: Processing Your Data with MapReduce
Recollecting the Concept of MapReduce Framework
Developing Simple MapReduce Application
Building the Application
Executing the Application
Points to Consider while Designing MapReduce
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 10: Customizing MapReduce Execution
Controlling MapReduce Execution with InputFormat
InputSplit
RecordReader
FileInputFormat
Implementing InputFormat for Compute-Intensive Applications
Implementing InputFormat to control the Number of Maps
Implementing InputFormat for Multiple HBase Tables
Reading Data with Custom RecordReader
Organizing Output Data with OutputFormats
Customizing Data with RecordWriter
Optimizing MapReduce Execution with Combiner
Controlling Reducer Execution with Partitioners
Implementing a MapReduce Program for Sorting Text Data
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 11: Testing and Debugging MapReduce Applications
Performing Unit Testing for MapReduce Applications
Unit Testing the Mapper Component
Unit Testing the Reducer Component
Integration Testing of the Mapper-Reducer Combination
Performing Local Application Testing with Eclipse
Logging for Hadoop Testing
Application Log Processing
Defensive Programming in MapReduce
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 12: Understanding Hadoop YARN Architecture
Background of YARN
Revisiting MapReduce
Limitations of MapReduce
Advantages of YARN
YARN Architecture
ResourceManager
ApplicationManager
Integration of ResourceManager and ApplicationManager
Working of YARN
YARN Schedulers
CapacityScheduler
FairScheduler
Backward Compatibility with YARN
Script Compatibility
Binary Compatibility
Source Compatibility
YARN Configurations
YARN Commands
Administration Commands
User Commands
Log Management in Hadoop 1
Log Management in YARN
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 13: Exploring Hive
Introducing Hive
Getting Started with Hive
Hive Variables
Hive Properties
Hive Queries
Data Types in Hive
Built-In Functions in Hive
Hive DDL
Creating Databases
Viewing a Database
Dropping a Database
Altering Databases
Creating Tables
Creating a Table Using the Existing Schema
Dropping Tables
Altering Tables
Using Hive DDL Statements
Data Manipulation in Hive
Loading Files into Tables
Inserting Data into Tables
Update in Hive
Delete in Hive
Using Hive DML Statements
Data Retrieval Queries
Using the SELECT Command
Using the WHERE Clause
Using the GROUP BY Clause
Using the HAVING Clause
Using the LIMIT Clause
Executing HiveQL Queries
Using JOINS in Hive
Inner Joins
Outer Joins
Cartesian Product Joins
Map-Side Joins
Joining Tables
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 14: Analyzing Data with Pig
Introducing Pig
The Pig Architecture
Benefits of Pig
Installing Pig
Properties of Pig
Running Pig
Running Pig Programs
Getting Started with Pig Latin
Pig Latin Structure
Pig Latin Application Flow
Working with Operators in Pig
FOREACH
ASSERT
FILTER
GROUP
ORDER BY
DISTINCT
JOIN
LIMIT
SAMPLE
SPLIT
FLATTEN
Working with Functions in Pig
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 15: Using Oozie
Introducing Oozie
Main Functional Components of Oozie
Benefits of Oozie
Installing and Configuring Oozie
•Understanding the Oozie Workflow
Execution of Asynchronous Actions in Oozie
Implementing the Oozie Workflow
Oozie Recovery Capabilities
Oozie Workflow Life Cycle
Oozie Coordinator
Types of Oozie Coordinator
Oozie Coordinator Lifecycle Operations
Oozie Bundle
Oozie Parameterization with EL
Workflow Functions
Coordinator Functions
Bundle Functions
EL Functions
Oozie Job Execution Model
Accessing Oozie
Oozie SLA
Event Status
SLA Status
Oozie Activity
The Oozie SLA Subsystem
SLA Language Schema
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 16: NoSQL Data Management
Introduction to NoSQL
Characteristics of NoSQL
Evolution of Databases
Aggregate Data Models
Key Value Data Model
Document Databases
Relationships
Graph Databases
Schema-Less Databases
Materialized Views
Distribution Models
CAP Theorem
Sharding
MapReduce Partitioning and Combining
Composing MapReduce Calculations
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 17: Understanding Analytics and Big Data
Comparing Reporting and Analysis
Reporting
Analysis
The Analytic Process
Types of Analytics
Basic Analytics
Advanced Analytics
Operationalized Analytics
Monetized Analytics
Characteristics of Big Data Analysis
Points to Consider during Analysis
Frame the Problem Correctly
Statistical Significance or Business Importance?
Making Inferences versus Computing Statistics
Developing an Analytic Team
Skills Required for an Analyst
Convergence of IT and Analytics
Understanding Text Analytics
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 18: Analytical Approaches and Tools to Analyze Data
Analytical Approaches
Ensemble Methods
Text Data Analysis
History of Analytical Tools
Graphical User Interfaces
Point Solutions
Data Visualization Tools
Introducing Popular Analytical Tools
The R Project for Statistical Computing
IBM SPSS
SAS
Comparing Various Analytical Tools
Installing R
Installing R on a Windows Computer
Installing R on a Macintosh Computer
Installing R on a Linux Computer
Installing RStudio on Windows
Installing RStudio on Linux
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 19: Exploring R
Exploring Basic Features of R
Statistical Features
Programming Features
Packages
Graphical User Interfaces
Exploring RGui
R Console
Developing a Program
Quitting R
Exploring RStudio
Handling Basic Expressions in R
Basic Arithmetic in R
Mathematical Operators
Variables in R
Calling Functions in R
Working with Vectors
Storing and Calculating Values in R
Creating and Using Objects
Interacting with Users
Handling Data in R Workspace
The ls() Function
The rm() Function
The getwd() Function
The save() Function
The load () Function
Executing Scripts
Creating Plots
Accessing Help and Documentation in R
Using Built-in Datasets in R
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 20: Reading Datasets and Exporting Data from R
Using the c() Command
Reading and Combining Numerical Data
Reading and Combining Text Data
Reading Both Numeric and Text Values in R
Using the scan() Command
Reading the Text Data Using the scan() Command
Using Clipboard to Create the Data
Reading the Data of a File from Disk
Reading Multiple Data Values from Large Files
Using the read.csv() Command
Using the read.table() Command
Reading Data from R Studio
Exporting Data from R
Using the write.table() Command
Using the write.csv() Command
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 21: Manipulating and Processing Data in R
Selecting the Most Appropriate Data Structure
Creating Data Subsets
Creating Subsets in Vectors
Creating Subsets in Data Frames
Merging Datasets in R
Using the merge() Function
Using the cbind Function
Using the rbind() Function
Sorting Data
Sorting Data
Ordering Data
Reverse Sort
Putting Your Data into Shape
Transposing Data
Converting Data to Wide or Long Formats
Melting Data to Long Format
Casting Data to Wide Format
Managing Data in R Using Matrices
Reshaping a Vector into a Matrix
Accessing Matrix and Subsetting the Data
Managing Data in R Using Data Frames
Creating Data Frames
Accessing Data Frames
Merging Data Frames
Performing Operations on Data Frames
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 22: Working with Functions and Packages in R
Using Functions Instead of Scripts
Transforming an R Script into a Function
Returning Results in R
Reducing the Number of Lines in an R Function
Assigning the Function Objects
Writing Function Without Braces
Using Arguments in Functions
Using Dot Argument in Function
Passing Functions as Arguments
Anonymous Functions
Local and Global Environment of Functions
Built-in Functions in R
Numeric Functions
Character Functions
Statistical Probability Functions
Miscellaneous Functions
Introducing Packages
Working with Packages
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 23: Performing Graphical Analysis in R
Using Plots
Using Plots for a Single Variable
Using Plots for Two Variables
Using Plots for Multiple Variables
Designing Special Plots
Saving Graphs to External Files
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 24: Integrating R and Hadoop and Understanding Hive
RHadoop?An Integration of R and Hadoop
Installing RHadoop
Using RHadoop
Text Mining in RHadoop
Data Analysis Using the MapReduce Technique in RHadoop
Data Mining in Hive
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 25: Data Visualization-I
Introducing Data Visualization
Techniques Used for Visual Data Representation
Types of Data Visualization
Applications of Data Visualization
Visualizing Big Data
Deriving Business Solutions
Turning Data into Information
Tools Used in Data Visualization
Proprietary Data Visualization Tools
Open-Source Data Visualization Tools
Analytical Techniques Used in Big Data Visualization
Tableau Products
Installation of Tableau Public
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 26: Data Visualization with Tableau (Data Visualization-II)
Introduction to Tableau Software
Tableau Desktop Workspace
Operations on Data
Data Analytics in Tableau Public
Using Visual Controls in Tableau Public
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 27: Social Media Analytics and Text Mining
Introducing Social Media
Introducing Key Elements of Social Media
Introducing Text Mining
Understanding Text Mining Process
Sentiment Analysis
Performing Social Media Analytics and Opinion Mining on Tweets
Online Social Media Analysis
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 28: Mobile Analytics
Introducing Mobile Analytics
Define Mobile Analytics
Mobile Analytics and Web Analytics
Types of Results from Mobile Analytics
Types of Applications for Mobile Analytics
Introducing Mobile Analytics Tools
Location-based Tracking Tools
Real-time Analytics Tools
User Behavior Tracking Tools
Performing Mobile Analytics
Challenges of Mobile Analytics
Summary
Quick Revise
Multiple-Choice Questions
Subjective Questions

Chapter 29: Finding a Job in the Big Data Market
Importance and Scope of Big Data Jobs
Big Data Opportunities
Skill Assessment for Big Data Jobs
Roles and Responsibilities in Big Data Jobs
Business Analyst for Big Data
Big Data Scientist
Software Developer for Big Data
Gaining a Foothold in the Big Data Market
Take Your Time
Preparing the Big Data Skill Learning and Testing Mechanism
Basic Educational Requirements for Big Data Jobs
Basic Technological Requirements for Big Data Jobs

ISBN - 9788126555864

Pages : 412