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Understanding Big Data Scalability: Big Data Scalability Series, Part I

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Understanding Big Data Scalability: Big Data Scalability Series, Part I

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Description

  • Copyright 2015
  • Dimensions: 7" x 9"
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-359870-5
  • ISBN-13: 978-0-13-359870-4

Get Started Scaling Your Database Infrastructure for High-Volume Big Data Applications 

“Understanding Big Data Scalability presents the fundamentals of scaling databases from a single node to large clusters. It provides a practical explanation of what ‘Big Data’ systems are, and fundamental issues to consider when optimizing for performance and scalability. Cory draws on many years of experience to explain issues involved in working with data sets that can no longer be handled with single, monolithic relational databases.... His approach is particularly relevant now that relational data models are making a comeback via SQL interfaces to popular NoSQL databases and Hadoop distributions.... This book should be especially useful to database practitioners new to scaling databases beyond traditional single node deployments.”

—Brian O’Krafka, software architect 

Understanding Big Data Scalability presents a solid foundation for scaling Big Data infrastructure and helps you address each crucial factor associated with optimizing performance in scalable and dynamic Big Data clusters.

Database expert Cory Isaacson offers practical, actionable insights for every technical professional who must scale a database tier for high-volume applications. Focusing on today’s most common Big Data applications, he introduces proven ways to manage unprecedented data growth from widely diverse sources and to deliver real-time processing at levels that were inconceivable until recently.

Isaacson explains why databases slow down, reviews each major technique for scaling database applications, and identifies the key rules of database scalability that every architect should follow.

You’ll find insights and techniques proven with all types of database engines and environments, including SQL, NoSQL, and Hadoop. Two start-to-finish case studies walk you through planning and implementation, offering specific lessons for formulating your own scalability strategy. Coverage includes 

  • Understanding the true causes of database performance degradation in today’s Big Data environments
  • Scaling smoothly to petabyte-class databases and beyond
  • Defining database clusters for maximum scalability and performance
  • Integrating NoSQL or columnar databases that aren’t “drop-in” replacements for RDBMSes
  • Scaling application components: solutions and options for each tier
  • Recognizing when to scale your data tier—a decision with enormous consequences for your application environment
  • Why data relationships may be even more important in non-relational databases
  • Why virtually every database scalability implementation still relies on sharding, and how to choose the best approach
  • How to set clear objectives for architecting high-performance Big Data implementations 

The Big Data Scalability Series is a comprehensive, four-part series, containing information on many facets of database performance and scalability. Understanding Big Data Scalability is the first book in the series.

Learn more and join the conversation about Big Data scalability at bigdatascalability.com.

Sample Content

Online Sample Chapter

Introduction to Understanding Big Data Scalability

Sample Pages

Download the sample pages (includes Chapter 1 and Index)

Table of Contents

Preface ix

About the Author xii

Chapter 1: Introduction 1

What You Will Learn 1

The Challenge of Big Data 2

Today’s Big Data Explosion 3

Background for This Book 6

Why the Focus on Database Sharding? 8

Summary 9

Chapter 2: Why Databases Slow Down 10

The Database Slowdown Curve 10

A Hard-Won Lesson 11

The Enemies of Database Performance 14

How to Identify Database Slowdown Issues 21

Summary 23

Chapter 3: What Is Big Data? 24

What Is Big Data Anyhow? 24

Sources of Big Data 28

Summary 32

Chapter 4: Big Data in the Real World 33

Some Real-World Examples of Big Data 33

FullContact 34

Social Point 36

Summary 38

Chapter 5: Scaling Your Application 39

The Goals of a Scalable Application Platform 39

The Excitement of a High-Growth Success 41

Application Scalability Fundamentals 42

A Typical Online Application Architecture 46

Analytics Application Architectures 50

Scaling an Analytics Application 53

How to Scale a Traditional Online Application 53

Summary 55

Chapter 6: When to Scale Your Database 56

The Last Mile of Application Scalability 56

How Do You Know When to Scale Your Database? 57

Options for Increasing Database Performance 58

Indications of the Need for Scale 65

Summary 68

Chapter 7: All Data Is Relational 69

Relational Data Overview 69

The Meaning of Data 70

Relationships Matter 73

Why Data Modelling Is Critical to Success 74

Summary 76

Chapter 8: It’s All About Sharding 77

Sharding: The Ultimate Answer to Database Slowdown 77

The Laws of Databases 78

Sharding Defined 80

Black-Box Sharding 83

Relational Sharding 86

Summary 88

Chapter 9: Scaling Big Data: The Endgame 89

The Game of Big Data Scalability 89

Scaling Big Data Theory 90

The Big Data Endgame 95

Data Locality 98

Summary 99

Index 101

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