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Hurwitz & Associates - Insight is Action

Big Data for Dummies

Big Data for Dummies

Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work.

Learn to:

  • Leverage big data tools and architectures
  • Explore how big data can transform your business
  • Integrate structured and unstructured data into your big data environment
  • Use predictive analytics to make better decisions

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Table of Contents

Contents At a Glance

Introduction

Part I: Getting Started with Big Data

Chapter 1: Grasping the Fundamentals of Big Data
Chapter 2: Examining Big Data Types
Chapter 3: Old Meets New: Distributed Computing

Part II: Technology Foundations for Big Data
Chapter 4: Digging into Big Data Technology Components
Chapter 5: Virtualization and How It Supports Distributed Computing
Chapter 6: Examining the Cloud and Big Data

Part III: Big Data Management
Chapter 7: Operational Databases
Chapter 8: MapReduce Fundamentals
Chapter 9: Exploring the World of Hadoop
Chapter 10: The Hadoop Foundation and Ecosystem
Chapter 11: Appliances and Big Data Warehouses

Part IV: Analytics and Big Data
Chapter 12: Defi ning Big Data Analytics
Chapter 13: Understanding Text Analytics and Big Data
Chapter 14: Customized Approaches for Analysis of Big Data

Part V: Big Data Implementation
Chapter 15: Integrating Data Sources
Chapter 16: Dealing with Real-Time Data Streams and Complex Event Processing
Chapter 17: Operationalizing Big Data
Chapter 18: Applying Big Data within Your Organization
Chapter 19: Security and Governance for Big Data Environments

Part VI: Big Data Solutions in the Real World
Chapter 20: The Importance of Big Data to Business
Chapter 21: Analyzing Data in Motion: A Real-World View
Chapter 22: Improving Business Processes with Big Data Analytics: A Real-World View

Part VII: The Part of Tens
Chapter 23: Ten Big Data Best Practices
Chapter 24: Ten Great Big Data Resources
Chapter 25: Ten Big Data Do’s and Don’ts

Glossary
Index

Read a Chapter

 Chapter 1 Grasping the Fundamentals of Big Data

In This Chapter

▶ Looking at a history of data management
▶ Understanding why big data matters to business
▶ Applying big data to business effectiveness
▶ Defining the foundational elements of big data
▶ Examining big data’s role in the future

Managing and analyzing data have always offered the greatest benefits and the greatest challenges for organizations of all sizes and across all industries. Businesses have long struggled with finding a pragmatic approach to capturing information about their customers, products, and services. When a company only had a handful of customers who all bought the same product in the same way, things were pretty straightforward and simple. But over time, companies and the markets they participate in have grown more complicated. To survive or gain a competitive advantage with customers, these companies added more product lines and diversified how they deliver their product. Data struggles are not limited to business. Research and development (R&D) organizations, for example, have struggled to get enough computing power to run sophisticated models or to process images and other sources of scientific data.

Indeed, we are dealing with a lot of complexity when it comes to data. Some data is structured and stored in a traditional relational database, while other data, including documents, customer service records, and even pictures and videos, is unstructured. Companies also have to consider new sources of data generated by machines such as sensors. Other new information sources are human generated, such as data from social media and the click-stream data generated from website interactions. In addition, the availability and adoption of newer, more powerful mobile devices, coupled with ubiquitous access to global networks will drive the creation of new sources for data.