Big Data For Dummies

Judith S. Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman

• Éditeur: For Dummies
• Année: 2013
• ISBN:
  9781118644171
• Édition: 1
• Sujet: Ciencia de la computación
• Sous-sujet: Aplicaciones empresariales

Find the right big data solution for your business or organization

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.

• Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals
• Authors are experts in information management, big data, and a variety of solutions
• Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more
• Provides essential information in a no-nonsense, easy-to-understand style that is empowering

Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

Part I

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In this part . . .

Trace the evolution of data management.

Define big data and its technology components.

Understand the different types of big data.

Integrate structured and unstructured data.

Understand the difference between real-time and non-real-time data.

Scale your big data operation with distributed computing.

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.

Although each data source can be independently managed and searched, the challenge today is how companies can make sense of the intersection of all these different types of data. When you are dealing with so much information in so many different forms, it is impossible to think about data management in traditional ways. Although we have always had a lot of data, the difference today is that significantly more of it exists, and it varies in type and timeliness. Organizations are also finding more ways to make use of this information than ever before. Therefore, you have to think about managing data differently. That is the opportunity and challenge of big data. In this chapter, we provide you a context for what the evolution of the movement to big data is all about and what it means to your organization.

The Evolution of Data Management

It would be nice to think that each new innovation in data management is a fresh start and disconnected from the past. However, whether revolutionary or incremental, most new stages or waves of data management build on their predecessors. Although data management is typically viewed through a software lens, it actually has to be viewed from a holistic perspective. Data management has to include technology advances in hardware, storage, networking, and computing models such as virtualization and cloud computing. The convergence of emerging technologies and reduction in costs for everything from storage to compute cycles have transformed the data landscape and made new opportunities possible.

As all these technology factors converge, it is transforming the way we manage and leverage data. Big data is the latest trend to emerge because of these factors. So, what is big data and why is it so important? Later in the book, we provide a more comprehensive definition. To get you started, big data is defined as any kind of data source that has at least three shared characteristics:

Extremely large Volumes of data

Extremely high Velocity of data

Extremely wide Variety of data

Big data is important because it enables organizations to gather, store, manage, and manipulate vast amounts data at the right speed, at the right time, to gain the right insights. But before we delve into the details of big data, it is important to look at the evolution of data management and how it has led to big data. Big data is not a stand-alone technology; rather, it is a combination of the last 50 years of technology evolution.

Organizations today are at a tipping point in data management. We have moved from the era where the technology was designed to support a specific business need, such as determining how many items were sold to how many customers, to a time when organizations have more data from more sources than ever before. All this data looks like a potential gold mine, but like a gold mine, you only have a little gold and lot more of everything else. The technology challenges are “How do you make sense of that data when you can’t easily recognize the patterns that are the most meaningful for your business decisions? How does your organization deal with massive amounts of data in a meaningful way?” Before we get into the options, we take a look at the evolution of data management and see how these waves are connected.

Understanding the Waves of Managing Data

Each data management wave is born out of the necessity to try and solve a specific type of data management problem. Each of these waves or phases evolved because of cause and effect. When a new technology solution came to market, it required the discovery of new approaches. When the relational database came to market, it needed a set of tools to allow managers to study the relationship between data elements. When companies started storing unstructured data, analysts needed new capabilities such as natural language–based analysis tools to gain insights that would be useful to business. If you were a search engine company leader, you began to realize that you had access to immense amounts of data that could be monetized. To gain value from that data required new innovative tools and approaches.

The data management waves over the past five decades have culminated in where we are today: the initiation of the big data era. So, to understand big data, you have to understand the underpinning of these previous waves. You also need to understand that as we move from one wave to another, we don’t throw away the tools and technology and practices that we have been using to address a different set of problems.

Wave 1: Creating manageable data structures

As computing moved into the commercial market in the late 1960s, data was stored in flat files that imposed no structure. When companies needed to get to a level of detailed understanding about customers, they had to apply brute-force methods, including very detailed programming models to create some value. Later in the 1970s, things changed with the invention of the relational data model and the relational database management system (RDBMS) that imposed structure and a method for improving performance. Most importantly, the relational model added a level of abstraction (the structured query language [SQL], report generators, and data management tools) so that it was easier for programmers to satisfy the growing business demands to extract value from data.

The relational model offered an ecosystem of tools from a large number of emerging software companies. It filled a growing need to help companies better organize their data and be able to compare transactions from one geography to another. In addition, it helped business managers who wanted to be able to examine information such as inventory and compare it to customer order information for decision-making purposes. But a problem emerged from this exploding demand for answers: Storing this growing volume of data was expensive and accessing it was slow. Making matters worse, lots of data duplication existed, and the actual business value of that data was hard to measure.

At this stage, an urgent need existed to find a new set of technologies to support the relational model. The Entity-Relationship (ER) model emerged, which added additional abstraction to increase the usability of the data. In thi...