The map operation provides an arbitrary way to transform each file into a new file, whereas the reduce operation combines two files. 5 As you would expect, there are two operations in MapReduce: map and reduce.
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One year later, Google published a new paper describing how to perform operations across the Google File System, an approach that came to be known as MapReduce. Figure 1.1 shows the findings of this report notice that every other year, the world’s information has grown tenfold. However, a more relevant finding from this report was that our footprint of digital information is growing at exponential rates. At that time, there were about 10 million terabytes of digital information, which is roughly about 10 million storage drives today.
3 This report noted that digital information surpassed analog information around 2003. The World Bank report on digital development provides an estimate of digital and analog information stored over the past decades. In contrast, analog information represents everything we have stored by any nonelectronic means such as handwritten notes, books, newspapers, and so on. Mathematician George Stibitz used the word digital to describe fast electric pulses back in 1942, 2 and to this day, we describe information stored electronically as digital information. Based on the storage and processing technologies employed, it is possible to distinguish four distinct phases of development: premechanical (3000 BC to 1450 AD), mechanical (1450–1840), electromechanical (1840–1940), and electronic (1940–present).
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We hope that this is a journey you will enjoy, that will help you to solve problems in your professional career, and to nudge the world into making better decisions that can benefit us all.Īs humans, we have been storing, retrieving, manipulating, and communicating information since the Sumerians in Mesopotamia developed writing around 3000 BC. The last chapter of this book provides you with tools and inspiration to consider contributing back to the Spark and R communities. The last chapters present additional topics, like real-time data processing and graph analysis, which you will need to truly master the art of analyzing data at any scale. Subsequent chapters help you move away from your local computer into computing clusters required to solve many real world problems. At which point, you will have the tools required to perform data analysis and modeling at scale. You then move into learning how to analyze large-scale data, followed by building models capable of predicting trends and discover information hidden in vast amounts of information.
It is the goal of that chapter to help anyone grasp the concepts and tools required to start tackling large-scale data challenges which, until recently, were accessible to just a few organizations.
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You will learn how to install and initialize Spark, get introduced to common operations, and get your very first data processing and modeling task done. Finally, this leads us to introduce sparklyr, a project merging R and Spark into a powerful tool that is easily accessible to all.Ĭhapter 2 presents the prerequisites, tools, and steps you need to perform to get Spark and R working on your personal computer. With this as a backdrop, we introduce the R computing language, which was specifically designed to simplify data analysis. First, it introduces Apache Spark as a leading tool that is democratizing our ability to process large datasets. This chapter presents the tools that have been used to solve large-scale data challenges. The increasing speed at which data is being collected has created new opportunities and is certainly poised to create even more. With information growing at exponential rates, it’s no surprise that historians are referring to this period of history as the Information Age.