What is Big Data? It has been called the “IT wave of the present era” because of its potential to change business in a fundamental way. Big Data is a discipline that deals specifically with methods to analyze, collect, or otherwise manipulate large data sets which are too complex or large to be efficiently dealt with by conventional data processing software. In addition, Big Data is also being used in specific areas of science and engineering, such as astronomy, computer science, finance, health care, and so on. Today, the term “Big Data” is starting to pop up more often as an informal term, and as such it deserves some close scrutiny.
In simple terms, Big Data is described as the combination of several forms of data analysis – from large database management systems, such as Oracle, Hibernation or Sybase; into the analytical processing power of computers themselves, from a web crawler spidering through billions of web pages to the statistical analysis of those pages’ content. In essence, Big Data is the aggregation of enormous amounts of previously inaccessible, hard-to-manage data into an easily accessible form. However, Big Data is not without its controversy. The main worry is that it may replace – or at least deplete – the role of analysts, specifically of scientists and data technicians, in managing and analyzing this wealth of information. In order to address this issue, several approaches have been developed over the years.
MapReduce is one of the many different data analysis technologies designed to deal with large amount of data. MapReduce is built on the idea of batch-logic technology, originally developed by Google. Batch-logic technology allows for the easy execution of large amount of work pieces, in a manner of putting together small segments of a large amount of work into a single piece of software. In the case of MapReduce, this piece of software is designed to deal with large amount of data from multiple sources and to do so in a much more efficient manner than would be possible with a single processor running multiple processes on an individual computer. Through the use of map Reduce, Google has been able to accelerate its cluster’s processing time by leveraging the machine’s own memory in a way that has never been done before. As such, MapReduce has revolutionized the way that data warehouses are built and managed at scale.
Despite its simplicity, there are two major concepts involved in MapReduce, namely: a data warehouse and a map. A data warehouse is composed of different sources of data. For example, it could contain product information derived from the products list, customer information derived from the contact table, technical data derived from the specifications and tests, etc. The sources of data can be anything that could potentially be considered part of the functionality of an organization. In the case of MapReduce, the various sources of data are consolidated into a single Map. Thus, instead of having to maintain separate source maps for each source of data, the Map contains all the source data in one map.
As previously mentioned, Map Reduce speeds up the analytics process because it allows the system to efficiently manage and sort large sets of unprocessed data. The performance boost comes from the fact that Map Reduce achieves higher efficiencies by combining the processing of large amounts of data with that of analytical tasks. With this concept, map Reduce allows the system to divide analytical tasks into smaller tasks, thereby speeding up the overall processing time of the system. As such, what is meant by “what is the big data analysis” becomes clear – Map Reduce performs much more efficiently than the traditional data analysis method.
However, what is important to understand about Map Reduce is that it is not a standalone piece of software. Rather, it is one of the components of an Apache Hadoop distribution like Hadoop Distributed Data Discovery (HDFD). For example, Twitter uses Hadoop Distributed Memory Analytics (DMA) to collect a large amount of user data and then used it for the purpose of analyzing the data. Facebook’s use of Hadoop is further examples of the application of big data analytics.
Hadoop framework is based on the Map-Reduce Architecture. In essence, Hadoop makes use of Map Reduce to achieve the set goal – the collection, processing and analysis of large amount of data that can be processed in real time. In addition, Map Reduce also takes care of the redundancy of jobs that require joins. This redundancy enables users to reap benefits from increased capacity and throughput of their data analysis project. Basically, Map Reduce takes care of the complexity of managing real-time data, providing a solution that can be used in almost any commercial enterprise software applications.
While Hadoop and Map Reduce technologies are designed for the analysis of large amount of data and are consequently, very useful for making insights on trends in the financial domain, they go beyond this. Beyond the key insights that Hadoop provides, it can also provide other inferences about changes in time. For example, taking a look at Twitter since 2021, it is easy to see how things have changed over the years, both for the website as well as its user base. Based on Twitter data, one can come to the conclusion that there has been significant increases in the number of new users, which can also be associated with rises in the number of tweets per day. The importance of big data analysis in this context is not limited to the business domain only, but also for public sector research and analysis.