What is Big Data Analysis? The Science of extracting Useful Information From Large Datasets

What is Big Data? Data has the ability to solve many business problems and improve organizational performance. Today’s business environment, however, gives us reason to believe that we will be able to use big data to our advantage more than ever. The rapid growth of the internet and computer technology has given us reason to believe that the future holds many exciting solutions to business problems. From customer demand to market trends, large-scale analysis of data sets can provide businesses with the information they need to make informed decisions that will improve their bottom line.

what is big data analysis

What is Data analysis? In its most simple form, data analysis is the extraction of value from large, complex data sets by applying statistical techniques. Big Data is a discipline which takes into account the fact that no two days are alike; therefore, no two Datasets will be similar enough to allow for easy, meaningful manipulation or enrichment. This leads to Data mining, which is the extraction of value from Datasets using statistical methods. One example of such a technique is the usage of Hive. The official definition of a Hive Table is “A supervised database in which each cell in the table corresponds to a key and each key corresponds to a value.”

An example of a Dataset used in Data analysis includes a text corpus from which an employee’s name is extracted. Hive allows an analyst to apply linear and logistic regression on the text corpus to derive the demographics of the employee. This example would not be possible without a Hive Table and MapReduce. MapReduce is an advanced variant of the relational databases used in relational databases such as MySQL, Oracle, and Postini.

MapReduce is extremely flexible because it can apply different algorithms to the same data set without the use of a program server. Because there are no rules or limitations dictating how the data should be stored, MapReduce allows an analyst to apply any algorithm to any piece of data. This leads to the ability of MapReduce to leverage the power of large amounts of data, and the parallel computing capabilities that come along with this capability makes MapReduce an ideal platform for what is known as large-scale Data Analytics.

So what is big data analysis? MapReduce and other forms of Data analytics technologies allow us to rapidly analyze and visualize large amounts of data sets. MapReduce takes care of the analytical part, while developers and designers can take full advantage of MapReduce’s parallel processing capabilities. This enables fast development cycles and highly cost effective deployment. Developers and designers can also easily adapt MapReduce’s features to their specific data analysis scenarios. Also, because it is an open source project, many third party tools and frameworks have been built to make working with MapReduce easier.

In order for us to understand what is the big data analysis, we first need to understand what is actually meant by big data. Companies have traditionally analyzed data sets using manually controlled software tools. As a result, programmers have developed poor interfaces, limited flexibility, and poor scaling. Furthermore, many analysts and developers have become frustrated with manually controlled processes because they have yielded little to no insights.

With MapReduce and other emerging technologies, developers and analysts are now able to rapidly derive analytical insight from large volumes of processed data. Such analytical insights are useful information, because they allow us to make better decisions, purchase decisions, and even generate more relevant insights. What is useful information, however, does not need to be mathematical equations, nor do we need to be able to crunch numbers on a spreadsheet. Analyzing large amounts of unprocessed data is more about how things fit together, as well as how things match up in space, time, and other dimensions. We are talking here about real-time visualization and machine learning techniques that help us see the world as a system, and not just an accumulation of individual parts.

There is no doubt that the combination of Map Reduce and advanced analytics will allow for massive leveraging of available resources in a shorter period of time. This will lead to increased profits and lower costs, something that is very important to all stakeholders in the supply chain. Ultimately, the combination of Map Reduce, advanced analytics, and social data analytics will create a new platform on which companies can make better informed decisions in a more cost effective manner. In essence, we are looking at new approaches to information management and a revolution in how the world markets and consumes its information. Indeed, what is big data analytics is about revolutionizing how we collect, analyze, and act upon the massive amounts of data that is available to us today.