By Author – Shubhangi Agarwal
Big data is non-traditional strategy and technology used to organize, process, and gather insights from large datasets. While the problem of working with data that exceeds the computing power or storage of a single computer is not new, the pervasiveness, scale, and value of this type of computing have greatly expanded in recent years.
In this article, we will talk about big data on a fundamental level and define common concepts you might come across while researching the subject. We will also take a high-level look at some of the processes and technologies currently being used in this space.
What Is Big Data?
An exact definition of “big data” is difficult to nail down because projects, vendors, practitioners, and business professionals use it quite differently. With that in mind, generally speaking, big data is:
Large Datasets: The category of computing strategies and technologies that are used to handle large datasets
In this context, “large dataset” means a dataset too large to reasonably process or store with traditional tooling or on a single computer. This means that the common scale of big datasets is constantly shifting and may vary significantly from organization to organization.
Why Are Big Data Systems Different?
The basic requirements for working with big data are the same as the requirements for working with datasets of any size. However, the massive scale, the speed of ingesting and processing, and the characteristics of the data that must be dealt with at each stage of the process present significant new challenges when designing solutions. The goal of most big data systems is to surface insights and connections from large volumes of heterogeneous data that would not be possible using conventional methods.
Big Data Analytics:
Big Data Analytics is one of the great new frontiers of IT. Data is exploding so fast and the promise of deeper insights is so compelling that IT managers are highly motivated to turn big data into an asset they can manage and exploit for their organizations. Emerging technologies such as the Hadoop framework and MapReduce offer new and exciting ways to process and transform big data – defined as complex, unstructured, or large amounts of data – into meaningful insights, but also require IT to deploy infrastructure differently to support the distributed processing requirements and real-time demands of big data analytics. Big data is data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are five dimensions to big data known as Volume, Variety, Velocity and the recently added Veracity and Value.
Lately, the term “Big Data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analysis methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but thats not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on “Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research. You can take data from any source and analyze it to find answers that enable
- Cost Reductions
- Time reductions
- New product development and optimized offerings
- Smart decision making
The importance of big data doesnt revolve around how much data you have, but what you do with it.