Introduction
In this blog, we will be discussing Big Data, its analysis, the benefits offered by analytics, and at the same time, what disadvantages come along with it. It has been a long struggle for businesses to find an approach that captures and analyzes their customers, products, and services. This approach comes as a competitive advantage for those who know how to utilize analytics properly. Not only for businesses, but Data analytics has also done wonders for Research and Development organizations.
Big Data Analytics is a thing of the present and future. But before indulging much into this, we need to understand the whole process of evolution in the history of Data Management.
Data Management
Most of the new stages of managing data are built on their predecessors. It is a holistic approach toward data that includes technological advances in hardware, software, networking, and computing models. Each management wave has evolved out of the necessity as a solution for a particular problem. These waves are, namely-
Creating Manageable Data Structures
The traditional way of storing data in flat files with no structure was of zero value. Data structures were introduced so that some detailed understanding could be made possible. It became easier to organize data, compare transactions, and use it for specific purposes.
Relational data model and a tool to manage it: Relational Database Management System (RDBMS) imposed structure and improved performance. Then, there arises another issue of Duplication. To solve this, ER Model was developed that added additional abstraction. New relationships could be created between data sources without any complex programming.
Still, the volume of data was not under control. This resulted in the formation of Data Warehouses that created subsets of data, each for a particular purpose. But, that did not offer much speed and agility. Therefore, Data marts had to be created.
That was all about the structured form of data, but where will unstructured data go?
With the rise of the web in the 1990s, there was a need among organizations to move beyond documents and sort something out for unstructured data. This led to another wave of Data Management: Web and Content Management.
Web & Content Management
As the amount of unstructured data expanded so much in volume, companies began to store it in Binary Large Objects, known as BLOBs. In these, a relational database would be used to store an unstructured data element as a chunk of data. It was not suitable for coming growth in the amount of data.
Object Database Management System, known as ODBMS, includes a programming language and a structure.
A more unified model for a set of disconnected data. A platform that took care of business process management, version control, information recognition, text management, and collaboration.
Managing Big Data
Big data Management is built on top of the evolution of data management practices. But for the first time, the cost of computing cycles and storage has reached a tipping point. This has helped organizations that would typically compromise by using snapshots or subsets just because of storage cost and limitations in processing to store and analyze data.
No technology transition happens in isolation. Technologies that revolve around Big Data are Parallel processing, Distributed Systems, Virtualization, Cloud Computing, and many more.
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