Unleashing Power of Data

Unleashing Power of Data | 21 [ THE EVOLUTION OF BIG DATA ] horizontally and vertically scalable. Big data requires a DBMS to manage and operate large volumes of data. Traditional DBMSs were not equipped to manage huge influx of data that big data generates. A scalable data plat form accommodates rapid changes in the growth of data, in terms of traffic and volume. When a company has a scalable data platform, it is also prepared for the potential of growth in its data needs. ƒ Advanced Anal yt i cs : To analyze business information f r om a v a r i e t y o f da t a sources, advanced analytics uses predictive modeling, machine learning algorithms, deep learning, bus iness process automat ion and other statistical methods. As such, DBMS must be able to handle such advanced analytics by providing the required support for these types of algorithms and tools, thus enabling organization to gain greater functionality from its data assets. ƒ Distributed Architectures: Distributed architecture helps the organizations to manage their vast amount of data which is present or divided in more than one location, as it is not limited to any single computer system. It is divided over the network of various systems. There are two t ypes of categories of Distributed Architectures, namely shared- nothing and shared-disk. Shared nothing architecture i s ut i l i zed in di st r ibuted computing inwhich each node is independent and different nodes are interconnected by a network. Shared Disk Architecture is adopted in distributed comput ing in which the nodes share the same disk devices but each node has i ts own private memory. ƒ R e a l - t i me P r o c e s s i n g : DBMSs must offer real-time data processing in order to handle big data. Such systems process data as soon as they receive input and produce the processed data as output. As a result, real-time processing requires a continuous flow of data. In-memory data processing allows applications to store data in memory instead of on disk, enabl ing quicker access to the data. It is the practice of acting on data entirely in computer memory. Streaming data processing allows applications to process data as it is generated, which provides real-time insights into the data. By using stream processing technology, data streams can be processed, stored, analyzed, as and when generated in real-time. Businesses are adopt ing solutions that provide an end-to-end streaming data archi tecture bui l t on the scalability of cloud data lakes. Data plays a vital role in understanding valuable insights. Big data has c h a n g e d h o w w e manage, analyze, and leverage data across businesses, industries, and sectors. B u s i n e s s e s a r e generating new data throughevery interaction with technology. The impac t o f b i g da t a o n b u s i n e s s e s i s exponential, however, organizations ought to organize and visualize da t a r i gh t f u l l y and seamlessly to leverage it effectively. Dun & Bradstreet