Hadoop: The most efficient solution to Big Data

What is Big Data?

In this age and time, the amount of data being produced on a daily basis is inconceivable. Study and analysis state that data generation is growing exponentially and the trend is said to continue. By the year 2020, experts predict that data generation is to hit 1.7 megabytes per second per living person on this planet. Despite generation and availability of humongous business information, only a negligible part of it is analyzed and put to use. This means that there are large chunks of raw information lying unutilized by businesses, which otherwise could have been harnessed to power the growth of an organization. To handle such raw information, Hadoop technology is utilized to maneuver that data through inspection and analysis. Even the second generation organizations like Google and Facebook use Hadoop as the primary tool for data access, storage, processing and management. Websites that integrate elements from various external sources involve titanic exchange of information. How is all that data managed? Database Management Systems or Relational Database Management systems cannot be the answer anymore. The structured query style of data processing is not efficient for such huge data. This is because, data storage is never eventual with big data. The staggering increase in big data cannot be managed through DBMS due to the limitations in its scalability and accessibility features. This is when tools like Hadoop come to the rescue.

What does Hadoop do?

Organizations today manage colossal volumes of data turning to the information market for the majority of operations. Sometimes, the data is duplicated and replicated in order to disseminate information throughout the distinguishing departments of the organization. This hazardous duplicate data makes for an exorbitant storage space making the system fall inefficient. Companies continue to invest in storage instead of investing in managing the data methodically. Primary and distinctive data should be indexed and preserved in order to dispose inconsistent data. Heterogeneous content is obtained through recurring processing of repetitive information (that amounts to vast data in an organization). Yet, storage is not something to be compromised. Hadoop is a tool that solves the shortcomings of storing such heterogeneous data.

Hadoop enables distributive processing mechanism to produce multi-nodal structure that capacitates information in individual storage elements. For example, to store 10TB of data, a 10TB data storage equipment will not be required. Instead, storage could be facilitated on 10 different storage equipments with each storage capacity bring 1 TB.  By reducing the individual data footprint through smaller systems, data curation efforts are eventually reduced.  Hadoop operates at a rapid data-transfer rate due to its apportioned information processing mechanism. A multi-node framework helps the apparatus stay robust, responsive and efficient.

Hadoop incorporates Google’s MapReduce algorithm which works on application partitioning as the prime source of processing. It is efficient due to its speed, security, scalability, portability, reliability, robustness and cost-effectiveness.

  • Speed: The multi-node system makes processing and access faster as the time required is brought down drastically due to parallel search mechanism.
  • Security: Even though multiple data sets or data nodes are hacked, damaged or become inoperative, the system will still be functional due to independent distributive data storage apparatus. The system as a whole is holistically protected due to apportioned dependency formula.
  • Scalability: It is easily scalable as it accommodates horizontal scaling options which do not require complex scaling implementation. Thus, saving immense effort and time to the organization and this eventually helps boost ROI.
  • Inexpensive: Hadoop’s unique and efficient operating mechanism and installation makes for a cost-effective solution for the major data problems.

Industries that generate Big Data:

  • Second generation IT companies
  • Banks
  • Government
  • Retail giants
  • Manufacturing hubs
  • Education sector
  • Health Care Units

Organizations like these with large amounts of data face many challenges right from data capturing, storage, analysis, retrieval, replicating, distributing and updating. Illimitable amount of data also poses corresponding security threat. Hadoop’s parallel distributive system secures the data through data duplication and minimal cycle access. Hadoop empowers an organization with advanced data analytics by especially enabling the legacy systems to leverage the data. The Hadoop system is highly robust, fast, secure and cost effective. Therefore, it is the most reliable solution for today’s Big Data problems.

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