Big Data and Hadoop at a Glance
Data is the fuel of the future. To optimize data, technologies such as big data and Hadoop have emerged and are making a huge difference in business intelligence and strategies. Hadoop, on one hand, is an open source distributed computing platform that connects with thousands or handful of servers to build an ecosystem for the huge volume of data, on the other big data procures huge volume of data for quantitative and qualitative analysis purposes. Both these technologies are resulting in fast-paced development of businesses and have made data more crucial than ever. These technologies are being utilized by industries to enhance their current business models or develop new marketing strategies to benefit the sales. With the capability of storing and analyzing huge volume of data these technologies have enabled organizations to understand flaws with current trend more precisely and predict the upcoming ones.
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The duo offers characteristics that have made it widely acceptable are-
• Scalability: Unlike the traditional relational database management system (RDBMS) Hadoop offers high scalability. The Hadoop framework can be easily modified as per the business requirements without disturbing or reconfiguring the existing one.
• Cost Effective: Being open source makes the big data and Hadoop highly cost-effective and also the infrastructural cost is comparably low to RDBMS as it uses a distributed framework which simply makes it scalable as well.
• Flexible: Big data architecture supports storage of all kinds of data whether structured or non-structured unlike the traditional RDBMS, which makes it compatible with all kinds of business intelligence software and embraced by all industries.
• Speed: Computational speed in big data architecture is very fast as it uses multiple system and resource sharing concepts which means that huge volume of data is distributed into data sets and stored at different nodes. This framework allows computing or running a set of queries at the same time of complete data saving computational time.
• Resilient to Failure: The data in Hadoop is stored in different nodes and has multiple copies of it stored in different nodes in the cluster so even if one node fails the cluster has another copy of its data which can be accessed for future and failure node is replaced by a new system. The framework is designed in such a way that at any point in time a data set has at least three copies.
The capabilities of big data and Hadoop have opened up gates for new business and job opportunities. Data analyst, data mining, big data expert and data scientist are few of the hot topics in the market and adoption of both the technologies has seen a vast growth and is evident to reach new heights in the proximate future.
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