Big Data: A technology for the future

As relational databases became more prominent, analysis of massive databases came by as a real challenge towards the beginning of the century. In 2013, the name ‘Big Data’ was adopted by the Oxford Dictionary.

What is Big Data?

Big Data characteristically are the data sets that are very large and complex. They are hence complicated to be processed by traditional formats of data processing. A few of the top technologies used for processing Big Data include Apache Hadoop, Microsoft HDInsight, NoSQL, and Hive.

As technologies such as the Internet of Things (IoT) and mobile technologies found popularity, Big Data was more in demand.

Why Big Data is important

A large number of people use digital devices. They generate significant amounts of data. This includes their geolocation and the data generated by fitness apps and social media apps.

Big data is broadly a term for the collection and analysis of significant amounts of data in ways that enhance business operations. Big Data and business processes are nowadays making a gradual shift towards the cloud.

5 Vs. of Big Data

Industry experts associate 5 Vs. with Big Data, which must all be addressed separately. Each of the 5 Vs. renders effects over business profits and cycles and interacts with other Vs. as well.


Volume refers to the amount of data an organization plans to use for gaining insights. The storage of data and methodology used for the same are significant metrics in this regard.


Variety is the comfort level involved with ingesting segregated data types. A few of the tools for enabling the same are Apache NiFi and Gobblin.


By delivering results quicker, Big Data technologies ensure that a business puts inconsistent efforts towards streamlining work structures. Results generated closer to real-time is more usable.


Sorting makes data sets more usable to a certain extent. Pieces of information collected may have variable levels of significance.

Role of Big Data analytics

It is the insights and uses cases that bring value to Big Data. The voluminous data is otherwise just unusable information.

Big Data analytics technically refers to the sets of processes that examine large data sets. This helps figure out patterns that are otherwise difficult to identify

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