The Pros And Cons Of Big Data

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The advent of social networking and Internet of Things has resulted in an exponential increase in the volume of data. Simultaneously, the need to process and analyze the large volumes of data for business decision making has also increased. Many business and scientific applications need to process petabytes of data in efficient manner on daily basis. This data is categorized as "Big Data" due to its sheer Volume, Variety and Velocity and has resulted in a problem for the industry due to the inability of conventional database systems and software tools to manage and to process the big data sets within tolerable time limits. The scale, diversity, and complexity of Big Data require new architecture, techniques and algorithms to manage
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Gaining critical business insights by querying and analyzing such a very big amounts of data is suitable need of the hour and become a challenge to the standard data taking out approach. This data comes from a broad range of sources such as logs, social communication, sensors etc. The organizations are facing new challenges to analyze vast amounts of information. The different aspects of the big data make it difficult to manage. Big data requires speed for its processing. It requires fast information retrieval techniques that can retrieve data from this huge amount. Four V’s that are used to explain Big data’s complexity…show more content…
Limitations of Traditional Databases to support Big Data

Traditional Databases are not suitable for Big Data because of so many reasons. Some of them are listed below:

Traditional Databases –

- Finds it challenging to handle such huge data volumes
- The majority of the data comes in a semi-structured or unstructured format from social media, audio, video, texts, and emails. Traditional databases can’t categorize unstructured data. They are designed to accommodate structured data.
- Traditional Databases lack in high velocity because these are designed for steady data retention rather than rapid growth
- Even if traditional databases are used to store and process big data it will turn out to be very expensive.

So, Big data requires exceptional technologies to efficiently process large quantities of data within tolerable time limits. Technologies being applied to big data include massively parallel processing databases, distributed file system, distributed databases, cloud computing platforms, the mining grids, the Internet and scalable storage systems. Real time or near-real time information delivery is one of the defining characteristics of Big Data analytics.

2. Hadoop: Solution for Big data

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