Big Data and Traditional Databases

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Big Data
Big Data is a popular phrase used to describe a massive amount of both structured and unstructured data. Big data is difficult to process with traditional database and software techniques because of large quantity of data. Volume, velocity, variability and variety are three characteristics of Big Data.

• Volume: Big data implies vast volumes of data. These data is generated by machines, networks and social media the volume of data to be analyzed is massive. Volume refers to the amount of data to be handled. Many organizations are producing large quantity of data internally or externally.

• Velocity: velocity refers to the speed of generation of data or how fast data is generated and processed to meet their objectives. The flow of data is massive and continuous.

• Variety: Organizations collects variety of data in several ways. Data which are collects by using internally or externally can be structured, semi-structured or unstructured. As a example social media sources, such as face book, blogs and tweets and data coming from sensors can be semi structured or unstructured. This variety of unstructured data creates problems for storage and analyzing data.[5]

Big data is important because it enables to analyze large amounts of raw data where it was not practical, either for cost or technology reasons. Big data is the term for a collection of data sets so large and complex, so it becomes difficult to process using on hand database management tools or traditional data processing applications. Big data differs from traditional, transactional data in a number of ways. Those are volume or storage issue, big data is often not relational (Some of the more structured data can be readily put into relational format but unstruct...

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... requires massive performance and scalability. But old platforms poor in scaling, loading data, respond, processing capacity for analytics and handling concurrent mixed workloads.

Store and analyze approach, and analyze and store approach can be identified as the two main techniques for analyzing big data.

Big Data analytics helps to explore hidden correlations, hidden patterns and provide other valuable insights into the data. This analysis helps to data scientists and other users to evaluate large volumes data, which might be left untapped by traditional business intelligence (BI) systems. Big data analytics can provide competitive advantages to organizations. It also help to achieve business benefits such as more effective marketing and increased revenue. The key goal of Big Data analytics is to assist organizations in making superior business decisions. [8]

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