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Data Analyser GUI

Journal: International Journal of Science and Research (IJSR) (Vol.8, No. 2)

Publication Date:

Authors : ; ;

Page : 1054-1058

Keywords : Big Data; RDBMs; Data Warehouse;

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Global digital content created will increase some 30 times over the next ten years to 35 zettabytes Big data is a popular, but poorly defined marketing buzzword. One way of looking at big data is that it represents the large and rapidly growing volume of information that is mostly untapped by existing analytical applications and data warehousing systems. Examples of this data include high-volume sensor data and social networking information from web sites such as FaceBook and Twitter. Organizations are interested in capturing and analyzing this data because it can add significant value to the decision making process. Such processing, however, may involve complex workloads that push the boundaries of what is possible using traditional data warehousing and data management techniques and technologies. This article looks the benefits analyzing big data brings to the business. It examines different types of big data and offers suggestions on how to optimize systems to handle different workloads and integrate them into a single infrastructure. Two important data management trends for processing big data are relational DBMS products optimized for analytical workloads (often called analytic RDBMSs, or ADBMSs) and non-relational systems (sometimes called NoSQL systems) for processing multi-structured data. A non-relational system can be used to produce analytics from big data, or to preprocess big data before it is consolidated into a data warehouse. Big Data is a concept that is leading the world right now and taking it by storm. We have tried to discuss on the fundamentals of Big Data and tools and techniques associated with it. We also have tried to categorize the Big Data elements into a model and tried to derive Big Data Ecosystem from it. The V Model for the Big Data has been defined and categorized into 3V, 4V or 5V dependent on the organization which uses it and under which business scenario. Catering to the aforementioned models, we have classified data into various forms and explanations have been provided on the same to gain a better insight and understanding on the same

Last modified: 2021-06-28 17:24:41