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Big Data Analytics in Cloud Computing






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Big Data Analytics in Cloud Computing

The vast increase in the amount of data storage places, the ever increasing number of internet users worldwide, continuous increase in video and audio stream, social media updates, tweets and the proliferation of blogs has led to the generation of a large amount of digital data.

The vast tools which are now available for sharing, storing and creating this data do less to help the surge of massive data, because these tools however efficient, aid data growth. To extract useful knowledge from large amount of digital data sets; programming tools, smart and scalable analytics services and also applications are required, hence the need for big analytical tools – cloud computing.

In big data analytics, high performance processors which work with intensive data mining algorithms are used in order to produce timely results. Cloud computing is an effective panacea to the problem of computational and data storage in big data analytics. There are already lots of big data in the cloud and these set of data have been touted to increase in the near future.

One of the companies that predicted the future rise in the amount of data generated is an information Technology and Advisory firm Garner which predicted that by year 2016, more than half of the data of big companies will be stored in the cloud. With this, it is imperative that the duo of pervasive and scalable data analytics platform be implemented in the cloud.

 

Cloud-based Big Data analytics

Big data includes heavy amounts of data, which are often from different sources, and are always difficult to process using regular data management tools and techniques. Big data also includes the variety of the data generated through different streams that are sensitive and also important, this complexity makes big data analytics even more demanding. It is however important to state here that big data management in cloud computing includes data storage, data retrieval, data integrity and accessibility to data. This is what makes cloud a preferred choice for data management as it meets the needs of organizations whatever their data requirements may be.

In the processing of data online, many firms generate heavy amounts of data, this may include business and scientific applications such as; tax payment collection, social studies, biosciences, customer related information, social information and only a well-orchestrated cloud computing service can adequately meet the demands of data storage and retrieval in this information age.

The combination of big data analytics, scalable computing systems and also technical expertise is necessary in managing and efficiently managing big data today. There are no much cloud based analytics platforms available today, but researches are going on to make sure Cloud based analytics will be in every industry in a few years.

There are some current solutions available which are majorly based on open programming environment and help build a new set of SaaS suites for big data analytics.

Users need not concern themselves with cloud platform or programming details with the above technique.

 

Workflows in Big Data Analytics

Workflows are made of complex graphs of many concurrent tasks; developers can use this to address the complexity of scientific and business applications. Workflow provides a paradigm that encompasses all the steps of data analytic. This approach makes working on big data easier.

 

Conclusion

High level easy to use design tools are necessary for cloud based data analytics which deal with huge distributed data resources. This ensures further research and development in several key areas. New models and tools are necessary to advance the cloud from a data management platform to a pervasive and data analytics infrastructure.

 

 

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