Companies like Facebook and Google amongst others are facing challenges unique to themselves after the growth in internet – especially after Web 2.0. Most of the companies receive Petabytes and Terabytes of data every day, something which is of concern to the companies.
That’s why the companies are ready to pay a lot of money to hire Managed IT Support by www.resolutets.com and other such companies with an ability to secure the data; because the consequences of its breach or leak can be very devastating.
Processing data from such companies require high computation resources. So, companies like Facebook and Google have massive privately owned data centers for storing and processing their data.
However, not many companies can afford such data centers to cater that kind of data-intensive computing. Small and medium-sized businesses, on the other hand, can utilize modern technology to compensate what they lose through cloud technology.
Cloud technology is the merging of various technologies like parallel, distributed and grid computing. It then incorporates all the benefits of such computing which offer an upper hand over traditional computing and medium. Main features of cloud technology include:
Different architectures have been developed to allow the processing of such amounts of data. These architectures can be used to perform data intensive computing in the cloud, and they include:
Data-driven applications are designed to process terabytes and petabytes of data sets. However, it becomes a challenge to feed such volumes of data as data may not exist in a single location and may be distributed in different geographical location.
This consumes a lot of time and can result in delays in the data processing. The following are some of the major challenges faced in data intensive computing in the cloud.
Some of these challenges can be easily overcome through the smart use of grid computing. Grid computing provides high computational power as well as storage facility through extraction of various resources which can be found in different administrative domain.
Through the data network, users can perform data intensive computing and process the large data sets stored in different places.