Batch data processing does not demand real time processing and instant network response. The self contained datasets do not demand intensive CPU times. The data processing cycles are likely to include applications, such as data conversion, data cleansing, data mining, data compression, data encryption, simulation, risk modeling and graphics rendering. However, the hardware and software resources required for these processes are large and often capital intensive. As a result, Cloud based batch processing applications, which are priced nominally and are wholly scalable, have been found best suited for batch processing. The Cloud backup repositories provide a relatively inexpensive and flexible source of computing power for such tasks.
Software development is one batch processing task that has adopted the Platform as a Service (PaaS) model successfully. The Cloud based environment provides an intuitive support for these projects. More so, if these projects require global collaboration and projects modules are distributed across locations. The scalability of the Cloud “on demand” ensures that project teams can obtain hardware and software resources on the fly and project delays can be minimized. Virtual test labs can be configured to assist the teams in testing the software and taking advantage of features that permit performance testing with simulated loads that would otherwise require expensive infrastructures.
Research teams, backing up large volumes of data to the Cloud backup system iteratively, have adopted Cloud backups for the fast-ramp-up capabilities of the technology. Commodity applications like email and personal productivity tools are being augmented with Cloud based productivity suites to save time and money.
Cloud backup service providers are promoting Cloud backups as a business continuity and disaster recovery option. The distributed, robust, reliable and scalable infrastructure with its bare metal and anywhere, anytime data recovery features is highly appealing to industries, which cannot invest huge sums in business continuity and disaster recovery projects.
Predictable and unpredictable peak loads can be handled with equal ease with Cloud backups in place. Organizations can plan predictable peak loads, such as year-end statements in financial service firms, by planned scale up of Cloud backup storage space. Unpredictable peak loads can be handled by dynamically balancing the loads between in house applications and Cloud based applications, using Cloud based applications that are fast gaining popularity.
It is expected that more and more batch data processing applications will find commercial uses as the Cloud matures and businesses demand streamlined services from Cloud backup service providers.