Big-Data - Grid Computing & HPC

Companies in various industries such as financial services, healthcare and medical research, telecom or logistics might have completely different business processes and missions, yet could encounter similar bottleneck performance issues. As the amount of available data unstoppably grows, together with it grows the effort and the time that are required in order to process, store, aggregate and analyse it. Throwing more computational power at the problem, in the form of additional new hardware, could improve the situation but only to some extent.

Solutions based exclusively on hardware scaling approach have a limited effect or flexibility and usually are not the cost-effective way to go. Introducing a grid computing middleware into your system can prove to be significantly more effective in terms of performance, scalability and cost. That does not contradict the concept of adding more hardware resources to the system. Grid middleware could be used to create compute clusters (physical, virtual or both) hence allowing to fully utilize available hardware resources dynamically balancing and distributing the workload over the cluster nodes. In such scenario, leveraging cluster computing could deliver noticeably better performance and lower the costs.

For "time critical" environments where the calculation/analysis is to be performed in a given and very tight time frame, distribution of the workload is significantly more productive as it allows to take advantage of all available resources. Mission critical environments having strict reliability requirements on top of the performance requirements, simply cannot rely on a single source of computation. Cluster computing could offer the necessary redundancy to address those requirements as well.

Distributed computing and specifically grid computing could offer a flexible, scalable and cost-effective approach to overcoming the aforementioned difficulties.
A customized grid middleware component could provide a cost-effective solution tailored to your needs, whether your organization offers financial services (frequently executing complex computations while running risk analysis, End-of-day (EOD) processing, derivatives valuation etc.), aggregating big data or streaming large amounts of information.

Grid computing solutions can be set up flexibly according to your needs, in order to provide the desired scalability for future expansion. A compute cluster can be set up locally on available commodity hardware or remotely in the cloud, leveraging the advantages of cloud computing. A distributed cluster or multiple clusters can be configured in a heterogeneous setup, where some nodes run on local machines while others are deployed on virtual cloud servers. Such set up could combine the advantages of both worlds, not only in terms of technology but in terms of licensing fees and overall costs either.
A custom solution could be based on open-source frameworks such as Spark, Storm and Ignite or compatible commercial products, according to the requirements and budget goals.