- Big-Data - Grid Computing & HPC
Big-Data - Grid Computing & HPC
Big Data - Grid Computing - High Performance Computing
We can offer multiple cost-effective and scalable high performance computing (HPC) solutions, custom tailored for your needs. Take advantage of the significant benefits without astronomic costs added to the budget of your IT department.
We will be happy to assist your organization in the process of digital transformation, entering the world of Bid Data, Big Data Streaming & Analytics and High Performance Grid Computing, getting ready for tomorrow.
Are you "drowning" in an ocean of Big Data? Your system fails to process large streams of data or time critical analysis computation is too slow?
Utilizing an advanced Grid Computing solution to distribute the workload over a custom cluster or leveraging a high performance Big Data streaming framework could save the day.
Parallel execution, distributed processing and in-memory grid computing can deliver a noticeable performance optimization to your enterprise system, making the processing up to hundreds of times faster.
Modern frameworks such as Apache Ignite, Apache Spark, Apache Storm and others, focus on offering solutions to the common challenges related to Bid Data, large scale analytics and high performance computing in general.
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.