In-memory computing simply means utilizing some sort of on-demand middleware program that enables one to store information onto a node, across a network of computers, and subsequently process it in parallel, across multiple nodes. Think of operational datasets usually stored on a central database that you could literally store on many different computers across the network. One computer would access and update a file while another might check it or even use it as an index file. Each time the file is accessed, the corresponding data is placed onto the nodes.

In-memory is very effective because it greatly speeds up application execution and data access times, especially when multiple workloads are involved. The biggest advantage, however, is in its ability to provide real-time analytics for applications. Companies like Salesforce, IBM, and Microsoft have all made extensive use of in-memory computing in order to provide complete visibility into their sales and customer activity. In fact, these three giants alone account for most of the revenues in this field. Here are a few other examples of companies that make use of this technology in their day-to-day operations:

Uses of In-memory Computing

Financial Services: The financial services industry requires fast execution and real-time processing. Because of this, applications must be able to deal with large amounts of data and extremely high load conditions. That is where in-memory computing shines for these industries. Scalable scalability is key in the industry, which is why most financial services IT departments utilize gridgain. Scalability is important because while there may be cases where your application can scale out efficiently, there will also be times where it may slow down too much. One solution to this problem is having a data cache that is in RAM instead of on a shared cluster.

Customer Service: Similar to the healthcare industry, customer service operators often face the same challenges when dealing with hundreds of calls in a day. This situation is even more frustrating for call centers, which has one of the highest instances of in-memory computing throughout the enterprise. In-memory analytics and data caches make it easier for call centers to deal with spikes in traffic and have the capacity to run automated queries that reduce processing time for agents. When this happens, agents can deal with other agents in the queue, reducing backlog issues and improving customer service for both customers and agents. Scalability is crucial for both operations and revenues in this industry.

Parallel distributed processing: Scalability is critical in both the commercial and consumer markets for parallel distributed processing. In most cases, this operation relies on servers that are not part of the production network. For instance, Hadoop clusters run on mainframes on the provider's own network. Because of this, both Hadoop and In-memory Computing meet a technical necessity for providing superior scaling solutions.

Chip scaling: Although in-memory computing is gaining momentum because of its price advantage, there are still some challenges to chip scaling. In chip scaling involves a series of operations that add to the workload of programmers and system managers. The first one is managing workload and making sure that workloads are appropriate for the available resources. Another one is ensuring the proper use of all of the available I/O resources (I/O ports). Then there is the matter of addressing issues in I/O ports such as buffering latencies and buffering transfer speeds. All of these activities will significantly impact chip performance, particularly for near-term growth plans.

Performance: Performing calculations on large amounts of memory requires significant overhead. In addition, programmers must pay close attention to the programming code to determine when it is appropriate to utilize the in-memory computing system. In parallel distributed processing calculations, it is often necessary to run the calculations on the main system while storing the results in memory until they are ready to be used on another system. Therefore, accessing memory on multiple cores is a technical necessity that cannot be ignored. While an in-memory system can provide faster execution, it is not a technical necessity for parallel distributed processing.

From a programming and system management perspective, in-memory computing offers several benefits over streaming. First, programming is faster and less error prone when tasks are executed in the compact space of an in-memory device. Second, it reduces the time needed to retrieve large amounts of data and enables programmers to write to large amounts of memory more quickly. And third, it makes it easier to implement robust streaming applications that can handle large volumes of input or output data without slowing the system down. However, programmers must still pay close attention to the programming code to determine when it is appropriate to use the in-memory computing system.