With the advancement in the software development field, apps and computer programs have become much heavier than before. Before, the user interfaces of applications and computer programs were very simplistic and didn’t transmit much data. However, nowadays, there is a lot involved, and with the rise of visual content, making applications load faster is becoming more challenging. Fortunately, in-memory computing has made an entrance in the data management and processing fields to solve this problem. Here is a breakdown of how in-memory computing works and its characteristics.
In-memory computing layer
To properly understand the in-memory computing architecture, it is important to understand which layer it is in the data access layering system. All in memory computing software plays as the middleware between the application or computer program and the RAM hardware on the device. This layer helps with storing and retrieving data on RAM hardware which minimizes the time it takes to load the stored insights.
The data stored on the RAM modules are rapidly accessible and eliminate retrieving insights from the database. This results in quicker loading speeds since the data is cached on the device’s randomly accessible memory. When transmitting large data, using a cluster of computers to contribute towards the quicker transmission of that information by combining their RAM is the true definition of in-memory computing.
Deployment of in-memory computing in the cloud
In-memory computing started becoming a thing in personal computer use cases to make applications load faster. However, data scientists have found a way to manipulate these systems to have a cloud-based solution aimed at solving enterprise application problems. Large companies with huge amounts of data sets can be very slow on the user-end, which negatively impacts the user experience.
A solution to solve this was in order, and in-memory computing was a viable solution when replicated in the cloud. IaaS and SaaS companies partnered with enterprises to offer a cluster of nodes that have RAM, which allows application data to be easily accessible. This also helps the company internally with the various systems that they might be using. Deploying this solution in the cloud also makes it affordable since companies can just pay for the number of nodes they require.
Life cycle of the data
When the data is stored in the RAM of this cluster of nodes, it is only temporarily there, so what is the full life cycle of these insights? The life cycle of the data stored in an in-memory computing network is pretty much what happens with cached information in personal computers. When starting a computer program, all the data that might be needed is cached in the RAM so it can be accessed quicker.
The data is retrieved from the database of the computer program or local storage unit. Once you shut down the application, the data is wiped from the RAM, opening some space up for other applications. The same happens in an in-memory computing network, only operational data is stored, and once an application is shut down, the insights are wiped.
Use of in-memory computing
In-memory computing is versatile and can be implemented in a variety of scenarios in large organizations. With the rise in virtual offices, the relevance of in-memory computing has been highlighted. How? Most companies that use virtual offices have a remote workforce that accesses data sets at the same time. As a result, to facilitate the large data requests that might come streaming in at the same time, using in-memory computing might be the solution.
By using in-memory computing in this scenario, there will be more nodes to retrieve data, and that prevents overcrowding of the database directly. At the same time, since the data is cached, it is much quicker to load the company applications that use this computing technique. Not to mention the other benefits of in-memory computing that include streamlining data sources for BI tools.
Implementation of hybrid systems
The main benefit of in-memory computing is that it is versatile to a wide variety of customizations and modifications. One prime example of this is the ability to implement a hybrid system with in-memory computing. Unlike other static data management and caching systems, in-memory computing allows complex system integration. With this technology, you can augment a variety of databases either stored on-premises, at a separate data storage location, or even cloud-based solutions.
This can facilitate your data caching needs even when transitioning to a different storage system. For example, when you are transitioning to cloud-based solutions, in-memory computing can pull data from both sources, including your current one. Hybrid systems can be very tricky to set up, but cloud-based in-memory computing systems simplify this to a great extent. Cloud-based in-memory computing solutions are plug and play, which eliminates all the complexities of setting up hybrid data caching systems.
Deployment in enterprise-level scenarios
Deploying in-memory computing in enterprise-level scenarios has been simplified greatly with plug-and-play solutions offered by SaaS providers. The deployment scenarios are limitless and can work for user experience improvements or internal system development. In-memory computing can help streamline reporting by gleaning relevant and operational information from various sources. From then, the insights can be quickly pulled into the relevant reports using BI tools.
The tools can then visualize the data and provide concise, timely reports with very limited latency. Another enterprise-level scenario deployment would be in end-user applications developed by a company. Caching user data using in-memory computing can help with hastening response times on the application. As a result, the overall user experience is improved, and you can also store the cached data in a warehouse if necessary.
Final thoughts
In-memory computing has a very versatile architecture that allows a wide variety of enterprise applications and hybrid deployments. This data management solution uses the similar technology of RAM in personal computers, but it is just scaled. The scaled cluster of nodes offering RAM hastens the data processing times, improving the end-user experience. At the same time, in-memory computing facilitates quick reporting for real-time analytics offering relevant business intelligence.