Cloud migrations continue unabated as businesses seek cost advantages, scalability, flexibility, and better performance. But not all cloud migrations are the same or deliver the intended benefits. 

Many businesses seek the same application capabilities in the cloud as they have on-premises. They only look for benefits in cost and speed. Such a net-generation infrastructure upgrade is fast and costs less than a complete redesign. 

But applications alone do not deliver the business outcomes that deliver competitive advantage. Satisfying today’s customers requires a neat, simple UX but a complex back-end and the ability to process streaming data. The enterprise needs solutions for real-time application integration and user interactions. They also need capabilities to process artificial intelligence. To deliver such outcomes, enterprises must adopt the systems approach while migrating to the cloud. They have to modernise the entire tech stack, with an overriding focus on making processes more agile and productive.

Modernising the tech stack requires upgrades to several subsystems. Businesses upgrade workflow orchestration capabilities, data storage and processing, and system integration platforms. Deploying In-memory computing resources also becomes critical to attaining the needed enhancements.

In-memory computing as a managed service

In-memory computing runs calculations in computer memory, such as the RAM. Data processing moves away from the disk and the database into RAM-based object-oriented data models. Today’s large-scale, complex calculations need specialised systems software to perform these tasks.

The most popular methods of in-memory computing are data grids and stream processing. Data grids involve multiple computers that interact with each other to process large data sets. Stream processing is ingesting a continuous data stream and analysing the data in real time. 

Most businesses could not afford such high-powered in-memory computing capabilities until recently. They had to invest heavily to acquire the software, train staff, and maintain the stack. In-memory computing remained cost-feasible only for applications with long-term or comprehensive use. It did not suit modern enterprises with more flexible processes.

The availability of in-memory computing companies as managed services improves affordability and accessibility. Enterprises may now avail the technology anytime, anywhere, and without high upfront investments. 

In-memory computing use cases

Enterprises migrating resources to the cloud leverage in-memory computing resources for many use cases. These solutions enable operational efficiency, speed, and scalability for large-scale and small-scale projects. Using conventional database-based memory retards operational efficiency in such instances. 

Seamless data processing 

Operational performance in the cloud depends on robust infrastructure. A high cycle time is unacceptable for a digital business. A latency of even milliseconds in data processing retards customer satisfaction and productivity. 

Cloud-based applications may be fast and scalable. But the constraints of traditional relational databases may increase latency. In-memory boost reduces the lag and accelerates application performance in a big way.

Most companies adopt a phased approach to cloud migration and application modernisation. Implementing in-memory managed services makes things seamless for such companies. The in-memory computing capabilities work well when migrating and updating legacy apps. The in-memory resources deliver speed, scalability stability, and enable business agility.

Apps related to customer engagement, e-commerce systems, and marketing need real-time data feeds. A cloud-based in-memory solution delivers the capacity and performance for such specialised apps.

Enabling AI-powered computation

Artificial Intelligence (AI) powered applications unlock several possibilities. But such apps can provide accurate analysis and instantaneous responses only if they can process large data volumes in real-time. 

Most enterprises struggle to enable such capabilities while side-by-side balancing cloud migration requirements. In-memory solutions offer the capacity to run these high-memory-intensive AI applications.

Enabling unobtrusive security validations

Security validations on transactional processes have become unavoidable today. Robust validations keep malicious external agents and rogue insiders at bay. But such validations may also cause lags. Enterprises have to do such validation without impacting user experience. Many enterprises, especially e-commerce portals, use in-memory computing for security enhancements. In-memory solutions allow seamless and fast validations without compromising performance.

Making pre-production environments cost-effective 

Most companies struggle with provisioning non-production environments. They either provide the bare minimums for the functionality to work or end up overspending on capacity and performance. 

Moving pre-production environments to the cloud offers the most optimal solution. The company saves on infrastructure costs, and developers get the right tools to simulate and test solutions. Co-opting in-memory technology allows developers to configure and test features and performance.

In-memory solutions allow data grid or stream processing benefits without the associated administrative overheads. Enterprises deploying cloud-based in-memory solutions do have to spend money or make extra efforts to configure, manage and maintain the solution. 

Enabling high-performance for temporary or limited-purpose applications

At times, enterprises develop applications for temporary use. Such temporary apps are to experiment with new services or for short-term business needs such as events. Such apps need high performance and capacity for a limited period. But provisioning large-scale infrastructure for temporary use is costly. Instead, a cloud-based in-memory computing platform suits the needs of such apps. The IT admins may scale up the data grid or stream processing engine capacity for peak demand scenarios and get the job done. 

Use-cases for full production cloud environment

Enterprises committed to the cloud find in-memory solutions handy for a wide range of post-migration options. Lightweight and embedded in-memory solutions suit edge and fog computing. It also syncs well with IoT and remote device deployments. For instance, it speeds up edge device processing and removes the strain on the back-end infrastructure.

Cloud-based in-memory solutions enable enterprises to achieve their goals quickly and with comparatively lower costs. A good solution supports both simple and complex cloud implementations and ensures enterprises get the speed, scalability and stability offered by the cloud. It supports enterprise IT in making systems agile for digital transformation.