The adoption of in-memory computing platforms has steadily increased over the past decade, growing in popularity as traditional disk-based databases could no longer provide the performance and scalability businesses needed to collect and analyze soaring volumes of business data. In-memory computing platforms, built using a cluster of servers, pool the available RAM and CPU power, enabling data and processing to be distributed across the cluster for blazing fast performance and massive scalability to enable real-time business processes.
Some industries, such as financial services, have embraced in-memory computing for decades. Others, such as food delivery, e-commerce, logistics, and remote access and collaboration services, did so throughout 2020 as companies faced COVID-19-fueled surges in website visitors, delivery requests, sales transactions, video streaming and more. In-memory computing platforms enabled these businesses to rapidly scale to support these dramatic usage increases.
A surge in in-memory computing adoption by businesses for a growing number of use cases has led to an increase in in-memory computing practitioners. This in turn has led to greater adoption of in-memory computing technology and the acceleration of innovation for in-memory computing platforms overall.
As we continue through 2021 and in-memory computing platforms mature, we will see the number of industries and companies adopting these solutions continue to grow. This trend will last far beyond the effects of the pandemic as companies shift their focus to meeting new strategic and competitive demands driven by digital transformation initiatives. Increasing adoption of in-memory computing will also be accompanied by the following key trends powered by the technology’s real-time capabilities and massive scalability.
Rapidly Improving Application Performance and Scalability
First, watch for in-memory data grids (IMDGs) to become the solution of choice for more and more businesses that need an easily implementable approach to maintaining or improving performance while increasing application throughput.
Because IMDGs cache application data in RAM and apply massively parallel processing (MPP) across a distributed cluster of server nodes, they provide a simple and cost-effective path to dramatically improving application performance. An IMDG is a component of an in-memory computing platform. An IMDG can be inserted between an existing application and disk-based database without major changes to either layer. The distributed architecture also enables rapidly scaleout of applications by allowing the RAM and compute power of the cluster to be increased simply by adding new server nodes. The platform automatically recognizes new nodes added to the cluster and redistributes data and compute across them.
Digital Integration Hubs
The next trend to watch is the rise of the IMDG-powered digital integration hub (DIH). Digital integration hubs are rapidly increasing in popularity because of their ability to power one of the hallmarks of digital transformations: real-time business processes that require access to multiple data sources to create 360-degree views of customers and the business. The DIH leverages an IMDG as a high-performance data access layer for aggregating a subset of data from multiple source systems. This data can then be simultaneously accessed by any number of business applications through a variety of common APIs such as SQL or REST at in-memory speeds. Synchronization between the IMDG and data sources which can include relational and NoSQL databases, data warehouses, data lakes, cloud-based applications, and streaming data is continuous to ensure the data in the in-memory cache is always current as changes are made to the underlying datastores. This makes the DIH ideal for launching a variety of critical real-time initiatives, from recommendation engines to automated business decision making to real-time upsell and cross-sell offers.
The last big in-memory computing-powered trend to watch in 2021 is the accelerating adoption of hybrid transactional/analytical processing (HTAP).
HTAP enables businesses to perform real-time analytics directly on their transactional or operational data, eliminating the need for a time-consuming ETL: process to move operational data to a separate datastore. In this way, HTAP enables real-time digital business across a range of data-intensive industries, from financial services and e-commerce to healthcare, transportation, and logistics.
Increasing HTAP adoption is also tied to soaring demand to roll out applications that can update their machine learning models in real-time. Providing what Gartner calls “in-process HTAP,” an in-memory computing-powered data grid can enable a continuous learning infrastructure in which the machine learning (ML) model is updated in real time based on new operational data flowing into the system.
Driving Real-Time Processes
In-memory computing has solidified its place as the most flexible and cost-effective strategy for achieving achieving real-time business processes using all of the relevant data in an organization’s systems As a result, we can expect to see in-memory computing adoption continue to accelerate for years to come as companies rely on it to achieve ever-greater levels of application speed and scalability as well as real-time data access across all datastores.