Memory Centric Architectures

Data processing is the core of most businesses migrating to digital platforms by developing enterprise systems or consumer-facing solutions. Analytical Business Intelligence also increases the demand for high-end data processing methods. Any solution being implemented by companies pushing the digital transformation agenda should be reliable and cost-effective. 

Unfortunately, for a long time, data processing systems were expensive or did not have the operational capacity to query databases swiftly without compromising system functionality. Fortunately, memory-centric architectures started being widespread, allowing companies to improve performance and reduce expenses. How do memory-centric architectures make data processing cost-effective and more reliable?

Most common memory-centric architecture

There are mainly two types of memory-centric architecture, and their main differences lie within the functionality of each one. First up, there is memory-rich accelerator architecture primarily hosting data as on-die memory. The memory-rich architecture has large-capacity on-die memory and can be integrated with advanced computing systems. Computing-based memory-centric architectures primarily focus on providing high-end computing power using transactional data. 

The in-memory computing architecture leverages higher bandwidth rates while significantly reducing data transfer overheads. In-memory computing systems are flexible and can be used for advanced data structures or management processes that can’t be carried out with traditional processing architecture. 

Between these two memory-centric architectures, in-memory is more popular and used by enterprise system developers due to its appealing characteristics. Memory-rich architecture is also widely used, but it has some bottlenecks compared with in-memory computing. Subsequently, in-memory computing trumps memory-rich architecture and other traditional data processing systems.

Benefits when compared with disk-based databases

For a long time, disk-based databases were used to process insights quickly and effectively. Unfortunately, disk-based databases pose a wide variety of challenges in today’s digital transformation landscape. The first concern enterprise system developers have is the affordability of disk-based solutions. 

RAM can be very expensive on legacy data processing systems, and the overheads of transmitting data quickly can mount up to a steep price. Therefore, using disk-based databases is not an economical choice for most organizations.

In-memory computing in the form of SaaS or IaaS can resolve this issue. Cloud-based in-memory computing solutions are cost-effective and do not require high overhead costs. 

At the same time, some offer pay-as-you-use services, further reducing data processing expenses. Another benefit of in-memory computing is its scalability and high availability, ultimately improving overall system performance.

These two benefits are just the beginning because there’s much more to it. When comparing in-memory computing with disk-based databases, enterprise system developers can enjoy more advantages by migrating to memory-centric architecture.


Part of the challenges enterprise systems face when processing data from disparate sources is the ETL process. Most data processing systems Extract, Transform and Load insights to enterprise systems. However, this causes delays and latency issues since there are more steps to processing the data. As a result, enterprise systems take longer to display queried insights to front-end interfaces or dashboards. 

In-memory computing uses a different approach called Hybrid Transactional/Analytical Processing (HTAP). Unlike the ETL process, HTAP can serve multiple clients at the same time, allowing more than one enterprise system to use the processed data. 

Since the data is gathered into one centralized database, the applications do not provide query insights directly from disparate sources. The high-availability HTAP system has led to the boom in crypto trading since these platforms use high-end AI technology to provide analytical insights and facilitate client-side data requests.

Consolidating several systems into one solution

Memory-centric architectures improve enterprise system performance by consolidating several data sources and centralizing the gathered insights. As a result, the applications using that centralized data source reduce latency since the data is highly available.

At the same time, in-memory computing allows enterprise system developers to integrate multiple applications and analytical dashboards into one database. 

Generally, integrating multiple systems into a single database could cause stability issues. With in-memory computing, this is not an issue because of the underlying architecture.

Developers can safely integrate multiple applications and glean real-time insights for their dashboards effectively. More especially, memory-centric architecture is flexible and can be scaled without disturbing current application functionality. 

Organizations can benefit from scalable and highly available data processing architecture by implementing in-memory computing. The client-side applications will significantly minimize system downtime and reduce any problems caused by latency.

Supporting digital transformation

In-memory computing supports digital transformation to a great extent since it simplifies data processing for enterprise applications. The very architecture it uses is permissive for rapid digital transformation within a company. By centralizing disparate data sources, it is more convenient to develop high-end enterprise applications. 

Additionally, with benefits such as reduced latency, applications developed using in-memory computing are competitive in the respective markets they are placed in. Since in-memory computing uses HTAP instead of ETL, developing highly-available apps with access to real-time insights is a reality. With reduced latency and high availability, enterprise systems can perform better and conserve data processing expenses. 

These benefits contribute to a seamless migration to newer digital solutions cloud-based vendors provide. As time goes on, in-memory computing greatly supports digital transformation and allows companies to leverage the latest technologies to streamline revenue while saving expenses.

Real-life use cases

Memory-centric architecture has multiple use cases in the business world. Due to its benefits and functionalities, memory-centric computing processes are mostly used for real-time business intelligence insights. Instead of relying on databases with latency issues, enterprises can use real-time insights from in-memory computing. 

Traditional data processing systems could alter with agile decisions that require real-time insights. In addition to using memory-centric architecture for Business Intelligence, this data processing system simplifies enterprise application management.

Instead of querying insights directly from disparate databases, fintech solutions like banking apps and investment applications can use centralized data on in-memory computing solutions. 

There are dozens of other real-life use cases for memory-centric databases. Most of them hinge on the fact that in-memory computing solutions can provide insights to multiple enterprise systems at a time. This capability makes it easier for companies with more than one software product to use a centralized data source than querying databases independently.

Rizwan is an avid mobile geek and a gaming lover. He loves to keep a tab on new tech and loves to share the latest tech news and reviews on Smartphones, Gadgets, Apps, and more.


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