The Impact Of In-Memory Computing On Software Performance

Technological evolution has been steady through the years, and the focus has been on size, speed and performance. The basic principles have remained the same, but the software and hardware industries haven’t stopped making improvements that continue to make the argument for technology in the fields of business, marketing, and other industries. The name of the game is big data and winning the game means employing tools that will help turn big data into insights that can push your business forward.

In-memory computing is gradually becoming a norm for businesses that require the power to quickly process data and require fast data processing/real-time analytics that help transform big data into actionable insights. With data and data sources continuing to increase, there’s been a thrust to rethink how businesses approach data handling and storage, with one of the main objectives being maximized performance and speed without increasing complexity and overall costs.

From Traditional To Revolutionary

In-memory computing, unlike traditional solutions, is capable of storing and handling both the data and the application itself in memory. Because the system is based on RAM data storage and indexing, data processing and querying is 100 times faster compared to any other solution. In-memory computing systems also require minimal to no performance tuning and maintenance,  providing a much faster and stable experience for end users. The main draw of the platform is the ability to analyze data in real-time and the easy scalability of its most common implementation, the in-memory data grid. Although different from in-memory computing, the in-memory data grid is a key technology enabler for the platform. Compared to business intelligence tools that can take as long as a year or more to deploy, in-memory solutions can speed up implementation and make scaling easier and more cost-effective. Business intelligence tools have their place in business, but without proper data processing, storage, and management, organizations will struggle to come up with effective business intelligence strategies.

Using the main memory instead of disk improves performance because latency is minimized. By doing away with the need to constantly access disk storage, the movement of data to and from disk within the network is reduced, thereby preventing bottlenecks that could hinder optimal performance. In-memory computing is different from traditional systems because it doesn't need to store redundant data to increase performance. Older or more conventional systems create a copy of the data for each component added to the system, like additional databases, middleware, or servers. This process presents sustainability issues and limits scalability because it leads to a system that becomes more complicated through time. With traditional systems, continuously adding hardware leads to constantly ballooning hardware costs, need for larger storage to store growing amounts of data, and continuous work on integration and maintenance.

Ultimately, this is a self-defeating approach because, as you add more hardware to improve performance, more copies of the data are created and more instances require this data to travel, resulting in a decrease in performance. In the long run, it may seem like you're trapped in a cycle of growing hardware and increased cost. In-memory computing addresses this by storing data in-memory to enable single data transfer and fast data processing that can be achieved using only a single server. It's agile, streamlined, and optimized to consume less memory and require less CPU cycles.

From Revolutionary To Mainstream

In-memory computing was an expensive alternative, and it's one of the main reasons adoption has been slow. RAM has always been more expensive than disk, but with costs gradually decreasing and reaching more reasonable levels business-wise, in-memory computing is becoming a viable solution that can help boost revenue and aid achieving a complete digital transformation even for small businesses. Also, traditional solutions simply can't keep up with today's data processing and business intelligence requirements.

The businesses of today require super-fast computing solutions and real-time data scalability if they want to be the businesses of tomorrow. Relying on traditional approaches, like relational databases that use SQL and rely on disk-based storage, isn't enough. Your competitive edge over competitors will depend on you using the appropriate platform or implementation for your business. Smaller businesses might still feel burdened by the costs of high-performing in-memory solutions, but this problem will eventually go away as the platform matures and becomes more mainstream. Fortunately, in-memory computing shows great promise of becoming the norm in the coming years. Businesses will however, need to adapt fast or die slow as we move to a more data-driven landscape.