Through the years, businesses have been gradually opening up to the possibility of implementing in-memory computing into their systems. The quick and simple access to data provided by the platform allows organizations in a variety of industries to make informed decisions through data analytics.
Consequently, in-memory computing enables increasing efficiency in different aspects of the business by transforming large amounts of data into comprehensible and actionable insights. The power of the platform lies in its use of RAM instead of disk to store and process data. This does away with the bottlenecks caused by the constant movement of data between memory and disk-based storage.
For small businesses, in-memory computing solutions like in-memory data grids are a viable solution because it provides high data processing speeds without the need for the latest and greatest hardware. Gartner estimates that the in-memory computing market will drive spending to over $15 billion by 2022. If you’re looking to push your business to new heights, it’s a good time to invest in the platform that will not only help you process and analyze data at scale but also act as a driver of digital transformation.
The Rise of In-memory Computing
In-memory computing (IMC) naturally came about due to traditional solutions being inadequate in handling the constantly growing amounts of data. Disk-based storage caused bottlenecks that weren’t resolved even if the fastest available hard disks were used. This also led to an unsustainable scaling cycle that required upgrading periodically to more powerful hardware and software to maintain peak performance over time.
As quantities of data continue to grow, reducing latency and access times becomes more important. In-memory computing presents a viable solution for small businesses looking to leverage technology to digitize their business or upgrade their current systems.
In-memory computing uses a data grid to distribute data across a horizontally scalable architecture. By using in-memory data stores, this platform is able to handle mixed workloads within the same architecture without separating transactional databases for analytics databases, reducing the main cause of disk slowdowns. It also uses RAM instead of disk and features parallel data processing to provide real-time data, empowering small businesses to make informed decisions and data-driven predictions.
The high cost of RAM used to be the main deterrent for small businesses in adopting in-memory computing, but this has gradually declined through the years, and this downward trend in cost is expected to continue. Horizontal scalability and computing elasticity also makes in-memory solutions a cost-effective option.
An in-memory data grid, for example, runs specialized software on each computer within a network to enable the pooling of RAM and computing power to ensure the fastest possible application performance. This implementation enables high speed data processing for large-scale applications that usually require a large amount of RAM to run.
Running special software on the networked computers means they are not dependent on disk and data movement within the network is minimized. Despite pooling RAM and sharing data, each computer still retains its own data structures, which are tracked at the individual level to make data sharing seamless whether they’re being shared between applications or different nodes.
Is Your Business Ready for In-memory Computing?
The increasing demand for in-memory computing and data analytics is borne out of the need for businesses to adapt to a data-driven landscape. The businesses of today demand applications and computing solutions that can and will process large quantities of data in seconds. One of the main draws of in-memory computing is its empowering of marketers and other business units to get insights from data without being too dependent on their IT teams.
Ultimately, the goal of a computing platform should be self-service, and in-memory computing provides this benefit to help small businesses save time and money while getting the best possible platform that can provide the best possible business outcomes. In-memory computing also provides other benefits, which include the following:
- Visualized big data
It can be challenging to keep up with the large amounts of data gathered each day, especially when it comes to filtering it and determining what could be of value to the business. Using data visualization tools allows users to “see” data in graphs or tables that can help them get insights on certain aspects of the business. Getting real-time insights allows a business to discover opportunities and determine next steps. - Integration of data from various sources
Business gathers data from a multitude of sources and, often, simply keeping up with where certain types of data came from can be a job in itself. In-memory computing solutions like in-memory data grids provide a centralized dashboard where users can interact with data so they can generate data-driven forecasts and historical reports. - Real-time data for actionable insights
By keeping data “fresh” and empowering small businesses to process data in the stream, in-memory computing provides access to the latest and freshest data once they are available. Big data is fast moving and changes often so the ability to get real-time data allows organizations to make decisions immediately instead of a few days after data is processed.
Conclusion
Big data may be complex, but it’s something that small businesses must learn to wield because having data work for you will provide the biggest gains. Fortunately, in-memory computing implementations can provide a streamlined data-processing approach that will help businesses focus on their bottom line. It’s also becoming easier to integrate it into your current systems, so it’s worth considering if you’re looking to scale your business anytime soon.
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