As businesses struggle to keep up with and leverage Big Data, they are starting to turn to in-memory computing (IMC). IMC provides a different way to store data and to process it.
Growing demand is boosting constant innovations in IMC. As IMC keeps improving and becoming more affordable, companies are seeing it as a way to deal with speed and scale challenges.
A paradigm shift away from past limitations
With disk-first architecture, memory was used sparingly to cache small data amounts for quick access. The limitations of this were apparent long ago. With data in storage that had to be accessed when needed and acted upon in the computer’s memory, a bottleneck exists that reduced speed, even when using the fastest hard drives. As data volumes increased, so did time needed to access it, let alone analyze it.
In-memory computing made use of increased affordability of memory to replace traditional storage methods. As prices of memory fell, it became easier to integrate in-memory data grids with existing layers of data and applications.
When data is stored in the computer’s memory, it is readily available and can be accessed almost instantaneously. In-memory speed, high availability, and scalability are all possible.
New in-memory databases were separately developed that would be able to replace existing disk-based technology.
Memory-centric architecture provides technology to support the use of other memory and storage types, such as solid-state drives and flash memory. Using memory-centric architecture, the data set can exceed the amount of RAM.
The ability to exceed the amount of memory allows data to be optimized so all the data is on disk but higher value and demand data also resides in-memory. Low value and demand data is only on the disk.
Another advantage of memory-centric architecture is it does away with the need for all data to be reloaded into RAM when rebooting. Data can be processed from the disk while the system warms up and memory is reloaded, enabling a quick recovery.
Memory-centric architecture provides companies with more control and flexibility to balance cost and performance. Using this strategy can optimize performance and minimize the costs of infrastructure.
Advantages of IMC platforms
In-memory computing is solving massive scalability and real-time speed requirements resulting from digitalization, demands for real-time regulatory compliance and omnichannel marketing.
IMC platforms bring together various components, such as streaming analytics, in-memory data-grids, and in-memory databases into a unified platform that reduces development and operational costs. These platforms allow organizations to analyze huge quantities of business data from a variety of sources, as and when it is received and needed.
One of the biggest advantages is speed. Without the problem of having to access data in storage, organizations can speedily analyze information and use it to formulate new strategies. Complex queries can be performed in minutes, rather than having to analyze information that may already be out of date.
Organizations can also examine whole sets of data, rather than just representative samples. Decisions can be made by examining all the facts.
Memory-centric architecture offers the speed and scalability benefits of in-memory computing but with improved economics. It can balance performance and costs, allow for availability in the face of growth, and accelerate recovery when a system crashes. It offers companies a cost-effective way to move forward to an in-memory future.
How IMC platforms are being used
In-memory computing is already being used by many organizations for various purposes, from analyzing gene sequences to maximizing sales. Some of the industries in which in-memory computer platforms are being used are financial services, SaaS, retail, healthcare, IoT software, and more.
One use of an IMC platform is to process millions of financial transactions every day. Some other uses are:
- Medical imaging processing.
- Real-time machine learning.
- Complex event processing of streaming sensor data.
- Natural language processing and cognitive computing.
- Real-time sentiment analysis.
- Insurance claim processing and modeling.
- Geospatial/GIS processing.
Best use cases are not so much defined by a specific industry, however, but by a need for the best scalability and performance for a specific task. IMC is an enabling technology, and it’s bringing a wave of innovation with new ideas that are now attainable. It’s moving beyond traditional computing that can’t keep up with Big Data.
Today streams of information are utilized to inform us, protect us, make us healthier and give us richer lives. The technology to support this is in-memory computing.
In-memory computing is powerful but it needs to be used to the correct end, or it’s worse than useless. The move to in-memory computing may be inevitable, but it’s not likely to be straightforward.
Organizations need to become informed before making any decisions about how to proceed. These decisions may involve the immediate or future use of in-memory computing, in-house or via the cloud, etc.
Transformations to expect over the next decade
Comprehensive in-memory computing platforms will become the systems of record. Non-volatile memory (NVM) will be the preferred storage method, and hybrid models will be used for storage of very large datasets. First class support will exist for in-memory SQL, and there’ll be growing artificial intelligence use cases for IMC.
The future is bright for in-memory computing as businesses continue to explore ways to cope with vast quantities of data and users who expect real-time performance. If an organization is large enough and collects plenty of data, it will need to adopt in-memory computing at some point to continue to function efficiently. Smaller organizations may feel that the costs of in-memory computing outweigh the benefits. For them, memory-centric architecture may provide a way to move forward in a cost-effective manner.