In-memory Data Grid Market Introduction
An in-memory data grid is a data structure that is distributed across multiple servers of a system, however resides only in its RAM (random access memory). In-memory data grids are used in various data processes to enhance data processing systems of a business. The primary role that an in-memory data grid plays in a system is to avoid data processing delays associated with the traditional input/output bottlenecks in relational databases of a system, for which most developers make use of object-oriented designs.
The needs for external storage and electromechanical mass storage media have been obviated by the technological advancements in multicore, 64-bit systems that enable storing terabytes of data in RAM. Thereby, with the use of an in-memory data grid, end-users are aiming to enhanced performance of RAM by improving the speed of processing data on it. Thereby, a distributed cluster with an in-memory data grid residing in it can ensure both capacity of the system to access and process data by using scalability features provided by a cluster and speed, by storing data in-memory/in RAM.
Vendors in the in-memory data grid market are aiming to capitalize on sales opportunities by focusing on its applications in e-commerce, financial-instrument pricing in banks, cloud applications reservation systems in the transportation industry, and user-preference calculations in various web applications.
In-memory Data Grid Market – Notable Developments
- In May 2018, Red Hat Inc. – an American multinational software company in the in-memory data grid market – entered a strategic partnership with another American multinational information technology company in the in-memory data grid market – IBM (International Business Machines Corporation). With the collaboration, the company aims to integrate services and technologies to focus on growth opportunities in a hybrid cloud sector. Both the companies also declared their mutual goals to offer their customers the combined benefits of technologies deployed by both, Red Hat and IBM, on private as well as public cloud.
- In January 2019, GridGain Systems – a privately held, U.S.-based information technology company in the in-memory data grid market – announced the integration of automatic disk-based data persistence with GridGain Cloud, its in-memory-computing-platform-as-a-service (imcPaaS) solution. The new feature of GridGain’s in-memory data grid ensures immediate data access whenever there is no option to a cluster restart. The company is continually upgrading the consisting designs of its in-memory data grids to offers more scalability and speed through such initiatives that involve artificial intelligence and digital transformations.
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- In March 2018, Oracle Corporation – an American multinational computer technology corporation in the in-memory data grid market – announced that it has opened a state-of-the-art cloud campus with a 560,000-square-foot office building in Austin, Texas. With this expansion of its capabilities, the company is aiming to strengthen its position in the market by bolstering developments of next-generation technologies, such as machine learning and Artificial Intelligence (AI). In the new cloud campus, Oracle will provide all the necessary resources for the development of cloud technologies, which will ultimately help the company to expand its business operations in the in-memory data grid market.
Some of the most prominent competitors operating in the competitive landscape of global In-memory data grid market include –
- Red Hat
- Software AG
- Oracle Alachisoft
- ScaleOut Software
In-memory Data Grid Market Dynamics
Growing Needs for Attaining Unprecedented Levels of Speed at Data Processing Drive the Market
Modern organizations across various industrial sectors are adopting Information Technology (IT) services to enhance their business operations by improving the digital quotient of their business. With the recent advancements in technologies, end-users are expecting unprecedented levels of speed and scalability at accessing and processing data. Furthermore, in-memory data grids offer extraordinary benefits over traditional disk-based data storage systems as, due to their high access latency, hard discs do not offer timely response. Thereby, taking into consideration the increasing needs for eliminating the need for relational database and improving the data storage and processing capability of RAM with a distributed architecture is expected to boost growth of the in-memory data grid market.
Vendors in the In-memory Data Grid Market to Capitalize on Lucrative Opportunities in the BFSI Sector
Financial organizations around the world have to deal with sensitive information involving critical financial transactions. As any error in the data access and processing systems can lead to severe implications in terms for financial as well as ethical problems. Thereby, financial organizations, including Banking, Financial Services, and Insurance (BFSI) institutions are inclined towards growing digitally. Thereby, adoption of in-memory data grids is likely to increase in the BFSI sector with an ultimate aim of building a flexible, lean, and efficient data processing system.
In-memory Data Grid Market Segmentation
Based on the components, the in-memory data grid market is segmented into,
- Support and Maintenance
Based on the deployment types, the in-memory grid market is segmented into,
Based on the size of the organization, the in-memory data grid market is segmented into,
- Large Enterprises
- Small- and Medium-sized Enterprises (SMEs)
Based on its applications, the in-memory data grid market is segmented into,
- Transaction Processing
- Supply Chain Optimization
- Fraud and Risk Management
- Sales and Marketing Optimization
Based on end-use industries, the in-memory data grid market is segmented into,
- Banking, Financial Services, and Insurance (BFSI)
- Transportation and Logistics
- Media and Entertainment
- Telecommunications and Information Technology
- Healthcare and Life Sciences
- Consumer Goods and Retail
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This post was originally published on Analytics News