In-Memory Data Grid Market Projected to Discern Stable Expansion by 2030

In-memory data grid (IMDG) is specifically designed to offer high scalability & availability by allocating data across multiple sources. In-memory data grid (IMDG) is also designed to build as well as run large-scale applications that usually require more random-access memory (RAM) than is presented in a computer server. IMDG empowers better application performance by deploying RAM and the processing power of numerous computers that can easily run tasks. IMDG is valuable for large-scale applications that perform wide-ranging equivalent processing on large data groups.
The increase in demand for social media, cloud, and Internet of Things (IoT) has significantly created demand for applications that require to be tremendously capable and fast to process huge amount of data per second.

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Impact of Coronavirus (COVID-19) on the In-Memory Data Grid Market

Lockdowns across major regions (Europe, Asia, and North America) due to an increase in the spread of Coronavirus (COVID-19) has a positive impact on the in-memory data grid market. During the period of Coronavirus (COVID-19), people have majorly shifted to online content that provides huge data distribution from one source to another, which is expected to positively influence the growth of the in-memory data grid market.

Increasing Demand for Fraud & Risk Management Expected to Drive the Global In-Memory Data Grid Market

Enterprises generally use fraud & risk management solutions to improve their risk intelligence abilities to overcome upcoming risk exposures. Fraud & risk management is now becoming a major focus for enterprises worldwide. It is progressively becoming significant for several enterprises to provide timely and accurate risk reporting to regulatory or government agencies. Adopting in-memory data grid (IMDG) helps to perform essential analytics, unify business risk data, and report comparatively easily as compared to the traditional approaches toward data management.

Therefore, financial organizations worldwide are majorly looking for in-memory data grid (IMDG) solutions that can easily process real-time data and enhance their business-critical applications performance more efficiently.
The impact of this driver is expected to be high throughout the forecast period. Use of in-memory data grids is on the rise across the globe, led by increasing demand for fraud and risk management. This is expected to boost the global in-memory data grid market in the next few years.

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Availability of substitute such as In-Memory Databases (IMDB) Expected to Hamper the In-Memory Data Grid Market

Similar technology such as in-memory databases (IMDB) also empowers high-speed applications. IMDG are suitable when data is recurrently accessed & modified, whereas IMDB is specifically designed for applications that are more focused on storage, where simple calculations are processed across large data subsets.
Therefore, availability of substitutes such as in-memory databases (IMDB) is expected to hamper the in-memory data grid market.

North America to Account for Major Share of the Global In-Memory Data Grid Market

In terms of region, the global in-memory data grid market can be segmented into North America, Europe, Asia Pacific, South America, and Middle East & Africa

North America is anticipated to account for major share of the global in-memory data grid market during the forecast period due to increasing regulatory compliances among organizations to boost the adoption of in-memory data grids across enterprises, indicating potential growth of the market. Europe is expected to be the second largest market for in-memory data grids from 2020 to 2030.

Asia Pacific is projected to be a rapidly growing market for in-memory data grids during the forecast period due to growing focus of enterprises to efficiently handle large data volumes, specifically in India, Japan, and China.

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Key Players Operating in the Global In-Memory Data Grid Market Include:

Apache Ignite
GigaSpaces
GridGain Systems, Inc.
Hazelcast, Inc.
IBM Corporation
Oracle Corporation
Parallel Universe
Red Hat, Inc.
ScaleOut Software
Software AG
TIBCO Software Inc.

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