In Memory Computing Market Overview:

The global in-memory computing market is likely to grow steadily in the future. The increasing adoption of in-memory computing platforms across various verticals to drive speed, performance, and scalability would accelerate the market value. Market Research Future (MRFR) asserts that the global in-memory computing market is projected to reach USD 11.12 BN by 2026, growing at a 4.22% CAGR during the review period (2022-2030). 

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Top Key Players of the In-Memory Computing Market:

Leading players in the in-memory computing market include,

• Oracle (US)
• Microsoft (US)
• SAP (Germany)
• SAS Institute (US)
• IBM (US)
• TIBCO (US)
• Fujitsu (Japan)
• Software AG (Germany)
• Red Hat (US)
• GigaSpaces (US)
• Altibase (US)
• GridGain (US)
• MongoDB (US)
• Hazelcast (US)
• Exasol (Germany)
• Qlik (US)
• Intel (Germany)
• Salesforce (US)
• Teradata (US)
• Workday (US)
• Kognitio (UK)
• VoltDB (US)
• Enea (Sweden)
• MemSQL (US)
• McObject (US)

Besides, the increasing demand for faster processing and analytics on big data at a lower overall cost of RAM and TCO substantiates market revenues. The increasing use of the Internet and mobile banking services has ensured large data processing demand. In-memory computing solutions have proven effective in low response time in mobile advertising networks and telecommunications network equipment sectors.

The market will likely witness substantial demand as enterprises undergo digital transformation, such as embracing and leveraging various new data types and forms. The increasing adoption of in-memory computing technology in various verticals such as aerospace & defense, BFSI, manufacturing, media & entertainment, healthcare, government, retail, and IT & telecommunication fosters market revenues.

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In-Memory Computing Market – Segments

The market is segmented into component, application, deployment mode, organization size, vertical, and region. The component segment is sub-segmented into solutions {in-memory database (IMDB)( online analytical processing (OLAP) and online transaction processing (OLTP), data stream processing, and in-memory data grid (IMDG)} and services (professional services (consulting, system integration & implementation, support, and maintenance) and managed services).

The application segment is sub-segmented into risk management & fraud detection, geospatial/GIS processing, sentiment analysis, sales & marketing optimization, predictive analysis, supply chain management, and others (image processing, route optimization, claim processing & modeling, and trade promotion simulations). 

The deployment mode segment is sub-segmented into cloud and on-premises. The organization size segment is sub-segmented into SMEs and large enterprises. The vertical segment is sub-segmented into IT & telecom, BFSI, retail & e-commerce, transportation & logistics, healthcare & life sciences, government & defense, media & entertainment, energy & utilities, and others (education, manufacturing, and travel & hospitality).

Global In-Memory Computing Market – Regional Analysis

North America dominates the global in-memory computing market. The growing demand for analytics and advanced analytics platforms boosts the market size. The presence of major solution providers, such as Oracle, Microsoft, IBM, and Hazelcast, positively impacts the region’s in-memory computing market share. The North American in-memory computing market will remain dominant throughout the forecast period. 

In-Memory Computing Market – Competitive Analysis 

Highly competitive, the in-memory computing market appears fragmented due to several well-established players forming a competitive landscape. To gain a larger competitive share in this market, industry players incorporate strategic initiatives such as mergers & acquisitions, collaborations, expansions, and product/ technology launches.

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Industry/ Innovation/ Related News 

Aug.27, 2023 — Researchers at IBM Research Europe announced the development of a new 64-core mixed-signal in-memory computing chip. Based on phase-change memory devices that could better support the computations of deep neural networks, the new 64-core mixed-signal in-memory computing chip attains highly promising results, retaining the accuracy of deep learning algorithms while reducing computation times and energy consumption.  

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