In-Memory Computing Market Research Report Information By Component (Solutions (In-Memory Database, (Online Analytical Processing, Online Transaction Processing), In-Memory Data Grid and Data Stream Processing), Services (Professional Services (Consulting, System Integration and Implementation, Support, Maintenance), Managed Services)), Application (Risk Management and Fraud Detection, Sentiment Analysis, Geospatial/GIS Processing, Sales and Marketing Optimization, Predictive Analysis, Supply Chain Management, Regions - Forecast till 2030

In Memory Computing Market Overview

The global in-memory computing market expected to reach USD 11.12 Billion and is poised to exhibit 4.22% CAGR from 2022 to 2030.

In-memory computing is a type of purpose-built database that stores data in RAM rather than in databases hosted on disks. IMC provides super-fast performance, which aids businesses to enhance performance, quickly analyze huge volumes of data in real-time at very high speeds, and detect patterns. It provides real-time insights that enable businesses to deliver faster reporting and immediate actions and responses.

Various factors are driving the growth of the in-memory computing market. These factors include the increasing demand for faster processing and analytics on big data and a decrease in the overall cost of RAM and TCO.  An increase in the use of internet services and mobile banking has ensued in demand for large data processing. Moreover, an increase in adopting in-memory computing platforms across various verticals results in high speed, performance enhancement, and scalability. However, the volatility of data and the concerns regarding the security of the data are factors, which hamper the growth of the in memory computing market.

The in-memory computing market is substantially influenced due to the increasing pandemic situation of COVID-19 across the world. The COVID-19 pandemic has had a major impact on the global economy. The in-memory computing market is anticipated to grow significantly due to the increasing need for rapid data processing and the explosion of big data across various verticals. This scenario has showcased a strong demand for in-memory computing technology in the global market. The increasing demand for in-memory computing technology in several verticals, including BFSI, IT and telecom, retail & e-commerce, healthcare & life sciences, transportation & logistics, government & defense, energy & utilities, media & entertainment, and manufacturing.  These factors have created an upsurge in demand for in-memory computing during the COVID-19 pandemic, which has helped boost the revenues of in-memory computing companies operating in the global market. Additionally, the top players in the in-memory computing market, such as Microsoft, Oracle, SAP, IBM, and SAS Institute, are focused on developing new business models and strategies to meet consumer demand in the global market.

Segmentative Analysis

Global In-Memory Computing Market has been segmented based on Component, Application, Deployment Mode, Organization Size, Vertical, and Region.

Based on the Component, the in-memory computing market has been segmented into solutions and services. The solution segment is further divided into in-memory database (IMDB), in-memory data grid (IMDG), and data stream processing. The in-memory database (IMDB) segment is further sub-classified into online analytical processing (OLAP) and online transaction processing (OLTP). The service segment is divided into professional services and managed services. The professional services segment is further sub-segmented into consulting, system integration and implementation, support, and maintenance.

Based on Application, the in memory computing market has been segmented into risk management and fraud detection, sentiment analysis, geospatial/GIS processing, sales and marketing optimization, predictive analysis, supply chain management, others. The other segment is sub-segmented into image processing, route optimization, claim processing and modeling, and trade promotion simulations.

Based on Deployment Mode, the in-memory computing market has been segmented into cloud and on-premises.

Based on Organization Size, the in-memory computing market has been segmented into SMEs and large enterprises.

Based on Vertical, the in-memory computing market has been segmented into BFSI, IT & telecom, retail & e-commerce, healthcare & life sciences, transportation & logistics, government & defense, energy & utilities, media & entertainment, and others. The other segment is sub-segmented into education, manufacturing, and travel & hospitality.

Regional Analysis

Based on region, the In-Memory Computing Market is segmented into Asia-Pacific, North America, Europe, the Middle East & Africa, and South America.

North America held the largest share of the in-memory computing market, followed by Asia- Pacific and Europe; it is expected to continue to retain its dominance until the end of the forecast period. In North America, growing demand for analytics and advanced analytics platforms by small and medium businesses and government agencies would drive the demand for IMC products. The presence of major solution providers in the regional market such as Microsoft, Oracle, IBM, SAS Institute, TIBCO, Red Hat, Altibase, GigaSpaces, GridGain, Hazelcast, MongoDB, Qlik, Salesforce, Workday, Teradata, VoltDB, McObject, and MemSQL is also contributing to the growth of the North American market. However, Asia-Pacific accounts for the fastest-growing region as it registers the highest CAGR.

Companies Covered

The Key Players of the Global In-Memory Computing Market are Microsoft (US), Oracle (US), SAP (Germany), IBM (US), SAS Institute (US), TIBCO (US), Software AG (Germany), Fujitsu (Japan), Red Hat (US), Altibase (US), GigaSpaces (US), GridGain (US), Hazelcast (US), MongoDB (US), Exasol (Germany), Intel (Germany), Qlik (US), Salesforce (US), Workday (US), Teradata (US), Kognitio (UK), Enea (Sweden), VoltDB (US), McObject (US), and MemSQL (US).

Key Developments

January 2024 – CXL: an advanced interconnect technology enabling high-bandwidth, low-latency connections between host processors such as CPUs with accelerators or memory buffers, making it possible to tackle the “memory wall” problem, thus improving heterogeneous computing. CXL is based on PCIe interfaces but can expand main memory beyond DIMM slots, thereby narrowing the latency gap between main memory and SSD storage. It plays an important role in AI applications by providing larger memory capacity and bandwidth, which contributes to high-performance computing.

August 2022 – Stanford engineers present a new chip that ramps up AI computing efficiency. They produced a more efficient and flexible AI chip at Stanford University that could take artificial intelligence to tiny edge devices.

On January 13, 2021, Oracle announced that Oracle Database 21c, the latest edition of the world’s most popular converged database, is now available on Oracle Cloud, including the Always Free tier of Oracle Autonomous Database. Over 200 brand-new innovations are included in Oracle Database 21c, such as immutable blockchain tables, In-Database JavaScript, native JSON binary data type, AutoML for in-database machine learning (ML), and persistent memory store along with improvements for in-memory, graph processing performance, sharding, multitenant and security.

July 2019 - Intel, together with SAP, revealed a multi-year technology partnership targeting the optimization of Intel platforms, including Intel Xeon Scalable processors and Intel Optane DC persistent memory for end-to-end mission-critical SAP enterprise software applications like SAP S/4HANA. The collaboration will use technologies from Intel to enrich the underlying platform technologies of SAP enterprise applications, which include real-time in-memory computing, streaming & Big Data analytics, blockchain, augmented /virtual reality, learning machines & artificial intelligence, the internet of things (IoT), and security.

May 2019 - TIBCO acquired SnappyData, which provides a Spark-based data platform. This acquisition is expected to provide data scientists with a fast, high-scale in-memory data store to explore new larger sets of data.