The global relational in-memory database market experienced significant growth in 2020 and is projected to continue growing at a double-digit revenue compound annual growth rate (CAGR) throughout the forecast period. This growth is primarily attributed to several key factors.

Firstly, there is an increasing adoption of relational in-memory databases across various industries to efficiently collect, store, and access big data. Industries such as banking, telecommunication, retail, healthcare, automobiles, and education are recognizing the benefits of in-memory databases for handling large volumes of data effectively.

Secondly, the demand for in-memory relational databases is growing due to the expansion of web applications, the rise in business analytics, and the need for efficient database warehousing. The proliferation of online transactions and the presence of a well-established IT sector further contribute to the increasing demand for relational in-memory databases.

Thirdly, the global market is driven by the growing usage of advanced technologies by consumers worldwide. As technology becomes more integrated into everyday life, the need for efficient and real-time data processing becomes crucial, leading to an increased demand for in-memory databases.

Lastly, the reduced prices of random access memory (RAM) play a significant role in driving the demand for relational in-memory databases. Lower RAM prices make these databases more affordable and accessible to businesses of all sizes, contributing to market growth.

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Rising demand and adoption: Various industries such as banking, telecommunication, retail, healthcare, automobiles, and education are generating large amounts of data. This has led to an increased demand for relational in-memory databases, which can efficiently store, organize, and access data.

Real-time processing: There is a growing need for analyzing and rapidly accessing data in real time to enhance application performance. Relational in-memory databases offer faster retrieval and organization of large database transactions compared to traditional databases, which has contributed to their adoption.

Benefits over traditional databases: Increasing awareness about the advantages of relational in-memory databases has fueled their market growth. These databases offer faster transaction processing and data retrieval, leading to improved performance.

Types of relational in-memory databases: Relational in-memory databases encompass various types, including relational, NoSQL, and NEWSQL databases. This provides flexibility for different data storage and processing needs.

Faster transaction and data processing: The increasing demand for relational in-memory databases is driven by the need for faster transaction processing and data analysis. These databases enable efficient handling of web-based transactions, leading to improved performance and user experience.

Falling RAM prices: The declining prices of Random Access Memory (RAM) have made in-memory databases more cost-effective. This has made them more accessible to businesses of various sizes and contributed to market growth.

Data Type Outlook (Revenue, USD Billion; 2018 – 2028)

• Relational
• NoSQL
• NEWSQL

Application Outlook (Revenue, USD Billion; 2018 – 2028)

• Transaction
• Reporting
• Analytics
• Others

Processing Type Outlook (Revenue, USD Billion; 2018 – 2028)

• Online Analytical Processing (OLAP)
• Online Transaction Processing (OLTP)

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Regional Markets Highlighted in the Global Relational In-Memory Database Market Report:

• North America
  • S.
  • Canada
  • Mexico
• Europe
  • Germany
  • K.
  • Italy
  • France
  • BENELUX
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Rest of Asia Pacific
• Latin America
  • Brazil
  • Rest of Latin America
• Middle East Africa
  • Saudi Arabia
  • A.E.
  • South Africa
  • Rest of Middle East Africa

Top Companies Profiled in the Report:

Microsoft Corporation, McObject, IBM Corporation, Oracle, SAP SE, Teradata, Altibase Corporation, Amazon Web Services, Tableau, Kognitio, DataStax, ENEA, and VoltDB.

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