Adoption of in-memory computing, also known as IMC, is on the rise. This can be attributed to the growing demand for faster processing and analytics on big data, the need for simplifying architecture as the number of various data sources increases, and technology enhancements that are optimizing their Total Cost of Ownership. In-memory computing, fundamentally comprises of RAM storage and parallel distributed processing. The adoption, however, is dependent on how businesses understand the value it can add, such as, Usage of less hardware to support the required performance, to reduce the upfront capital costs and Lifetime extension for the hardware and software via increased performance. Further, the demand to process huge and growing datasets in real-time are acting as catalyst towards the in-memory computing middleware adoption. Over the past several years, companies across a broad range of industries have been observed commencing adoption of in-memory computing platforms to achieve the application performance and scalability they need to achieve their digital transformation or omnichannel customer experience. Some of the use cases are in banking and financial services, retail, healthcare, to name a few. For instance, the BFSI sector deploys the in-memory computing to facilitate smooth investment banking transactions, insurance claim processing & modelling, among others. Risk and Asset Management firms used the technology to gain an understanding on range and probabilities of probable investment outcomes through complex simulations.

1. INTRODUCTION

2. RESEARCH METHODOLOGY

3. EXECUTIVE SUMMARY

4. MARKET INSIGHTS

5. MARKET DYNAMICS

6. MARKET SEGMENTATION

7. COMPETITIVE LANDSCAPE

8. INVESTMENT ANALYSIS

9. FUTURE OF THE MARKET