The In Memory Data Grid Market is expected to register a CAGR of over 11% over the forecast period. In Memory Data Grids are suited to handle Big Data’s “big-three V’s”: variability, velocity, and volume.
– With the emergence of technologies such as Big Data, cloud, mobile, and IoT, businesses need their applications to provide higher performance, flexibility, availability, reliability, and scalability than ever before.
– For instance, the amount of data generated is growing at a rapid pace. According to Seagate Technology PLC, the volume of data created worldwide is expected to increase to 47 zettabytes and 163 zettabytes in 2020 and 2025, respectively, from 12 zettabytes in 2015.
– Additionally, the rising usage of the cloud has supported the data generated across various verticals. With the rapid growth of the cloud, enterprises of all sizes and industries are producing more data than ever, even up to terabytes per second. These data provide insights with potential business value.
– However, this massive data growth is creating new obstacles that make it difficult for applications to meet such demands. Scaling the data tier creates both economic and technical challenges for organizations.
– With the implementation of Platform-as-a-Service (PaaS), cloud, and container-based infrastructures, these challenges have become even more complicated. Whether data is hosted in the cloud, or on-premise, in a distributed or centralized architecture, IT infrastructures are more complex than ever before. Organizations are in need of flexible applications that can be used in a variety of hybrid cloud environments.
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– To meet the challenges of IT complexity and data growth, In-memory data grids deliver elasticity and flexibility to help organizations achieve the full benefits of PaaS and microservices architectures, while also helping applications run effectively in the cloud. Additionally, the In-memory data grid gives applications a scalable in-memory repository for rapidly changing application data.
– Additionally, with the pandemic outbreak, non-essential businesses have been shut. The need for computing across sectors has risen, thereby requiring seamless scaling of the data, messaging and application tiers, offloads compute, read, and write-intensive workloads from existing core infrastructure. Hence such trends are expected to create scope for the market.
Key Market Trends
Growing Need for Real Time Data Processing in BFSI Driving the Industry Growth
– Growing digitalization are compelling financial companies to build a lean, flexible, and efficient approach to cater to their customers. Financial institutions deal with critical information, which, if not properly processed, can have severe financial and ethical implications.
– Hence, financial organizations worldwide are looking for in-memory data grid solutions, which can process data in real-time and improve their business-critical applications.
– A company such as Grid Gain is one of the prominent providers of In-memory data grid. Leading banks depend on GridGain to help them offer an integrated omnichannel banking experience. By using GridGain, organizations have not only added speed and scale to digital channels. They have opened up previously siloed data for seamless sharing across channels and implemented in-process HTAP using real-time streaming analytics, machine, and deep learning to monitor and enhance the end-to-end banking experience proactively.
– Trading systems with high transaction rates are examples of environments best-suited for the in-memory data grid. It enables financial applications with real-time analytics due to faster data access.
– One of the prominent use cases for the In-memory data grid is running large-scale simulations such as “Monte Carlo simulations,” which help create a clearer picture of what might happen in the future by considering various factors. These kinds of simulations are commonly run in the financial services industry to better understand the risks that the firms face.
– The increasing speed of commerce and the growing sophistication and organization of the people intent on criminal activity creates a complicated problem for financial institutions.
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