Data virtualization is a data integration technology. It incorporates data from numerous formats, sources, and locations, without replicating the information, to form a single ‘virtual’ data layer. The layer provides combined data services to support different users and applications.
Virtual data optimizer is a ready-to-run software package that is designed to add data reduction abilities to the Linux block storage heap. This technology performs aligned at a 4 KB granularity, providing the best possible balance of performance and data reduction rates. Virtual data optimizers utilize data deduplication and inline compression methods to transparently recoil data as it is being written to storage media. Furthermore, in order to reduce the data footprint, virtual data optimizers combine three techniques: data deduplication, zero-block elimination, and data compression. By using these techniques, virtual data optimizers can vividly enhance the productivity for both network bandwidth utilization and storage.
The global virtual data optimizer market is expanding at a rapid pace, mainly due to an increase in demand for cost-efficient data management solutions among enterprises with an aim to manage large amounts of data. Virtual data optimizers enable enterprises to save storage space and reduce cost in terms of solution deployment. This can translate to reduced costs for cloud-based exterior block storage, reduced costs per compute instance, and reduced costs for long-term maintenance of data pictures. Additionally, a rise in the requirement for data deduplication software tools in order to remove duplicate copies of blocks of data that have already been stored is anticipated to boost the demand for virtual data optimizer around the globe.
Rise in demand for cloud-based solutions across major end-use industries, including health care, IT & telecom, and BFSI in order to manage business more efficiently is projected to fuel the market. However, lack of standardization in data optimization processes is likely to hinder the market.
There is an increase in the number of data centers across the world and rise in the adoption of data virtualization solutions in data centers to efficiently manage data. This is primarily because data centers are the major source of generating large amount of data globally. This, in turn, is anticipated to create new opportunities for the growth of the global virtual data optimizer market in the coming years.
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The global virtual data optimizer market can be segmented based on component, enterprise size, deployment, industry, and region. In terms of component, the virtual data optimizer market can be bifurcated into solutions and services. The services segment can be further split into professional and managed services. Based on enterprise size, the virtual data optimizer market can be categorized into small and medium enterprises and large enterprises. In terms of deployment, the virtual data optimizer market can be categorized into cloud-based and on-premise. Based on industry, the global virtual data optimizer market can be classified into retail & e-commerce, media & entertainment, health care, manufacturing, IT & telecom, BFSI, government, and others.
In terms of geography, the virtual data optimizer market can be segmented into South America, North America, Europe, Asia Pacific, and Middle East & Africa. North America is expected to dominate the virtual data optimizer market over the forecast period. The region witness strong adoption of virtual data optimizers due to the presence of a large number of data centers in the region. The virtual data optimizer market in Asia Pacific is anticipated to expand at a significant growth rate during the forecast period. In Asia Pacific, enterprises are highly emphasizing on deploying data management solutions in order to reduce upfront costs, enhance value on projects, and to build an agile information architecture.
Major players operating in the global virtual data optimizer market include Microsoft Corporation, SAP SE, Oracle Corporation, VMware, Inc., International Business Machines (IBM) Corporation, Intel Corporation, Cisco, Inc., Amazon Web Services (AWS), and Red Hat, Inc.
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