Market Size in 2023 | Market Forecast in 2032 | CAGR (in %) | Base Year |
---|---|---|---|
USD 509.36 Million | USD 2368.35 Million | 18.62% | 2023 |
According to Zion Market Research, the global GPU Database Market was worth USD 509.36 Million in 2023. The market is forecast to reach USD 2368.35 Million by 2032, growing at a compound annual growth rate (CAGR) of 18.62% during the forecast period 2024-2032. The report offers a comprehensive analysis of the market, highlighting the factors that will determine growth, potential challenges, and opportunities that could emerge in the GPU Database industry over the next decade.
GPU database head large volumes of data faster and more efficiently than CPUs because they work in parallel, instead of sequence. GPUs speed up location-based and in-memory analytics, machine learning, and AI. With thousands of processing cores easily available on a single card, it is possible to perform functions in parallel, using brute force to solve complex analytics operations that traditional databases struggle with. Aggregations, sorts, and grouping operations are workload intensive for a CPU, but can work effectively in parallel on a GPU database.
The key factors driving the GPU Database market include massive data generation across BFSI, retail, and media and entertainment industries. In addition to this, massive use of GPU database for GRC, fraud identification & prevention, threat intelligence, SCM, and CEM is likely to spur the GPU Database market trends in the coming years. The massive popularity of GPU accelerated databases and tools in banks, insurance firms, and many other financial institutions will contribute substantially towards GPU Database market revenue in the forthcoming years. With inception of supercomputer, the demand for GPU database is gaining momentum across the world for effective data analytics and achieving accurate outcomes.
Launching of GPU-accelerated tools has eased operations, thereby enabling speedy data processing operations and real-time analytics. These tools help firms in performing risky computations as well as simulations in real-time. Parallel computing with GPU applications is projected to help firms monitor irregularities in different streams of transactions and log data. All these aforementioned factors are likely to further steer the growth trajectory of GPU Database market in the years ahead.
The global GPU Database market can be divided based on application and tools.
Based on the application, the GPU Database market can be sectored into GRC, CEM, Threat Intelligence, SCM, and Fraud Detection and Prevention.
On the basis of tools, the market can be segmented into GPU-accelerated databases and GPU-accelerated analytics,
Region-wise, the global market is segregated into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.
Report Attributes | Report Details |
---|---|
Report Name | GPU Database Market |
Market Size in 2023 | USD 509.36 Million |
Market Forecast in 2032 | USD 2368.35 Million |
Growth Rate | CAGR of 18.62% |
Number of Pages | 110 |
Key Companies Covered | Kinetica, OmniSci, SQream, Neo4j, NVIDIA, Brytlyt, Blazegraph, BlazingDB, Zilliz, Jedox, HeteroDB, H2O.ai, FASTDATA.io, Fuzzy Logix, Anaconda, and Graphistry |
Segments Covered | By Application, By Tools And By Region |
Regions Covered | North America, Europe, Asia Pacific (APAC), Latin America, Middle East, and Africa (MEA) |
Base Year | 2023 |
Historical Year | 2018 to 2022 |
Forecast Year | 2024 - 2032 |
Customization Scope | Avail customized purchase options to meet your exact research needs. Request For Customization |
Based on regions, the global GPU Database market can be divided into five main regions: North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. North America GPU Database market is expected to account for the highest global market share, due to the early adoption of GPU- accelerated tools as well as prominent initiatives taken by the industry players in the form of partnerships with different technology players to offer rapid analytics and data processing solutions. The huge presence of GPU database vendors in countries such as the U.S. as well as the widespread awareness about these solutions in the region is expected to help the region retain its largest market share during the forecast period.
Asia Pacific GPU Database market is also likely to record lucrative growth over the next few years, subject to massive use of supercomputers for weather forecasting, and massive use of new technologies in banking sectors in countries such as India, Japan, and China along with growing need for secured solutions in myriad financial institutions across these emerging economies. Countries such as Germany and the UK are expected to drive the growth of Europe GPU Database market in the years ahead.
Some main participants of the GPU Database market are
By applications
By components
By deployment models
By verticals
GPU Database Market: Regional Segment Analysis
FrequentlyAsked Questions
The key factors driving the GPU Database market include massive data generation across BFSI, retail, and media and entertainment industries. In addition to this, massive use of GPU database for GRC, fraud identification & prevention, threat intelligence, SCM, and CEM is likely to spur the GPU Database market trends in the coming years.
According to Zion Market Research, the global GPU Database Market was worth USD 509.36 Million in 2023. The market is forecast to reach USD 2368.35 Million by 2032, growing at a compound annual growth rate (CAGR) of 18.62% during the forecast period 2024-2032.
North America GPU Database market is expected to account for the highest global market share, due to the early adoption of GPU- accelerated tools as well as prominent initiatives taken by the industry players in the form of partnerships with different technology players to offer rapid analytics and data processing solutions.
Some main participants of the GPU Database market are Kinetica, OmniSci, SQream, Neo4j, NVIDIA, Brytlyt, Blazegraph, BlazingDB, Zilliz, Jedox, HeteroDB, H2O.ai, FASTDATA.io, Fuzzy Logix, Anaconda, and Graphistry among others.
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