A GPU Database is a database, relational or non-relational, that uses a GPU (graphical processing unit) to perform some database operations. Most of the GPU databases tend to focus on analytics, and they’re offering it to a market that was oversold on Hadoop for Big Data analytics. GPU-accelerated databases with their brute force computing power can ingest a vast amount of data related to customers from a variety of sources.
The global GPU database market size is expected to grow from USD +178 million in 2019 to USD +455 million by 2026, at a Compound Annual Growth Rate (CAGR) of +20% during the forecast period 2019-2026.
The report is titled as GPU Database Market which emphases in describing the primary visions and outlines in the Industry. The research study throws more light on the competitive landscape of the market across the globe along with Development strategy, company profiles, financial overview, business strategies, SWOT analysis, and gives an understanding into the newest expansions of the key players that have been tinted in the study. A thorough analysis of these factors has been undertaken for determining the future growth prospects of the Global Market.
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Leading GPU Database Market Players:
Kinetica (US), OmniSci (US), SQream (US), Neo4j (US), NVIDIA (US), Brytlyt (UK), Blazegraph (US), BlazingDB (US), Zilliz (US), Jedox (Germany), HeteroDB (Japan), H2O.ai (US), FASTDATA.io (US), Fuzzy Logix
GPU Database Market Segment by Application :-
- Threat Intelligence
- Fraud Detection and Prevention
- Predictive Maintenance
- Others (Network Management and Campaign Management)
GPU Database Market Segment by Component :-
- GPU-accelerated Databases
- GPU-accelerated Analytics
GPU Database Market Segment by verticals :-
- Retail and eCommerce
- Healthcare and Pharmaceuticals
- Telecommunications and IT
- Transportation and Logistics
- Government and Defense
- Others (Energy and Utilities, Oil and Gas, Media and Entertainment, and Manufacturing)
This research report is an intellect report which has been completed by using primary and its subordinate procedures. In the study of the GPU Database market, the current industries, as well as upcoming startups, have been measured. It helps to make knowledgeable decisions in the businesses. Well explained Porter’s five analysis and SWOT study have been used by an analyst of the report
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Key questions answered in the report include:
- What will the market size and the growth rate be in 2025?
- What are the key factors driving the global GPU Database Solutions market?
- What are the key market trends impacting the growth of the global GPU Database Solutions market?
- What are the challenges to market growth?
- Who are the key vendors in the global GPU Database Solutions market?
The business profiles of important key players have been outlined to get a complete description of applicable strategies carried out by top-level businesses. The global GPU Database market has been studied in terms of the competitive landscape. It highlights the Vendor analysis of GPU Database industries. This research report supports to offer the proper strategies for improving the performance of the business. Detailed information of some clients, vendors, and sellers have been involved in the report. Financial positions such as prices, shares, and profit margin have been presented in terms of facts and figures.
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Table of Contents:
Global GPU Database Market Research Report 2019-2025
Chapter 1: GPU Database Market Overview
Chapter 2: Global Economic Impact on Industry
Chapter 3: GPU Database Market Competition by Top Vendors
Chapter 4: Development, Revenue (Value) by Region
Chapter 5: Global strategy, development, Export, Import by Regions
Chapter 6: Global Segmentation, Price Trend by Type
Chapter 7: Global Market Analysis by Application
Chapter 8: Strategic Planning and analysis
Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10: Marketing Strategy Analysis, Distributors/Traders
Chapter 11: GPU Database Market Effect Factors Analysis
Chapter 12: Global GPU Database Market Forecast
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