Dublin, Sept. 17, 2019 (GLOBE NEWSWIRE) -- The "High Performance Computing and Data as a Service Market by Technology, Computing Type, Deployment Model, Use Case, Application, Sector (Consumer, Enterprise, Industrial, Government), Industry Vertical, and Region 2019-2024" report has been added to ResearchAndMarkets.com's offering.

This research evaluates the HPC market including companies, solutions, use cases, and applications. The analysis includes HPC by organizational size, software and system type, server type, and price band, and industry verticals. It also assesses the market for integration of various artificial intelligence technologies in HPC. It also evaluates the exascale-level HPC market including analysis by component, hardware type, service type, and industry vertical. It also provides HPC market sizing by component, hardware type, service type, and industry vertical from 2019 to 2024.

This research also evaluates the technologies, companies, strategies, and solutions for DaaS. It assesses business opportunities for enterprise use of own data, others data, and combination of both. It analyzes opportunities for enterprise to monetize their own data through various third-party DaaS offerings. It evaluates opportunities for DaaS in major industry verticals as well as the future outlook for emerging data monetization. Forecasts include global and regional projections by Sector, Data Collection, Source, and Structure from 2019 to 2024.

Market Summary & Insights

No longer solely the realm of supercomputers, the high-performance computing market is increasingly provided via cluster computing. By way of example, Hewlett Packard Enterprise provides a computational clustering solution in conjunction with Intel that represents HPC Infrastructure as a Service (IaaS). This particular HPC IaaS offering environment provides customized tenant clusters tailored to client and application requirements. Key to this particular solution is the intelligent use of APIs, which enable a high degree of flexibility and what HPE refers to as Dynamic Fabric Configuration.

These type of solutions, provided within a cloud-computing based as a Service model, allow HPC market offerings to be extended via HPC-as-a-Service (HPCaaS) to a much wider range of industry verticals and companies, thereby providing computational services to solve a much broader array of problems. Industry use cases are increasingly emerging that benefit from HPC-level computing, many of which benefit from split processing between localized device/platform and HPCaaS.

HPC currently suffers from an accessibility problem as well as inefficiencies and supercomputer skill gaps. Stated differently, the market for HPCaaS (e.g. access to high-performance computing services) currently suffers from problems related to the utilization, scheduling, and set-up time to run jobs on supercomputers. Some companies are leveraging artificial intelligence (AI) to improve HPC scheduling. As supercomputing resources are typically scarce and expensive, scheduling is important to ensure optimal computational workload scheduling. One company, Microsurgeonbot, Inc. (doing business as MSB.ai), which is developing a tool for setting up computing jobs for supercomputers. There is clearly a very long tail opportunity in HPC through Use of AI tools. HPCaaS will reach scale only through greater supercomputer accessibility.

Data produced by HPC is often difficult to use or even useless at times. Data needs to be managed and presented in a manner that is useful as information. Data as a Service (DaaS) represents a service model in which data is transformed into useful information. DaaS is one part of the larger Everything as a Service (XaaS) cloud computing based services model, including the traditional three horizontals of SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). It intersects with all three and derives value from a number of different horizontals and verticals.

Vendor managed DaaS systems provide necessary scalability and security for sustainable services execution. DaaS is expected to grow significantly in the near future due to a few dominant themes including cloud-based infrastructure/services, enterprise data syndication, and the consumer services trend towards XaaS. In addition to leveraging big data analytics, another approach to transform data into useful information is through the use of AI. One of the important growth areas for the DaaS market is to leverage AI to offer value-added data in a Decisions as a Service model.

Companies Mentioned

  • Advanced Micro Devices Inc.
  • Amazon Web Services Inc.
  • Atos SE
  • Cisco Systems
  • DELL Technologies Inc.
  • Fujitsu Ltd.
  • Hewlett Packard Enterprise
  • IBM Corporation
  • Intel Corporation
  • Microsoft Corporation
  • NEC Corporation
  • NVIDIA
  • Rackspace Inc.

Key Topics Covered

High-Performance Computing (HPC) Market by Component, Infrastructure, Services, Price Band, HPC Applications, Deployment Type, and Region 2019 - 2024

1. Executive Summary

2. Introduction
2.1 Next Generation Computing
2.2 High-Performance Computing
2.2.1 HPC Technology
2.2.1.1 Supercomputers
2.2.1.2 Computer Clustering
2.2.2 Exascale Computation
2.2.2.1 United States
2.2.2.2 China
2.2.2.3 Europe
2.2.2.4 Japan
2.2.2.5 India
2.2.2.6 Taiwan
2.2.3 High-Performance Technical Computing
2.2.4 Market Segmentation Considerations
2.2.5 Use Cases and Application Areas
2.2.5.1 Computer-Aided Engineering
2.2.5.2 Government
2.2.5.3 Financial Services
2.2.5.4 Education and Research
2.2.5.5 Manufacturing
2.2.5.6 Media and Entertainment
2.2.5.7 Electronic Design Automation
2.2.5.8 Bio-Sciences and Healthcare
2.2.5.9 Energy Management and Utilities
2.2.5.10 Earth Science
2.2.6 Regulatory Framework
2.2.7 Value Chain Analysis
2.2.8 AI to Drive HPC Performance and Adoption

3. High-Performance Computing Market Analysis and Forecast
3.1 Global High-Performance Computing Market 2019 - 2024
3.1.1 Total High-Performance Computing Market
3.1.2 High Performance Computing Market by Component
3.1.2.1 High Performance Computing Market by Hardware and Infrastructure Type
3.1.2.1.1 High Performance Computing Market by Server Type
3.1.2.2 High Performance Computing Market by Software and System Type
3.1.2.3 High Performance Computing Market by Professional Service Type
3.1.3 High Performance Computing Market by Deployment Type
3.1.4 High Performance Computing Market by Organization Size
3.1.5 High Performance Computing Market by Server Price Band
3.1.6 High Performance Computing Market by Application Type
3.1.6.1 High Performance Technical Computing Market by Industry Vertical
3.1.6.2 Critical High Performance Business Computing Market by Industry Vertical
3.1.1 High Performance Computing Deployment Options: Supercomputer vs. Clustering
3.1.2 High Performance Computing as a Service (HPCaaS)
3.1.3 AI Powered High Performance Computing Market
3.1.3.1 AI Powered High Performance Computing Market by Component
3.1.3.2 AI Powered High Performance Computing Market by AI Technology
3.2 Regional High Performance Computing Market 2019 - 2024
3.2.1 High Performance Computing Market by Region
3.2.2 North America High Performance Computing Market by Component, Deployment, Organization, Server Price Band, Application, Industry Vertical, and Country
3.2.3 Europe High Performance Computing Market by Component, Deployment, Organization, Server Price Band, Application, Industry Vertical, and Country
3.2.4 APAC High Performance Computing Market by Component, Deployment, Organization, Server Price Band, Application, Industry Vertical, and Country
3.2.5 MEA High Performance Computing Market by Component, Deployment, Organization, Server Price Band, Application, Industry Vertical, and Country
3.2.6 Latin America High Performance Computing Market by Component, Deployment, Organization, Server Price Band, Application, Industry Vertical, and Country
3.2.7 High Performance Computing Market by Top Ten Country
3.3 Exascale Computing Market
3.3.1 Exascale Computing Driven HPC Market by Component
3.3.2 Exascale Computing Driven HPC Market by Hardware Type
3.3.3 Exascale Computing Driven HPC Market by Service Type
3.3.4 Exascale Computing Driven HPC Market by Industry Vertical
3.3.1 Exascale Computing as a Service

4. High-Performance Computing Company Analysis
4.1 HPC Vendor Ecosystem
4.2 Leading HPC Companies
4.2.1 Amazon Web Services Inc.
4.2.2 Atos SE
4.2.3 Advanced Micro Devices Inc.
4.2.4 Cisco Systems
4.2.5 DELL Technologies Inc.
4.2.6 Fujitsu Ltd
4.2.7 Hewlett Packard Enterprise
4.2.8 IBM Corporation
4.2.9 Intel Corporation
4.2.10 Microsoft Corporation
4.2.11 NEC Corporation
4.2.12 NVIDIA
4.2.13 Rackspace Inc.

5. Conclusions and Recommendations

6. Appendix: Future of Computing
6.1 Quantum Computing
6.1.1 Quantum Computing Technology
6.1.2 Quantum Computing Considerations
6.1.3 Market Challenges and Opportunities
6.1.4 Recent Developments
6.1.5 Quantum Computing Value Chain
6.1.6 Quantum Computing Applications
6.1.7 Competitive Landscape
6.1.8 Government Investment in Quantum Computing
6.1.9 Quantum Computing Stakeholders by Country
6.1 Other Future Computing Technologies
6.1.1 Swarm Computing
6.1.2 Neuromorphic Computing
6.1.3 Biocomputing
6.2 Market Drivers for Future Computing Technologies
6.2.1 Efficient Computation and High-Speed Storage
6.2.2 Government and Private Initiatives
6.2.3 Flexible Computing
6.2.4 AI-enabled, High-Performance Embedded Devices, Chipsets, and ICs
6.2.5 Cost-Effective Computing powered by Pay-as-you-go Model
6.3 Future Computing Market Challenges
6.3.1 Data Security Concerns in Virtualized and Distributed Cloud
6.3.2 Funding Constrains R&D Activities
6.3.3 Lack of Skilled Professionals across the Sector
6.3.4 Absence of Uniformity among NGC Branches including Data Format

Data as a Service (DaaS) Market: Enterprise, Industrial, Public, and Government DaaS 2019 - 2024

1. Executive Summary
1.1 Global Data as a Service Market
1.2 Data as a Service Market by Data Type
1.3 Data as a Service Market by Region

2. Data as a Service Technologies
2.1 Cloud Computing and DaaS
2.2 Database Approaches and Solutions
2.2.1 Relational Database Management System
2.2.2 NoSQL
2.2.3 Hadoop
2.2.4 High Performance Computing Cluster
2.2.5 OpenStack
2.3 Data as a Service and the XaaS Ecosystem
2.4 Open Data Center Alliance
2.5 Market Sizing by Horizontal

3. Data as a Service Market
3.1 Market Overview
3.1.1 Understanding Data as a Service
3.1.2 Data Structure
3.1.3 Specialization
3.1.4 Vendors
3.2 Vendor Analysis and Prospects
3.2.1 Large Vendors
3.2.2 Mid-sized Vendors
3.2.3 Small Vendors
3.2.4 Market Sizing
3.3 Data as a Service Market Drivers and Constraints
3.3.1 Data as a Service Market Drivers
3.3.1.1 Business Intelligence and DaaS Integration
3.3.1.2 The Cloud Enabler DaaS
3.3.1.3 XaaS Drives DaaS
3.3.2 Data as a Service Market Constraints
3.3.2.1 Need for Data Integration
3.3.2.2 Issues Relating to Data-as-a-Service Integration
3.4 Barriers and Challenges to DaaS Adoption
3.4.1 Enterprises Reluctance to Change
3.4.2 Responsibility of Data Security Externalized
3.4.3 Security Concerns
3.4.4 Cyber Attacks
3.4.5 Unclear Agreements
3.4.6 Complexity is a Deterrent
3.4.7 Lack of Cloud Interoperability
3.4.8 Service Provider Resistance to Audits
3.4.9 Viability of Third-party Providers
3.4.10 No Move of Systems and Data is without Cost
3.4.11 Lack of Integration Features in the Public Cloud results in Reduced Functionality
3.5 Market Share and Geographic Influence
3.6 Vendors

4. Data as a Service Strategies
4.1 General Strategies
4.1.1 Tiered Data Focus
4.1.2 Value-based Pricing
4.1.3 Open Development Environment
4.2 Strategies for Emerging Market Opportunities
4.2.1 Communication Service Providers and DaaS
4.2.1.1 Service Ecosystem and Platforms
4.2.1.2 Bringing to Together Multiple Sources for Mash-ups
4.2.1.3 Developing Value-added Services as Proof Points
4.2.1.4 Open Access to all Entities including Competitors
4.2.2 Internet of Things and Data as a Service
4.2.2.1 Data as a Service is a Perfect Match for IoT
4.2.2.2 IoT Management for DaaS
4.2.2.3 Integrating IoT Data for DaaS
4.2.2.4 IoT Data as a Service requires Data Mediation
4.2.3 Edge Networks and Data as a Service
4.2.3.1 Mobile Edge Computing
4.2.3.2 Data from the Edge: MEC and Data as a Service
4.3 Service Provider Strategies
4.3.1 Telecom Network Operators
4.3.2 Data Center Providers
4.3.3 Managed Service Providers
4.4 Infrastructure Provider Strategies
4.4.1 Enable New Business Models
4.5 Application Developer Strategies

5. Data as a Service Applications
5.1 Business Intelligence
5.2 Development Environments
5.3 Verification and Authorization
5.4 Reporting and Analytics
5.5 Data as a Service in Healthcare
5.6 Data as a Service and Wearable Technology
5.7 Data as a Service in the Government Sector
5.8 Data as a Service for Media and Entertainment
5.9 Data as a Service for Telecoms
5.10 Data as a Service for Insurance
5.11 Data as a Service for Utilities and Energy Sector
5.12 Data as a Service for Pharmaceuticals
5.13 Data as a Service for Financial Services

6. Market Outlook and Future of Data as a Service
6.1 Security Concerns
6.2 Cloud Trends
6.2.1 Hybrid Computing
6.2.2 Multi-Cloud
6.2.3 Cloud Bursting
1.1 General Data Trends
6.3 Enterprise Leverages own Data and Telecom
6.3.1 Web APIs
6.3.2 SOA and Enterprise APIs
6.3.3 Cloud APIs
6.3.4 Telecom APIs
6.4 Data Federation Emerges for Data as a Service

7. Data as a Service Market Analysis and Forecasts 2019 - 2024
7.1 DaaS Market by Sector: Business, Public, and Government
7.1.1 DaaS Market for Public Data
7.1.2 DaaS Market for Business Data (Enterprise and Industrial)
7.1.3 DaaS Market for Government Data
7.2 DaaS Market by Source: Machine and Non-Machine Data
7.3 DaaS Market by Data Collection: IoT and Non-IoT Data
7.4 DaaS Markets by Hosting Type: Private, Public, and Hybrid
7.5 DaaS Markets by Pricing Model
7.6 DaaS Market by Service
7.7 DaaS Markets by Industry Vertical

8. Regional DaaS Market Analysis and Forecasts 2019 - 2024
8.1 North America Data as a Service Market
8.1.1 North America: DaaS Market by Sector (Business, Public, and Government)
8.1.2 North America: DaaS Market for Public Data
8.1.2.1 North America: DaaS Markets by Solution using Public Data
8.1.3 North America: DaaS Market for Business Data
8.1.3.1 DaaS Markets by Solution using Business Data
8.1.4 North America: DaaS Market by Data Source (Machine and Non-machine)
8.1.5 North America: DaaS Market by Data Collection Type
8.1.6 North America: DaaS Markets Hosting Type
8.1.7 North America: DaaS Markets by Pricing Model
8.1.8 North America: DaaS Market by Service
8.1.9 North America: DaaS Market by Industry Vertical
8.2 South America Data as a Service Market
8.2.1 South America: DaaS Market by Sector (Business, Public, and Government)
8.2.2 South America: DaaS Market for Public Data
8.2.2.1 South America: DaaS Market Solution using Public Data
8.2.3 South America: DaaS Market for Business Data
8.2.3.1 DaaS Market by Solution using Business Data
8.2.4 South America: DaaS Market by Data Source Type
8.2.5 South America: DaaS Market by Data Collection Type
8.2.6 South America: DaaS Market Hosting Type
8.2.7 South America: DaaS Market by Pricing Model
8.2.8 South America: DaaS Market by Service
8.2.9 South America: DaaS Market by Industry Vertical
8.3 Western Europe Data as a Service Market
8.3.1 Western Europe: DaaS Market by Sector (Business, Public, and Government)
8.3.2 Western Europe: DaaS Market for Public Data
8.3.2.1 Western Europe: DaaS Market by Solution using Public Data
8.3.3 Western Europe: DaaS Market for Business Data
8.3.3.1 DaaS Market by Solution using Business Data
8.3.4 Western Europe: DaaS Market by Data Source Type
8.3.5 Western Europe: DaaS Market by Data Collection Type
8.3.6 Western Europe: DaaS Market Hosting Type
8.3.7 Western Europe: DaaS Market by Pricing Model
8.3.8 Western Europe: DaaS Market by Service
8.3.9 Western Europe: DaaS Market by Industry Vertical
8.4 Central & Eastern European Data as a Service Market
8.4.1 Central & Eastern Europe: DaaS Market by Sector (Business, Public, and Government)
8.4.2 Central and Eastern Europe: DaaS Market for Public Data
8.4.2.1 Central and Eastern Europe: DaaS Market by Solution using Public Data
8.4.3 Central and Eastern Europe: DaaS Market for Business Data
8.4.3.1 DaaS Market by Solution using Business Data
8.4.4 Central and Eastern Europe: DaaS Market by Data Source Type
8.4.5 Central and Eastern Europe: DaaS Market by Data Collection Type
8.4.6 Central and Eastern Europe: DaaS Markets Hosting Type
8.4.7 Central and Eastern Europe: DaaS Markets by Pricing Model
8.4.8 Central and Eastern Europe: DaaS Markets by Service
8.4.9 Central and Eastern Europe: DaaS Markets by Industry Vertical
8.5 Asia Pacific Data as a Service Market
8.5.1 Asia Pacific: DaaS Market by Sector (Business, Public, and Government)
8.5.2 Asia Pacific: DaaS Market for Public Data
8.5.2.1 Asia Pacific: DaaS Market by Solution using Public Data
8.5.3 Asia Pacific: DaaS Market for Business Data
8.5.3.1 DaaS Market by Solution using Business Data
8.5.4 Asia Pacific: DaaS Market by Data Source Type
8.5.5 Asia Pacific: DaaS Market by Data Collection Type
8.5.6 Asia Pacific: DaaS Market by Hosting Type
8.5.7 Asia Pacific: DaaS Markets by Pricing Model
8.5.8 Asia Pacific: DaaS Markets by Service
8.5.9 Asia Pacific: DaaS Market by Industry Vertical
8.6 Middle East and Africa Data as a Service Market
8.6.1 Middle East and Africa: DaaS Market by Sector (Business, Public, and Government)
8.6.2 Middle East & Africa: DaaS Market for Public Data
8.6.2.1 Middle East & Africa: DaaS Market by Solution using Public Data
8.6.3 Middle East & Africa: DaaS Market for Business Data
8.6.3.1 DaaS Market by Solution using Business Data
8.6.4 Middle East & Africa: DaaS Market by Data Source Type
8.6.5 Middle East & Africa: DaaS Market by Data Collection Type
8.6.6 Middle East & Africa: DaaS Markets Hosting Type
8.6.7 Middle East & Africa: DaaS Markets by Pricing Model
8.6.8 Middle East & Africa: DaaS Markets by Service
8.6.9 Middle East & Africa: DaaS Markets by Industry Vertical

9. Conclusions and Recommendations
9.1.1 Data as a Service and IoT
9.1.2 Data as a Service and CSP Data
9.1.3 Data as a Service and Enterprise

10. Appendix
10.1 Structured vs. Unstructured Data
10.1.1 Structured Database Services in Telecom
10.1.2 Unstructured Database Services in Telecom and Enterprise
10.1.3 Emerging Hybrid (Structured/Unstructured) Database Services
10.2 Data Architecture and Functionality
10.2.1 Data Architecture
10.2.1.1 Data Models and Modelling
10.2.1.2 DaaS Architecture
10.2.2 Data Mart vs. Data Warehouse
10.2.3 Data Gateway
10.2.4 Data Mediation
10.3 Data Governance
10.3.1 Data Security
10.3.2 Data Quality
10.3.3 Data Integration
10.4 Master Data Management
10.4.1 Understanding MDM
10.4.1.1 Transactional vs. Non-transactional Data
10.4.1.2 Reference vs. Analytics Data
10.4.2 MDM and DaaS
10.4.2.1 Data Acquisition and Provisioning
10.4.2.2 Data Warehousing and Business Intelligence
10.4.2.3 Analytics and Virtualization
10.4.2.4 Data Governance
10.5 Data Mining
10.5.1 Data Capture
10.5.1.1 Event Detection
10.5.1.2 Capture Methods
10.5.2 Data Mining Tools

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