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New York, US, April 10, 2023 (GLOBE NEWSWIRE) -- According to a comprehensive research report by Market Research Future (MRFR), IoT Cloud Platform Market Research Report Information By Deployment, By Application, By End-user, And By Region Market Forecast Till 2030., the market is anticipated to acquire a valuation of approximately USD 23.66 billion by the end of 2030. The reports further predict the market to flourish at a robust CAGR of over 14.10% during the assessment timeframe.

Key Players:

The market research report by MRFR provides a comprehensive analysis of the key players in the IoT cloud platform market, including-

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Report Scope:

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IoT Cloud Platform Market Drivers:

The adoption of cloud computing has revolutionized the way businesses operate, providing a flexible and scalable platform for hosting applications and managing data. This has led to an increase in the adoption of cloud-based IoT platforms, which allow businesses to connect and manage their devices, sensors, and data in real-time.

The proliferation of connected devices, including smartphones, wearables, and smart home appliances, has created a massive amount of data that needs to be processed, analyzed, and stored. IoT cloud platforms provide a centralized platform for managing this data, allowing businesses to gain valuable insights into customer behavior, product performance, and operational efficiency.

IoT Cloud Platform Market Challenges:

Despite the numerous benefits of IoT cloud platforms, there are several challenges that need to be addressed. One of the main challenges is data security and privacy concerns, as the use of connected devices and cloud-based platforms creates new vulnerabilities that can be exploited by hackers and cybercriminals.

Another challenge is the lack of interoperability standards, which makes it difficult for different devices and platforms to communicate with each other. This can lead to compatibility issues and can limit the effectiveness of IoT solutions.

IoT Cloud Platform Market Segmentation:

The market research report by MRFR provides a detailed segmentation analysis of the IoT cloud platform market based on deployment model, application, platform, and region.

Based on deployment model, the market is segmented into public cloud, private cloud, and hybrid cloud. Based on application, the market is segmented into smart homes, smart cities, industrial automation, healthcare, and others. Based on platform, the market is segmented into device management, application management, connectivity management, and others.

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Regional Analysis:

The market research report by MRFR provides a comprehensive regional analysis of the IoT cloud platform market, covering North America, Europe, Asia-Pacific, and the rest of the world.North America is expected to dominate the market during the forecast period, due to the presence of major IoT players in the region, as well as the high adoption of cloud computing and connected devices.Asia-Pacific is expected to grow at the highest CAGR during the forecast period, due to the rapid digitization of businesses and the increasing adoption of IoT technologies in the region.

Industry Trends:

The market research report by MRFR highlights several industry trends that are expected to shape the future of the IoT cloud platform market, including the increasing adoption of edge computing, the rise of 5G networks, and the emergence of blockchain technology.

Recent Developments:

The market research report by MRFR highlights several recent developments in the IoT cloud platform market, including the launch of new platforms and services, partnerships and collaborations between key players, and acquisitions and mergers.

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In June 2022, Amazon Web Services (AWS) launched a new IoT device management service, AWS IoT Device Management, which allows customers to easily onboard, organize, monitor, and remotely manage their IoT devices at scale.

In August 2022, Microsoft announced a partnership with Bosch to develop a new IoT platform for the automotive industry. The platform, called Bosch Automotive Cloud Suite powered by Microsoft Azure, will provide real-time data analytics and insights to improve vehicle performance and maintenance.In September 2022, Salesforce announced the acquisition of Slack, a collaboration platform that will enable Salesforce to integrate real-time communication and collaboration into its IoT cloud platform.

Opportunities:

The market research report by MRFR highlights several opportunities in the IoT cloud platform market, including the adoption of edge computing, the rise of 5G networks, and the emergence of blockchain technology.

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data, such as sensors and IoT devices. This reduces latency and improves data processing, making it an ideal solution for real-time applications, such as autonomous vehicles and industrial automation.

The rise of 5G networks is expected to revolutionize the IoT industry, providing faster speeds, lower latency, and greater connectivity. This will enable the development of new applications and services that require real-time data processing and analysis, such as remote healthcare monitoring and smart city management.

Blockchain technology is another emerging trend in the IoT industry, providing a secure and decentralized platform for managing IoT devices and data. This technology has the potential to address the security and privacy concerns associated with IoT devices and cloud-based platforms, making it an ideal solution for industries that require high levels of security, such as finance and healthcare.

Conclusion:

The global IoT cloud platform market is undergoing significant growth, driven by the increasing adoption of cloud computing and connected devices. The market is expected to grow at a significant rate during the forecast period of 2023 to 2028, with several key players leading the way in terms of innovation and market share. As the market continues to evolve, opportunities will emerge for new players to enter the market and for existing players to expand their offerings and capabilities.

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About Market Research Future:

Market Research Future (MRFR) is a global market research company that takes pride in its services, offering a complete and accurate analysis regarding diverse markets and consumers worldwide. Market Research Future has the distinguished objective of providing the optimal quality research and granular research to clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help answer your most important questions.Follow Us:LinkedIn|Twitter

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IoT Cloud Platform Market Is Expected To Reach USD 23.66 Billion ... - GlobeNewswire

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PRESS RELEASE

Published April 11, 2023

New Jersey, N.J, April. 11, 2023 (Digital Journal) - Cloud-native software refers to applications that are designed and optimized for deployment in cloud computing environments. Cloud-native software is built using specific design principles and technologies, such as containerization, microservices architecture, and dynamic orchestration, that enable applications to be highly scalable, resilient, and fault-tolerant. This approach to software development emphasizes the use of cloud services and APIs for all aspects of an applications lifecycle, including development, testing, deployment, and management. By adopting a cloud-native approach, organizations can take advantage of the scalability, agility, and cost savings offered by cloud computing while delivering highly responsive and reliable applications to their users.

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The global Cloud-native Software Market is expected to grow at a significant CAGR of +23% during the forecasting Period (2023 to 2030).

Cloud-native Software Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data that has been looked at considers both the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Top Key Players Profiled in this report are:

? IBM? Nokia? Onica? Microsoft Azure? Google? Oracle? SAP? Symantec? VMWare? Salesforce? HCL? Amadeus? Pivotal? AWS

The key questions answered in this report:

Various factors are responsible for the market's growth trajectory, which is studied at length in the report. In addition, the report lists the restraints that are posing a threat to the global Cloud-native Software market. It also gauges the bargaining power of suppliers and buyers, the threat from new entrants and product substitutes, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analysed in detail in the report. It studies the market's trajectory between forecast periods.

Global Cloud-native Software Market Segmentation:

Market Segmentation: By Type

Market segmentation by Application:

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Regions Covered in the Global Cloud-native Software Market Report 2022:

The Middle East and Africa (GCC Countries and Egypt)

North America (the United States, Mexico, and Canada)

South America (Brazil etc.)

Europe (Turkey, Germany, Russia UK, Italy, France, etc.)

Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia

The cost analysis of the global Cloud-native Software market has been performed while keeping in mind manufacturing expenses, labour costs, raw materials, their market concentration rate, suppliers, and price trend. Other factors such as supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

The report provides insights on the following pointers:

Market Penetration: Comprehensive information on the product portfolios of the top players in the Cloud-native Software market.

Product Development/Innovation: Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.

Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.

Market Development: Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.

Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the Cloud-native Software market.

Table of Contents

Global Cloud-native Software Market Research Report 2022 - 2029

Chapter 1 Cloud-native Software Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Cloud-native Software Market Forecast

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COMTEX_428938950/2769/2023-04-11T01:54:49

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Cloud-native Software Market to Flourish with an Impressive CAGR ... - Digital Journal

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In the history of big data, no one knows exactly how the term Big Data originated. It has been used since the 1990s. John R. Mashey, a Silicon Graphics professional, is credited with popularizing the term. It may surprise many that Big Data is not a term coined in recent years. Data analysis and techniques related to analysis were used by people over the course of centuries to help them make better decisions. The speed and volume of data generation have increased incredibly over the last two decades. It is now reached a level where it has sprung beyond measures of human comprehension.

Data analysis, analytics, and the concept of Big Data are all connected to data management. They rely on various features and techniques, including storing, extracting, and optimizing data stored in Relational Database Management Systems (RDBMS).

The key components in the first phase were database management and data warehousing. They provided the base for the further development of modern data analysis.

The process of advanced data collection and data analysis features began in early 2000. During this period, web traffic and online stores started proliferating. Leading organizations dealing with Big Data initiated the detailed analysis of customer behavior by researching aspects such as search logs, click rates, and location data. It also opened up a whole new world of possibilities.

HTTP-based web traffic helped drive efforts in proper analysis and storage of semi-structured and unstructured data. Organizations were keen to find solutions for the storage and analysis of standard structured data as well as new data types so that they could be analyzed efficiently. Additionally, the rapid growth of social media data greatly intensified the need for the right tools, techniques, and technologies to extract meaningful information from this unstructured data.

Organizations have to deal with the exceptional challenges posed by web-based unstructured content for data analysis, data analytics, and big data. An answer to this problem seems to emerge from mobile devices.

Mobile devices have the technology to analyze behavioral data. It also allows for storing and analyzing location-based data (GPS data). With mobile devices becoming smarter by the day, tracking various aspects of human behavior and arriving at definite conclusions is possible.

With internet speeds improving, making it possible to spew data at exponentially faster rates, the stage was set for the next giant leap in Big Data history.

With the introduction of the World Wide Web and the development of HTML, URLs, and HTTP, access to data became relatively easy and decidedly faster. In 1996, digital storage became an affordable way of storing information compared to storing data on paper. In addition the search engine system took shape in 1997 with the registration of Google as a domain name. The development of several other tech innovations also took place alongside. These included areas of machine learning, big data, and analytics. In 1999, a book published by Hal R. Varian and Peter Lyman made efforts to quantify the volume of digital information available across the globe.

The real big change in Big Data happened in the 21st century. Doug Laney from Gartner coined the term 3Vs of Big Data. He defined how volume, velocity, and variety impacted Big Data. Since then, other Vs., such as veracity, value, and variability, are also used in the Big Data context.

2005 saw the creation of Apache Hadoop, the open-source framework, by scientists Doug Cutting and Mike Cafarella. This framework is used to store and process large data sets. Soon after, in 2006, Amazon Web Services (AWS) began its web-based computing infrastructure services (cloud computing). It is also a dominant name in the current cloud services industry.

Some of the key developments that happened during this era are:

Edge computing is a new technology that defines the data management process for critical sectors of the economy. This kind of computing is done near the source of data collection rather than in the cloud or a centralized data center.

The explosive increase in the use of connected devices, the dependence on the cloud, and the upcoming edge computing revolution have played important roles in the growth of Big Data. Enhanced use of technologies such as Machine Learning, Artificial Intelligence, and IoT analytics have also contributed to the ability to process and analyze data. Over the years, we can expect major developments in Big Data, which will help accelerate the analytics process and boost the efficiency and ease of use of tools for leveraging Big Data.

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A Quick Guide To The History of Big Data - Baseline

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