Category Archives: Cloud Computing
HiveIO Top 5 IT Predictions For 2020 – RTInsights
Five predictions for cloud computing and artificial intelligence in 2020.
We are living in a post-migration era of cloud computing, according to HiveIO VP of Product, Toby Coleridge, who provided RT Insights with a list of five predictions for cloud computing and artificial intelligence in 2020.
Influxof AI in Healthcare
Theethics behind large companies harvesting medical data for artificial intelligencesystems may rage on for another decade, but HiveIO sees healthcareorganizations as the catalysts for this evolution.
As a result of datas increasing perceived value, healthcare organizations will go to greater lengths in collecting data to meet end-user demands in 2020, said Coleridge. By capturing more personal data, healthcare organizations will be able to more accurately assist patients and predict their needs.
SEE ALSO: Cloudwick Teams Up with Pepperdata for Improved AWS Cloud Migration
Coleridgebelieves that synergy between fitness brands, like Fitbit and Apple, andhealthcare organizations will occur in the next five to ten years. Biometricdata will be sent to a doctors office, which may run AI-assisted diagnosticsto recognize any problems at an earlier stage.
Automation of diagnostics will remove a lot of tedious work for the doctor, while also spotting problems quicker. The only issue, currently, is the lack of clear connections between healthcare providers and technology companies, although there are some signs the big four want to break into the healthcare market.
EducatorsUse IT To Meet Student Needs
Digitalnative students require, according to Coleridge: immediate gratification and adeeper level of knowledge and understanding. To meet this demand, educationalfacilities will continue to adopt virtual desktop infrastructure systems, whichallow students to work from remote locations, save the school money in updatingand upgrading systems, and improve security with a centralized interface.
Startingin 2020, we will see a shift in the entire education system and VDI will be akey enabler for this, said Coleridge.
On-Premisevs Cloud: Its Not An Either-Or
While a vast majority of workloads will be processed by data centers, on-premises is still relevant and will remain necessary for some companies.
In2020, we will see the conversation around data storage shift from choosingcloud or on-premise, to deciding which applications an organization should runon-premise, said Coleridge. Its not a matter of selecting one or the other,but rather, determining how both contribute to a comprehensive IT strategy.
Ciscopredicts that over 90 percent of workloads will be processed in the cloud by 2021,but that other 5-10 percent of usage will remain on-premise, and its notlikely that number will fall rapidly in the next few years.
CloudMigration Stage Will Pass
Onereason why that percentage wont drop is that the cloud migration stage isover, according to Coleridge. Cloud migration will decrease in 2020, for thefirst time since analysts predicted major migration.
Thisis because most organizations interested in implementing a cloud strategy havealready done so, said Coleridge. We will now begin to see the migration focuson automation in cloud and edge computing.
HowMuch Can We Store?
Atopic rarely spoken about in the cloud computing world is whats the limit forstorage. More and more data centers are being built around the world, and moreinformation than ever is stored on hard drives, remaining on there for decades.Coleridge expects we will start to see storage constraints in the next five to10 years, which will force the major data storage providers to build tools thatdiscard raw data while keeping primary themes.
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HiveIO Top 5 IT Predictions For 2020 - RTInsights
Adoption of Cloud-Native Architecture, Part 1: Architecture Evolution and Maturity – InfoQ.com
Key Takeaways
Architecture stabilization gaps and anti-patterns can emerge as part of a hasty microservices adoption.
Understanding the caveats and pitfalls of historic paradigm shifts should enable us to learn from previous mistakes and position our organizations to thrive at the latest technology waves.
Its important to know the pros and cons of different architectural styles like monolithic apps, microservices, and serverless functions.
Repeating cycle of architecture evolution: initial stage of not knowing best practices in the new paradigm, which accelerates the technical debt. As the industry develops new patterns to address the gaps, teams adopt new standards and patterns.
Consider the architecture patterns as strategies that favor rapid technological evolution while protecting the business apps from volatility.
Technology trends such as microservices, cloud computing, and containerization have been escalating so quickly in recent years that most of these technologies are now part of the day-to-day duties of top IT engineers, architects, and leaders.
We live in a cloud-enabled world. However, being cloud-enabled does not mean being cloud-native. In fact, its not only possible but dangerous to be cloud-enabled without being cloud-native.
Before we examine these trends and discuss what architectural and organizational changes corporations should implement to take full advantage of a cloud-enabled world, it is important to look at where we have been, where we are, and where we are going.
Understanding the caveats and pitfalls of the historic paradigm shifts should allow us to learn from previous mistakes and position our organizations to thrive on the latest waves of this technology.
As we briefly walk through this evolution, well be exploring the concept of anti-patterns, which are common responses to a recurring problem that are usually ineffective and risk being counterproductive.
This article series will describe the anti-patterns mentioned.
For the last 50 years or so, software architecture and application hosting models have experienced major transformation from mainframes to microservices and serverless.
Figure 1 shows this evolution of architecture models and the paradigms they promoted.
Figure 1: Architecture evolution from mainframe to cloud and microservices
Back in the 70s and 80s, mainframes were the way of computing. Mainframes are based on a centralized data storage and computing model, with basic client terminals used for data entry and data display on primitive screens.
The original mainframe computers used punch cards and most of the computation happened within batch processes. There was no online processing and latency was at 100% as nothing was processed in real time.
Some evolution happened within the mainframe paradigm with the introduction of online processing and user interface terminals. The overall paradigm of a massive central unit of processing contained within the four walls of a single organization still had a "one size fits all" approach, however, and that was only partially able to supply the capabilities needed by most business applications.
Client/server architecture put most of the logic on the server side and some of the processing on the client. Client/server was the first attempt in distributed computing to replace the mainframe as the primary hosting model for business applications.
In the first few years of this architecture, the development community was still writing software for client/server using the same procedural, single-tier principles that they had used for mainframe development, which resulted in anti-patterns like spaghetti code and the blob. This organic growth of software also resulted in other anti-patterns like big ball of mud. The industry had to find ways to stop teams from following these bad practices and so had to research what was necessary to write sound client/server code.
This research effort mapped out several anti-patterns and best-practice design and coding patterns. It introduced a major improvement called object-oriented programming (OOP), which had inheritance, polymorphism, and encapsulation features, along with paradigms to deal with decentralized data (as opposed to a mainframe with one version of the truth) and guidance for how industry could cope with the new challenges.
The client/server model was based on three-tier architecture consisting of presentation (UI), business logic, and data tiers. But most of the applications were written using two-tier models with a thick client encapsulating all presentation, business, and data-access logic, directly hitting the database. Although the industry had started to discuss the need to separate presentation from business from data access, that practice didnt really become vital until the advent of Internet-based applications.
In general, this model was an improvement over the mainframe limitations, but the industry soon ran into its limitations like needing to install the client application on every users computer and an inability to scale at a fine-grained level as a business function.
During mid 90s, the Internet revolution occurred and a completely new paradigm arrived with it. Web browsers became the client software while web and application servers hosted all the processing and logic. The World-Wide Web (www) paradigm promoted a true three-tier architecture with presentation (UI) code hosted on web servers, business logic (API) on application servers, and the data stored in database servers.
The development community started to migrate from thick (desktop) clients to thin (web) clients, driven mainly by ideas like service-oriented architecture (SOA) that reinforced the need for a three-tiered architecture and fueled by improvements to client-side technologies and the rapid evolution of web browsers. This move sped up time to market and required no installation of the client software. But developers were still creating software as tightly coupled designs, leading to jumble and other anti-patterns.
The industry in response came up with evolved three-tiered architectures and practices such as domain-driven design (DDD), enterprise integration patterns (EIP), SOA, and loosely coupled techniques.
The first decade of the 21st century saw a major transformation in application hosting when hosting became available as a service in the form of cloud computing. Application use cases requiring capabilities like distributed computing, network, storage, compute, etc., became much easier to provision with cloud hosting at a reasonable cost compared to traditional infrastructure. Also, consumers were taking advantage of elasticity of the resources to scale up and down based on the demand. They only needed to pay for the storage and compute resources that they used.
The elastic capabilities introduced in IaaS and PaaS allow for a single instance of a service to scale as needed, eliminating duplication of instances for the sake of scalability. However, these capabilities cannot compensate for the duplication of instances for other purposes, such as having multiple versions, or as a byproduct of monolith deployments.
The appeal of cloud-based hosting is that the dev and ops teams no longer had to worry about server infrastructure. It offered three different hosting options:
PaaS became the sweet spot among the cloud options because it allows developers to host their own custom business application without having to worry about provisioning or maintaining the underlying infrastructure.
Even though cloud hosting encouraged modular application design and deployment, many organizations found it enticing to lift and shift legacy applications that had not been designed to work on an elastic distributed architecture directly to the cloud, resulting in a somewhat modern anti-pattern called "monolith hell".
To address these challenges, the industry came up with new architecture patterns like microservices and 12-factor apps.
Moving to the cloud also presented industry with the challenges of managing the application dependencies on third-party libraries and technologies. Developers started struggling with too many options and not enough criteria for selecting third-party tools, and we started seeing some dependency hell.
Dependency hell can occur at different levels:
Library-based dependency hell is a packaging challenge and the latter two are design challenges. A future article in this series will examine these dependency-hell scenarios in more detail and offer design patterns for avoiding the unintended consequences to prevent any proliferation of technologies.
Software design practices like DDD and EIP have been available since 2003 or so and some teams then had been developing applications as modular services, but traditional infrastructure like heavyweight J2EE application servers for Java applications and IIS for .NET applications didn't help with modular deployments.
With the emergence of cloud hosting and especially PaaS offerings like Heroku and Cloud Foundry, the developer community had everything it needed for true modular deployment and scalable business apps. This gave rise to the microservices evolution. Microservices offered the possibility of fine-grained, reusable functional and non-functional services.
Microservices became more popular in 2013 - 2014. They are powerful, and enable smaller teams to own the full-cycle development of specific business and technical capabilities. Developers can deploy or upgrade code at any time without adversely impacting the other parts of the systems (client applications or other services). The services can also be scaled up or down based on demand, at the individual service level.
A client application that needs to use a specific business function calls the appropriate microservice without requiring the developers to code the solution from scratch or to package the solution as library in the application. The microservices approach encouraged a contract-driven development between service providers and service consumers. This sped up the overall time of development and reduced dependency among teams. In other words, microservices made the teams more loosely coupled and accelerated the development of solutions, which are critical for organizations, especially the business startups.
Microservices also help establish clear boundaries between business processes and domains (e.g., customer versus order versus inventory). They can be developed independently within that vertical modularity known as the "bounded context" in the organization.
This evolution also accelerated the evolution of other good practices like DevOps, and it provided agility and faster time to market at the organization level. Each development team would own one or more microservices in its domain and be responsible for the whole process of designing, coding, deploying to production as well as post-production support and maintenance.
However, similar to the previous architecture models, the microservices approach ran into its own issues.
Legacy applications that had not been designed as microservices from bottom-up started being cannibalized in attempts to force them into a microservices architecture, leading to the anti-pattern known as monolith hell. Other attempts tried to artificially break monolithic applications into several microservices even though these resulting microservices were not isolated in terms of functionality and still heavily depended on other microservices broken out of the same monolithic application. This is the anti-pattern called microliths.
It's important to note that monoliths and microservices are two different patterns, and the latter is not always a replacement for the former. If we are not careful, we can end up creating tightly coupled, intermingled microservices. The right option depends on the business and scalability requirements of an applications functionality.
Another undesired side effect of the microservices explosion is the so-called "Death Star" anti-pattern. Microservices proliferation without a governance model in terms of service interaction and service-to-service security (authentication and authorization) often results in a situation where any service can willy-nilly call any other service. It also becomes a challenge to monitor how many services are being used by different client applications without decent coordination of those service calls.
Figure 2 shows how organizations like Netflix and Twitter ran into this nightmare scenario and had to come up with new patterns to cope with a "death by Death Star" problem.
Figure 2: Death Star architectures due to microservices explosion without governance
Although the examples depicted in figure 2 might look like extreme cases that only happen to giants, do not underestimate the exponential destructive power of cloud anti-patterns. The industry must learn how to operate a weapon that is massively larger than anything the world has seen before. "Great power involves great responsibility," said Franklin D. Roosevelt.
Emerging architecture patterns like service mesh, sidecar, service orchestration, and containers can be effective defense mechanisms against malpractices in the cloud-enabled world.
Organizations should understand these patterns and drive adoption sooner rather than later.
With the emergence of cloud platforms, especially the container orchestration technologies like Kubernetes, service mesh has been gaining attention. A service mesh is the bridge between application services that adds additional capabilities like traffic control, service discovery, load balancing, resilience, observability, security, and so on. It allows the applications to offload these capabilities from application- level libraries and allows developers to focus on business logic.
Some service mesh technologies like Istio also support features like chaos injection so that developers can test the resilience and robustness of their application and its potentially dozens of interdependent microservices.
Service mesh fits nicely on top of platform as a service (PaaS) and container as a service (CaaS), and enhances the cloud-adoption experience with the above-mentioned common platform services.
A future article will delve into the service-mesh-based architectures with discussion on specific use cases and comparison of solutions with and without service mesh.
Another trend that has received a lot of attention in the last few years is serverless architecture, also known as serverless computing. Serverless goes a step further than the PaaS model in that it fully abstracts server infrastructure from the application developers.
In serverless, we write business services as functions and deploy those functions to the cloud infrastructure. Some examples of serverless technologies are Amazon Lambda, Spring Cloud Function, Google Cloud Functions, and Microsoft Azure Functions.
The serverless model sits in between PaaS and SaaS in the cloud-hosting spectrum, as shown in the diagram below.
Figure 3: Cloud computing, containers, service mesh, and serverless
In a similar conclusion to the discussion of monolithic versus microservices, not all solutions should be implemented as functions. Also, we should not replace all microservices with serverless functions just like we shouldnt replace or break down all of monolithic apps into microservices. Only the fine-grained business and technical functions like user authentication or customer notification should be designed as serverless functions.
Depending on our application functionality and non-functional requirements like performance and scalability and the transaction boundaries, we should choose the appropriate monolith, microservices, or serverless model for each specific use case. Its typical that we may need to use all three of these patterns in a solution architecture.
If not designed properly, serverless solutions can end up becoming nanoliths, where each function is tightly coupled with other functions or microservices and cannot operate independently.
Complementary trends like container technologies came out around the same time as microservices to help with deploying the services and apps in microserver environments that offered true isolation of business services and scalability of individual services. Container technologies like Docker, containerd, rkt, and Kubernetes can complement the microservices development very well. Nowadays, we cannot mention one - microservices or containers - without the other.
As mentioned earlier, its important to know the pros and cons of the three architectural styles: monolithic apps, microservices, and serverless functions. A written case study on monolith versus microservices describes in detail one decision to avoid microservices.
Table 1 highlights the high-level differences between these three options.
Note: Sometimes teams artificially break down related functions into microservices and experience the limitations of microservices model.
Application is completely shut down when there's no traffic.
Dev teams don't have to care about underlying infrastructure.
Table 1: Service architecture models and when to use or avoid them
Its important for us to keep an eye on the anti-patterns that may develop in our software architecture and code over time. Anti-patterns not only cause technical debt but, more importantly, they could drive subject-matter experts out of the organization. An organization could find itself with only the people who dont bother about the architecture deviations or anti-patterns.
After the brief history above, lets focus on the stabilization gaps and anti-patterns that can emerge as part of a hasty microservices adoption.
Specific factors like the team structure in an organization, the business domains, and the skillsets in a team determine which applications should be implemented as microservices and which should remain as monolith solutions. But we can look at some general considerations for choosing to design a solution as a microservice.
The Eric Evans book, Domain-Driven Design (DDD), transformed how we develop software. Eric promoted the idea of looking at business requirements from a domain perspective rather than from one based on technology.
The book considers microservices to be a derivation of the aggregate pattern. But many software development teams are taking the microservices design concept to the extreme, by attempting to convert all of their existing apps into microservices. This has led to anti-patterns like monolith hell, microliths, and others.
Following are some of the anti-patterns that architecture and dev teams need to be careful about:
Well look in more detail at each of these anti-patterns in the next article.
To close the stabilization gaps and anti-patterns found in different application hosting models, the industry has come up with evolved architecture patterns and best practices to close the gaps.
These architecture models, stabilization gaps and patterns are summarized in the table below.
Connected/shared
Table 2: Application hosting models, anti-patterns, and patterns
Figure 4 shows all these architecture models, the stabilization gaps in the form of anti-patterns, and the evolved design patterns and best practices.
Figure 4: Architecture evolution and application-hosting models
Figure 5 lists the steps of architecture evolution, including the initial stage of not knowing the best practices in the new paradigm, which accelerates the technical debt. As the industry develops new design patterns to address the stabilization gaps, teams adopt the new standards and patterns in their architecture.
Figure 5: Architecture models and adoption of new patterns
IT leaders must protect their investment against the ever-growing rapid transformation of technologies while providing a stable array of business applications running on a constantly evolving and optimizing technological foundation. IT executives across the globe have been dealing with this problem more and more frequently.
They and we should embrace the evolution of technology but not at the price of constant instability of the apps supporting the business.Disciplined systematic architecture should be able to deliver just that. Consider the patterns discussed in this article series as strategies that favor rapid technological evolution while protecting the business apps from volatility. Lets explore how that can be done in the next article.
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Adoption of Cloud-Native Architecture, Part 1: Architecture Evolution and Maturity - InfoQ.com
Cloud, AI, and personalisation: Key issues to consider – www.computing.co.uk
According to analysts at IDC, worldwide spending on AI systems will hit $35.8 billion by the end of 2019, a year-on-year increase of 44 percent. Much of that growth will come from applications of AI in the cloud and online, because of what IDC calls a "natural, evolutionary symbiosis between AI and the internet".
However, parallel to that growth are rising public concerns over a broad range of related issues: privacy, transparency, liability, security, bias, and the unknown workings of so-called black box' solutions.
This is partly due to public worries over the security of their private data on platforms such as Facebook - especially in the wake of the Cambridge Analytica scandal. Those concerns have hardly been helped by more recent stories, such as the November 2019 news that the Facebook mobile app has been tracking users' faces as they look at their feeds.
Policy matters
At a recent Westminster eForum event on UK AI policy and skills in London, one speaker raised his own concerns about the extent to which our lives are becoming influenced and managed by offshore algorithms, largely written in Silicon Valley and run in Californian data centres, such as Facebook's, Google's, or Amazon's.
These algorithms increasingly make decisions about what we watch and read, based on our previous likes and dislikes, implying that we are being streamed like primary school children into different groups - often without us knowing - in order to sell us advertising.
That speaker was none other than Roger Taylor, Chair of the government's new Centre for Data Ethics and Innovation. He said, "We now have a media world in which anyone can put something out there and a Californian algorithm decides whether or not to distribute it to every household, or only to certain households, in our country. And there is no mechanism in that process where anybody has any degree of real social accountability."
Despite the UK's Data Protection Act 2018 and Europe's General Data Protection Regulation (GDPR), which came into force in May 2018, nearly half of UK consumers (48 percent) have no idea how brands are using their data, according to a recent survey by the Chartered Institute of Marketing (CIM).
As a result, canny enterprises are beginning to recognise that ethics, data protection and consumer rights could be real competitive differentiators in terms of winning users' loyalty and trust.
A personal approach?
Personalisation is part of this particular knot of challenges. While personalising content, such as information feeds, to individual users might be useful or help to create a more direct or loyal relationship between a service and its users, it may screen out other data that might have been of equal interest to that person.
More, personalisation implies underlying trust, privacy, and ethical concerns: clearly a platform is learning about each user, but what does it do with that data? Who is it shared with? And to what end?
As the Internet of Things grows, with greater intelligence, AI, and inference abilities being embedded into smart devices, those fears can only deepen. For example, last year a Consumers Association survey found that one smart TV sent information to 700 different IP addresses in just 15 minutes - invisibly to the user.
The personal enterprise
But what about the use of cloud services and AI within the enterprise itself? A Computing Research survey of 150 IT leaders across every type of medium to large enterprise in the UK, found that access to AI and automation capabilities were either a major or significant motivation for shifting back-office applications into the cloud for 64 percent of respondents. A further 19 percent regarded it as important.
Nearly as many respondents said that AI and automation access had been achieved either extremely or very successfully in the cloud, with a further 25 percent indicating some success.
Gaining customer and employee insights are of similar importance to respondents, according to the survey. Customer insights were cited as major or significant motivations by over 60 percent of IT leaders, with 20 percent indicating some importance. Meanwhile, 57 percent acknowledged a major or significant motivation in gaining employee insights, with 23 percent seeing this as important.
However, when it came to moving back-office functions such as Finance, Accounting, and Human Capital Management (HCM) into the cloud, personalisation was not a massive driving factor for IT leaders, found the Computing survey.
Fifty-eight percent of respondents identified it as either very important or important, but those figures were significantly smaller than the responses for business insights, reliability, customer service, the applications always being up to date, security, or the overall user experience, among other factors.
This article is from Computing'sCloud ERP Spotlight, hosted in association withWorkday.
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Cloud, AI, and personalisation: Key issues to consider - http://www.computing.co.uk
Cloud computing IaaS in Life Science Market Global Industry Demand, Scope and Strategic Outlook,Growth Analysis,Business Opportunities and Future…
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Cloud computing IaaS in Life Science Market Global Industry Demand, Scope and Strategic Outlook,Growth Analysis,Business Opportunities and Future...
Global Medical Device Security Solutions Market 2020-2024 | Increasing Demand for Cloud-Based Solutions to Boost the Market Growth | Technavio -…
LONDON--(BUSINESS WIRE)--The global medical device security solutions market size is expected to grow by USD 301.04 million during 2020-2024, according to the latest market research report by Technavio. Request a free sample report
Healthcare organizations are gradually moving toward creating a connected hospital infrastructure with the aid of IoT to provide timely and improved care. IoT is increasingly leveraged in the healthcare industry through various applications, including telemedicine, connected imaging, medication management, and inpatient monitoring. However, the increasing use of connected medical devices coupled with increasing deployment of IoT has made computer systems more vulnerable to cybersecurity threats. This is prompting stakeholders in the healthcare sector to increase their focus on improving network security and forming robust healthcare IT infrastructure. Thus, with the growing adoption of IoT and connected devices in healthcare industry, the demand for medical device security solutions is expected to rise considerably during the forecast period.
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Medical Device Security Solutions Market: Increasing Demand for Cloud-based Solutions
The deployment of cloud computing in the healthcare industry has increased considerably in recent years as it offers business agility, privacy, and security at lower costs. Cloud computing quickens the access of electronic medical records and enables the storage of clinical statistical data related to hospitals and clinics. Furthermore, factors including the rising need to comply with regulations, growing penetration of high-speed networks, and rising digital awareness are increasing the adoption of cloud-based solutions in the healthcare sector. With more healthcare organizations upgrading to cloud-based systems, the demand for cloud-based medical device security solutions is anticipated to rise considerably during the forecast period.
Growing concerns about healthcare data, stringent government regulations, and rising demand for self-medication and homecare medical devices are expected to boost the medical device security solutions market growth during the forecast period, says a senior analyst at Technavio.
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This market research report segments the medical device security solutions market by device (wearable and external medical devices, hospital medical devices, and internally embedded medical devices) and geography (APAC, Europe, North America, MEA, and South America).
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How much cloud does an IT disaster recovery plan need? – TechTarget
To avert disaster, organizations look at all sorts of combinations of cloud-based and on-premises resources. It's understandable to an extent, but every move to advance your resiliency comes with complications, costs and catches.
A solid IT disaster recovery plan will almost certainly include some coverage from a cloud provider. Valuable information and mission-critical applications in your data center are protected to whatever degree your organization can keep them safe. And that will be fine -- until it's not. In a moment of crisis, where do you turn? Unless you've got a reasonably sophisticated second data center elsewhere, the business is in considerable peril.
Those moments of emergency are why cloud computing is so appealing for disaster recovery (DR), as cloud expert Brian Kirsch explains in this handbook's lead article. The idea is that you sync your data and have some VMs ready to go in an environment managed by a trusted cloud provider. If your data center fails, then those cloud-based resources come riding to the rescue -- right?
Kirsch explains that this premise is correct, but only if you've gone to the trouble -- and the expense to do things properly. Your IT disaster recovery plan needs to be thorough enough that you are sure your data is not just protected from whatever afflicted your on-premises environment, but also quickly recoverable from its off-premises safe haven. Doing this is neither simple nor cheap.
DR in the cloud is possible, and, in most cases, it is perfectly sensible. Just don't expect it to be easy.
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How much cloud does an IT disaster recovery plan need? - TechTarget
Global Healthcare Cloud Computing Market 2018-2022 | Introduction of Blockchain in Cloud Computing to Boost Growth | Technavio – Business Wire
LONDON--(BUSINESS WIRE)--The global healthcare cloud computing market is expected to post a CAGR of close to 22% during the period 2018-2022, according to the latest market research report by Technavio. Request a free sample report
Research collaborations have been increasing considerably in recent years, particularly, in the field of healthcare. Healthcare establishments and organizations prompting research initiatives require systems with high computational capabilities. Deploying cloud computing in healthcare ecosystems offers various advantages including cost savings, enhanced flexibility, and system scalability to the organizations. Furthermore, the use of cloud computing also facilitates better collaborative research among various healthcare researchers and other stakeholders. The cloud computing modules designed for the healthcare ecosystem help healthcare professionals in making precise decisions for prescribing appropriate medications to their patients. Thus, growing collaborations among different stakeholders of the healthcare industry will drive the healthcare cloud computing market.
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As per Technavio, the introduction of blockchain in cloud computing will have a positive impact on the market and contribute to its growth significantly over the forecast period. This research report also analyzes other important trends and market drivers that will affect market growth over 2018-2022.
Global Healthcare Cloud Computing Market: Introduction of Blockchain in Cloud Computing
Rising deployment of cloud computing systems in the healthcare industry has resulted in an increase in data and information theft, resulting in cybersecurity issues. However, implementation of blockchain in healthcare IT infrastructure will help in achieving greater data security, streamlining claims, managing the billing process, and ensuring integrity within drug supply chain and health research. In addition, blockchain-enabled systems also help in reducing breaches during data exchange and offering greater ownership to the patients about their data and records. As a result, with the growing awareness of benefits offered by blockchain technology, vendors in the healthcare industry are collaborating with cloud computing companies to develop blockchain-based healthcare management systems.
Some other major factors such as the introduction of edge computing, integrated service offerings for healthcare industry, and increasing number of cloud vendors, and development of hyper-converged infrastructure (HCI) will boost market growth during the forecast period, says a senior analyst at Technavio.
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Global Healthcare Cloud Computing Market: Segmentation Analysis
This market report segments the global healthcare cloud computing market by product (SaaS, IaaS, and PaaS) and geography (Americas, APAC, and EMEA).
Americas led the market in 2017, followed by EMEA and APAC respectively owing to the increased adoption of cloud computing technologies in the healthcare sector in the region and strict government regulations like HIPAA. Growing partnerships among the stakeholders of the healthcare sector and cloud computing companies, particularly, in Canada and Latin American economies will further lead the region to account for the highest incremental growth during the forecast period.
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Some of the key topics covered in the report include:
Market Landscape
Market Sizing
Five Forces Analysis
Market Segmentation
Geographical Segmentation
Market Drivers
Market Challenges
Market Trends
Vendor Landscape
About Technavio
Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.
With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.
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Global Healthcare Cloud Computing Market 2018-2022 | Introduction of Blockchain in Cloud Computing to Boost Growth | Technavio - Business Wire
Alibaba Is Taking the Cloud Battle to Amazon – Investopedia
Alibaba Group Holdings Inc. (BABA), the e-commerce giant known as the "Amazon of China," is poised for explosive growth and set to become a dominant player in cloud-computing services, a league currently dominated by Google parent Alphabet Inc. (GOOGL), Amazon.com Inc. (AMZN), and Microsoft Corp. (MSFT). And a new study by Forrester predicts the companys cloud unit will surpass Google Cloud next year as the third biggest competitor in the global cloud market, according to a recent story in Business Insider.
Alibaba, with amarket capof $484 billion, currently dominates the online retail market in China, the worlds second largest economy and most populous country with around 1.4 billion people. The expected rapid pace of growth of the e-commerce company will not only put it ahead of Google in the market for cloud services, but will put it into direct competition for global customers with Amazon and Microsoft.
When we say that Alibaba is threatening Google for the third post, we believe in 2020 Alibaba will make more money than Google will, Forresters vice president and principal analyst Dave Bartoletti told Business Insider.
The Forrester report predicts that Alibabas cloud infrastructure unit, first formed in 2009, will bring in $4.5 billion in revenue next year. While Googles reported annual revenuerun ratefor its cloud business is $8 billion, that figure is a combination of revenue from both its cloud infrastructure unit and its G Suite productivity software. Solely based on infrastructure, Alibaba is expected to replace Google as the third most dominant player in global cloud services.
Alibaba has not always been the most innovative of big tech companies and has propelled itself forward by largely being adept at imitating the innovations of its competitors. However, things may be starting to change. Nowadays, innovation is happening everywhere in the China market, and Alibaba Cloud has become one of the most important platforms to support business-driven innovation, especially based on the Internet, vice president of Canon Ehara Taisei toldBarrons in September.
But regardless, one of the major sources of Alibabas strength is its dominance in the biggest marketplace in the worldChina. The company raked in total revenues of more than $56 billion in its most recent fiscal year andcomprises about two-thirdsof the online-retail market share in China. When it comes to cloud computing, demand in China is only growing, and Alibaba is the household name for such services.
They are the leading public cloud provider in China, which is a very big market, said Bartoletti. They have a lot of people using their services there. They are doing well financially. They have money to invest in build out. They are doing a good job of being a fast follower.
While financially strong, another thing that will help Alibaba to invest in further growth is Alibabas latest round of share issuance for its secondary listing in Hong Kong next week. The secondary offering wasoversubscribedwith proceeds of the sale amounting toroughly $13 billion, a pile of cash that could help Alibaba grow its cloud business to Amazon-like proportions.
But while Alibaba may be starting to shift its focus on the two frontrunners in the cloud space, Google wont give up its third place spot without a fight. Google Cloud is likely to maintain its dominance with domestic customers as Alibaba has a much smaller presence in North America. Google could also gain ground if it shifts some of its attention to expanding abroad in European markets.
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Alibaba Is Taking the Cloud Battle to Amazon - Investopedia
Video Streaming Platforms And The Benefits Of Cloud – Forbes
Video streaming has come a long way from the old days when transferring video wasnt even possible on the Internet. Today, businesses all over the world use video streaming as a tool for marketing and communication, as well as a modern means of providing entertaining or educational content.
The advancements in cloud computing have revolutionized video streaming and brought forth massive corporations and popular streaming platforms such as YouTube and Netflix. Thanks to successful video streaming startups, even small companies can leverage the benefits of video streaming in their business.
Video Streaming and Cloud
Video streaming platforms have adopted cloud scaling in order to enable larger bandwidth and speed. These factors are necessary to handle heavier video requirements and provide a better viewing experience.
One of the greatest examples of a platform that scaled massively on cloud to provide a better video viewing experience is YouTube. Thanks to the advancement in cloud technology, YouTube was able to reach millions of people and become the second largest search engine online.
Nowadays, even small businesses use video streaming to bring their customers closer to the brand and make them feel more connected to their mission in the industry. This additional human interaction can take their marketing strategies to the next level.
Cloud computing symbol, random lines creating cloud shape, 3D illustration of cloud technology, ... [+] internet of things
While video streaming is considered a powerful marketing tool, it comes with several challenges in terms of technological requirements. Video streaming includes the transmission of large data pockets that results in latency issues the frustrating buffering that ruins any viewers experience.
By scaling on cloud, streaming platforms can increase their bandwidth to provide better video streaming performance and viewing experience. Certain startups provide video streaming services to businesses that want to leverage this tool but dont have the required interface.
Since most enterprises dont have networks capable of handling video streaming and heavy traffic, there is a large demand for cloud scaling and video streaming platforms. Here are three startups worth mentioning that have used cloud to scale and increase their capabilities for purposes such as video delivery, live streaming, and gaming:
Whether were talking about gaming, streaming, or live video, cloud computing plays an important role in providing a high quality experience. With the focus being on the end users experience, video streaming platforms will continue adopting cloud technologies to provide better results.
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Video Streaming Platforms And The Benefits Of Cloud - Forbes
Cloud Computing Market Expected to Grow at 623.3 Billion In Revenue by 2023 – Hitz Dairies
A latest published report on Cloud Computing Market delivering key insights and providing a competitive advantage to clients through a detailed report. The report contains 174 pages which highly exhibit on current market analysis scenario, upcoming as well as future opportunities, revenue growth, pricing and profitability. An exclusive data offered in this report is collected by research and industry experts team.
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The Global Cloud Computing Market size is expected to grow from US$ 272.0 Billion in 2018 to US$ 623.3 Billion by 2023, at a Compound Annual Growth Rate (CAGR) of 18.0% during the forecast period. Increased automation and Agility need for delivering enhanced customer experience, and increased cost savings and return on investment are the major growth factors for the cloud computing market.
Key Players- AWS (US), Microsoft (US), Google (US), Alibaba (China), SAP (Germany), IBM (US), Oracle (US), VMware (US), Rackspace (US), Salesforce (US), Adobe (US), Verizon (US), CenturyLink (US), Fujitsu (Japan), NTT Communications (Japan).
The key features of IaaS include automated administrative tasks, dynamic scaling, platform virtualization, and network connectivity. IaaS enables enterprises to leverage their IT infrastructure without paying for the construction of the physical infrastructure. Moreover, it provides flexibility, mobility, easy, and scalable access to applications, and enhanced collaboration to help enterprises focus on their core businesses.
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The retail and consumer goods vertical is one of the fastest-growing verticals with respect to the adoption of emerging and innovative technologies, such as cloud computing, big data analytics, DevOps, digital stores, and social networks. Various factors driving this adoption are the rising purchasing power of customers and the need to satisfy customer expectations leading to the existing customer retention and new customer acquisition.
North America is the most mature market in terms of cloud computing services adoption, due to several factors, such as the presence of many enterprises with advanced IT infrastructure, and availability of technical expertise. APAC is expected to offer significant growth opportunities for cloud computing vendors during the forecast period. Rapid advancements in emerging technologies, IT infrastructure services, and the Internet of Things (IoT) have led many organizations to adopt cloud computing services.
Breakdown of primary participants profile:
The Study Objectives of this report are:
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Reason to buy this report:
The report will help the market leaders/new entrants in the cloud computing market with information on the closest approximations of the revenue numbers for the overall cloud computing market and the sub segments. The report will help stakeholders understand the competitive landscape and gain more insights to better position their businesses and to plan suitable go-to-market strategies.
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Cloud Computing Market Expected to Grow at 623.3 Billion In Revenue by 2023 - Hitz Dairies