Category: Cloud

The medical industry is rapidly evolving and becoming more technologically inclined. Therefore, building an interconnected healthcare system to adapt to these changes has proven to be complex and elusive. Notwithstanding, maintaining a seamless connection between data insights, care teams, and patients has remained invaluable in offering quality medical care to patients.

As a result, modern innovative health institutions need a solution that can help them stay abreast with the evolving healthcare market while offering customized healthcare services to their patients. And one of the best clouds for medical solutions is known as the Microsoft Cloud for healthcare. This cloud solution allows medical institutions to evolve with changing trends while prioritizing healthcare data security, patient access, and interoperability. It also allows patients to customize their medical care journey by enabling them to stay connected with their care providers in real-time.

But what is Microsoft cloud for healthcare? Stay with us as we take you on a journey to discovering the key elements of MS cloud for healthcare.

What is Microsoft Cloud for Healthcare?

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Microsoft Cloud for Health is a platform that offers innovative tools that help health institutions optimize patient engagement, enable collaboration among health teams, and accelerate clinical and operational data insights. As a result, Microsoft cloud for health makes it simpler to deliver effective personalized care while ensuring that health institutions uphold the standards of security and compliance with patient health data.

What more? The Microsoft Cloud for Healthcare allows medical institutions to sync their clinical and business needs. Thus making it easier for them to deploy faster, attain digital transformation, and better plan their patient’s future.

But how is it possible? Through combining the powers of Azure, Power Platform, Dynamics 365, and Microsoft 365, Microsoft Cloud for Healthcare taps into the potential of the MS cloud to revolutionize the patient experience by making it more accessible and safe.

THE APP SOLUTIONS – CUSTOM HEALTHCARE SOFTWARE DEVELOPMENT COMPANY

Top 4 Benefits of Microsoft Cloud for Healthcare

The easiest way to understand how Microsoft Cloud Solution can help health organizations deliver better medical care experiences, insight, and care management is to peep through the benefits of Microsoft Cloud for Healthcare. Below, we will scan the top 4 benefits of Microsoft services for the medical industry.

Of course, the medical world is evolving rapidly, but with Microsoft Cloud for healthcare, any healthcare solution customer can catch up with changing trends without breaching health data standards. In addition, Microsoft Cloud for Healthcare offers services that can help healthcare providers to achieve enhanced patient monitoring and engagement.

Microsoft Cloud solutions like patient outreach tools, Patient Service Center services, virtual health, and patient portals help you provide your care teams with the support, innovations, and tools needed to provide advanced and personalized care management to patients.

The Microsoft Cloud for healthcare also provides virtual health services. This solution helps healthcare providers to improve patient care. It also allows customers of healthcare solutions to achieve remote patient monitoring and take the delivery of personalized care management service a step further. Care teams can offer high-quality, personalized, and affordable consultations via video conferencing, audio conferencing, and screen sharing. 

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Connect data from many systems to generate insights that can be used to anticipate health risks and enhance patient care, quality control, and operational effectiveness. Medical institutions can alter health outcomes by leveraging data-driven insights to enhance clinical judgment and improve patient experiences. Also, deploying Microsoft Cloud for Healthcare allows medical institutions to effectively coordinate management efforts between clinicians and administrators. Thus increasing operational efficiency. Furthermore, by making provisions for consolidating health information on a single, secure data platform, Microsoft cloud can make data governance and compliance easier.

To fully understand how Microsoft Cloud for Healthcare help improve clinical and operational insights take a look at the following:

We all know that Microsoft cloud for health helps connect protected health data from different sources to help enhance patient care. But it won’t hurt if we look into some solutions that make this possible.

• Azure Health Data Services: this solution consolidates data that originating from sources like clinical, imaging devices, and unstructured data by relying on the Fast Health Interoperability Resources (FHIR) and DICOM services, as well as the Azure IoT Connector for FHIR. To get the depths of this solution, you may visit Azure Health Data Services for additional details.

• Patient population dashboard (preview): This solution summarizes the significant indicators for different patient population categories. Assess the health of your patient population, customize the dashboards using Power BI for your organization’s requirements, and effortlessly integrate these dashboards into your Dynamics 365 apps.

• Another solution worth mentioning is Text Analytics for health.

COMPUTER VISION FOR HEALTHCARE

Microsoft cloud offers operational analytics using the following programs:

• Azure Health Data Services: this solution allows customers to consolidate and regularize data originating from different channels like clinical, imaging, and unstructured data. This process is achieved using FHIR and DICOM services and Azure IoT Connector for FHIR.

• Health Document Intelligence: this solution allows customers and partners to leverage Azure Form Recognizer to develop their solutions. This will allow them to extract data from medical papers and photos to automate and automate workflows, improve knowledge mining, and more. Check out What is Azure Form Recognizer to get more depth.

• Provider Data Model: This tool is known as the HL& FHIR standard-based data model. And it accompanies all Microsoft Cloud for Healthcare solutions. It is also offered as an independent system on which other businesses can create or improve their medical applications.

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Microsoft cloud for healthcare offers medical institutions and care providers solutions that improve patient engagement. This is achieved by providing solutions that help them manage the plethora of patient data, improve communications, improve clinical and operational insights, and improve patients’ health outcomes. Microsoft cloud for health also facilitates real-time data transfer between different services while ensuring end-to-end data security.

Data management solutions make it possible to integrate with Electronic Health Records (EHR) and Electronic Medical Records (EMR) systems to guarantee that health information is kept safe and available across all services.

PUBLIC VS. PRIVATE VS. HYBRID CLOUD COMPUTING

To begin with, the Microsoft cloud for healthcare provides solutions that allow caregivers to improve patient care by doing the following:

• Make customized treatment routines

• Supervise the care teams

• Coordinate care management

• Organize and schedule home visits 

• Manage care team members

CLOUD SERVICE MODELS EXPLAINED: PAAS VS. SAAS VS. IAAS VS. DBAAS

Additionally, care providers can adjust Microsoft collaboration tools to fit into their medical team’s care management requirements. Furthermore, health institutions can utilize Microsoft cloud to sync a patient record from several other sources to achieve a holistic patient history that will offer the care team a complete picture of a patient’s health.

WHAT IS A LIFT AND SHIFT CLOUD MIGRATION?

Set up and Configure Microsoft Cloud for Healthcare

Microsoft cloud for healthcare comprises solutions developed on capabilities within Microsoft Dynamics 365, Microsoft 365, Microsoft Azure, and Microsoft Power Platform. To set up Microsoft Cloud for your institutions, you must deploy these solutions on Microsoft Cloud Solution Center. 

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Microsoft’s Solution Center offers a central location that allows health institutions and other partners to set up industry-specific cloud solutions, including those that are a part of Microsoft Cloud for Healthcare. To get started, the following requirement must be met.

• Intending healthcare organizations and partners who wish to set up Microsoft Cloud for Healthcare must be a tenant admin, Dynamics 365 admin, or power platform admin.

• Intending medical institutions and partners must have the requisite license for the Microsoft Cloud for Healthcare solution and app they wish to deploy.

• Intending medical care institutions and partners must be aware of Microsoft Cloud for Healthcare’s compliance standards and ensure strict compliance.

HOW THE HEALTHCARE INDUSTRY BENEFITS FROM HYBRID CLOUD SOLUTIONS

If you are considering deploying Microsoft Cloud Solution for your health organization, here is how to go about it.

• Sign in to the solution center and click on solution > Healthcare.

• Click on Quick view to learn more about the solution and the dependencies required to deploy it.

• Select the solution you wish to deploy. After selecting your preferred solution, you will see two distinct types of deployment choices– Add and Go to set up.

Add: this option is available for solutions powered by Dynamics 365. The solution will be deployed in the background as the Solution Center walks you through the process. You can click here to learn more about this deployment option.

Go to set up: This feature is available for solutions powered by Microsoft 365 and Azure. Based on your preferred solution, you will get directed to the Azure portal or site for setup or the Microsoft 365 admin center. Click here to learn about this deployment channel.

Notwithstanding the deployment method listed above, some solutions will require configuration before they can be used. Others possess extra capabilities that you can set up to enhance the solution. If you wish to learn how to configure Microsoft solutions for the healthcare industry, click here.

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Conclusion

Digitalization has made it a constant for health service providers to perpetually be on their toes if they wish to stay abreast with the latest technological innovations. However, Microsoft Cloud for Healthcare has stepped in to breach the gap by providing a platform through which medical institutions can provide personalized care management flow for their patients. By utilizing Microsoft Cloud for Healthcare, medical teams can access data from different sources, analyze them and employ the same towards achieving enhanced patient engagement.

In addition, patients do not have to go through the rigors of fixing appointments with their caregivers. Neither do they have to be subjected to the rigors of standing in long queues to meet their caregivers. Through the solutions offered by Microsoft Cloud, patients can now access virtual visits and book virtual appointments from the comfort of their homes.

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If you seek more guidance on integrating Microsoft Cloud solutions into your health systems, feel free to contact us. We will be glad to partner with you in achieving your business vision.

Cloud computing has become an important part of modern businesses, and the healthcare industry is no exception. Healthcare providers must use cloud-based solutions if they want to keep up with the fast changes in the industry and give patients the best care possible. When used in the medical field, it significantly affects how information is stored, retrieved, and shared. It can also help hospitals save money, increase speed and agility, and provide better care to patients.

By 2027, the global market for cloud computing in healthcare will be worth $42.21 billion. This growth is likely driven by the growing need for healthcare organizations to store and analyze data and the growing number of healthcare organizations that use cloud solutions.

MICROSOFT CLOUD FOR HEALTHCARE: HOW MS CLOUD SOLUTIONS ARE BENEFITING HEALTHCARE ORGANIZATIONS

In some way, more than 83% of healthcare organizations are already using cloud computing, according to a survey done by HIMSS. The survey also found that medical firms are using cloud-based data storage solutions more to improve patient care, lower operational costs, and make their work more efficient. According to a study, global spending on cloud services will increase from $494.7 billion in 2022 to nearly $600 billion by the end of 2023. 

But why are healthcare organizations moving to the cloud? Keep reading this article; we will discuss cloud computing, its use in healthcare, its benefits, various types and platforms, and how it impacts the healthcare industry. Let’s jump right into it!

What is Cloud Computing?

The term “cloud computing” is often used to make computing resources, such as data storage and processing power, available on demand through a network of remote servers. The “cloud” is a network of remote data centers that can be accessed through the internet and used by many people simultaneously.

Cloud computing makes it easy to store documents in a central place where people can access them from any device at any time. In the past, you had to be at a certain place to use software and apps stored on a computer or server. With the help of cloud solutions, users can now access their data and apps through the web. 

Ins and Outs of Cloud Computing in Healthcare

When we talk about “cloud-based healthcare,” we’re referring to the practice of using solutions based on the cloud to develop and administer healthcare services. In contrast to the traditional method of setting up data centers on-site to store data on individual computers, this method gives healthcare stakeholders various ways to access the data servers from a distance. Also, this is very helpful for big and small health organizations because it lets them store data safely away from their main office.

Real-World Illustrations of How Cloud Computing is Transforming Healthcare

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Cloud computing can help providers improve patient care by assisting them in keeping up with the latest advances in medicine and technology. Here are ten examples of how cloud computing is used in healthcare:

Thanks to cloud and telehealth services, patients can get clinical care no matter where they are. Telemedicine projects, like telemedicine apps, telesurgery, etc., can use cloud computing as the backbone of their information and communication technology. Also, doctors and healthcare stakeholders can talk to each other and share their knowledge to treat more complex and challenging conditions. In the field of telemedicine, cloud-based solutions can be used for the following:

• Real-time, cross-border exchange of patients’ medical records
• Accessing the stored information at a time and location of their choosing
• Saving time and money by reducing pointless doctor visits

In the drug discovery and recovery process, much computing power is needed to sort through trillions of chemical structures and find promising compounds. The many IaaS services available in the cloud make this process much easier and faster. Several joint ventures, like the one between Newcastle University, Molplex, and Microsoft Research, have used IaaS to help find new drugs, thus saving a lot of time and money.

The healthcare sector has adopted management information systems to improve internal and external communication and serve patients better in several ways. Some of these ways include improving querying services, billing and finances, and the management of human resources. 

Because the data in this system is sensitive, the developers build, test, and put it into use using cloud-based platform services. With the help of cloud technology, the system can be constructed quickly and encourage teamwork. It can also be more easily connected to other healthcare systems.

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Health firms can also use the cloud to manage patient health records (PHR) and electronic health records (EHR). Patients usually keep their health records (PHR), while managed-care organizations and single hospitals each keep their electronic health records (EHR) and electronic medical records (EMR). Thanks to cloud computing, it is now easier to control who has access to which resources and how.

The clinical decision support system (CDSS) is a highly developed program that acts and thinks like a doctor to help doctors analyze patient records. It is an expert system that models its recommendations on the knowledge and actions of a practicing doctor who has reviewed the patient’s medical records. 

With the development of fitness trackers and smartphones with biometric sensors for monitoring vitals like heart rate, blood pressure, and glucose levels, these cloud-based systems are helpful for real-time diagnosis. In addition, health providers can use CDSS for making diagnoses and writing prescriptions.

The adaptability and cost-effectiveness of cloud technology have made it a useful tool in the classroom. Medical students, doctors, professionals, and researchers are increasingly turning to cloud-based libraries to keep up with the most recent developments in their field.  

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Digital libraries ensure that teachers, students, doctors, and scientists can always find the most up-to-date resources when they need them. In addition, doctors can learn about developments in the healthcare system and find resources to help them become more efficient in their work.

With cloud systems, healthcare organizations can better connect with patients because they can access patient data quickly and easily. Also, doctors and nurses can check on patients from a distance and collect real-time data.

Cloud computing helps healthcare organizations manage their supply chain more efficiently. They can also better manage patient populations more effectively, as they can access real-time data and make better decisions faster. 

Cloud solutions make it easier and faster for healthcare organizations to make and use mobile health apps. Health firms can also store and manage medical images quickly, securely, and efficiently.

HOW THE HEALTHCARE INDUSTRY BENEFITS FROM HYBRID CLOUD SOLUTIONS

Cloud-based solutions can help healthcare organizations prescribe medications to patients safely and efficiently. In addition, the big data analytics feature of cloud computing makes it easier for healthcare providers to analyze patient data and make informed decisions.

Types of Cloud Computing in Healthcare

Cloud computing in healthcare is classified based on its deployment environment and distribution service. 

In the deployment environment, there are three main types of cloud computing in healthcare: public, private, and hybrid. We’ll briefly describe them below:

1. Private Cloud Services

Private cloud services run on the organization’s servers and are managed by the IT department of the organization. Private cloud services are better for healthcare organizations that need to store and access sensitive data because they give more control and security than public cloud services. 

1. Public Cloud Services

Public cloud services are hosted on the public internet and managed by a third-party hosting provider. Public cloud services are typically the least expensive and most widely used in healthcare. Public cloud services are ideal for healthcare organizations that don’t have the resources to manage their cloud infrastructure. 

1. Hybrid Cloud Services

Hybrid cloud services combine public with private cloud services so that businesses can get the best of both worlds. Hybrid service is ideal for healthcare organizations that need to store sensitive and non-sensitive data. Hybrid cloud services combine the flexibility of public cloud services with the security and control of private cloud services.

CLOUD SERVICE MODELS EXPLAINED: PAAS VS. SAAS VS. IAAS VS. DBAAS

Under distributive service, there are three major types of cloud computing in healthcare: SaaS, PaaS, and IaaS. We’ll briefly describe them below:

• SaaS

SaaS is a cloud-based technology that offers web-based applications that are already set up, such as medical records management systems. With software as a service (SaaS), healthcare organizations can use cloud-based applications, but an outside party handles the hosting and management of those applications.

• PaaS

Platform as a Service (PaaS) is a type of architecture in which development environments are hosted and managed by a remote cloud service provider. It also deals with software and hardware tools, such as a debugger, compiler, and source code editor. With a PaaS, HealthTech developers can quickly build, test, and launch their apps in production environments.

• IaaS

IaaS, which stands for “infrastructure as a service,” is when an outside cloud provider hosts a network’s servers and storage spaces.

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9 Benefits of Cloud Computing in the Healthcare Industry

The introduction of cloud solutions has greatly benefited the health sector. Let’s look at some of the significant benefits of cloud computing in the healthcare industry.

With the help of cloud computing, the managed healthcare system can easily combine data from many different sources, like other facilities, data repositories, healthcare apps, wearables, etc. As a result, it helps spread information about patients, lets doctors make quick diagnoses, and ensures that people get the proper treatment as soon as possible.

Cloud solutions are usually cheaper than on-premise solutions, which can help healthcare organizations save money. Also, they can help lower operational costs by automating data storage and computing power and eliminating the need for physical storage.

Cloud solutions can help healthcare organizations streamline processes to do more work in less time. Healthcare providers can do their jobs more effectively by making it easier to access and analyze data and by using less expensive and time-consuming IT infrastructure. 

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Cloud providers typically use advanced encryption technologies and access control to protect sensitive medical data while in transit and at rest. Also, because cloud service providers regularly update their data security measures, health firms can keep sensitive medical information from getting into the wrong hands and prevent data breaches. 

By using cloud computing, organizations can take advantage of the security measures that providers have in place rather than developing and maintaining their security infrastructure, which can be costly and difficult to manage.

Cloud computing makes it easier for healthcare providers to collaborate and share data with other providers more quickly and securely. Also, healthcare providers can quickly and easily access patient information, which lets them give timely, personalized care.

Cloud solutions can make it easier and faster for organizations to grow their operations because they can get more resources when needed. Also, you are only charged for the resources like data storage and computing power that you use. Thus preventing business owners from spending money upfront for expensive in-house hardware and servers that they may or may not use to their full potential.

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Cloud storage for medical records makes storing and finding data more accessible, reducing the need to re-enter data and making it more accessible. When patients have access to their records whenever they need them, they feel more in charge of their health care and have a more significant say in the decisions that affect them. It helps with self-care and boosts engagement.

Cloud adoption can help healthcare organizations respond more quickly to changing market conditions as they can access more resources as needed. It also helps healthcare organizations rapidly adapt to evolving regulations and technologies.

Cloud computing helps healthcare organizations reduce medical errors by providing real-time access to data. Organizations can also reduce their risk of data breaches, as cloud solutions are typically more secure than on-premise solutions.

The Daunting Sies of Using the Cloud for Healthcare

In the same way that there are benefits to using the cloud for medical care, the innovation also has potential drawbacks and dangers to look out for. As a result, many businesses are still determining whether or not to adopt the technology. Some potential risks of using the cloud for medical care include the following:

Moving from an aging system to a cloud-based one necessitates a comprehensive rethink of how work is done. Healthcare institutions need to let everyone know what their daily work means.

One of the main ways this technology is used is to store sensitive medical information in the cloud. However, because of this, it’s vulnerable to assault. In addition, in a typical configuration, the data of many healthcare organizations is stored on the same server, leading to a risk of security breaches. Also, the isolation mechanisms put in place to prevent cross-contamination may fail.

COMPUTER VISION FOR HEALTHCARE

Like other industries, healthcare won’t become more efficient by only adopting cloud computing. Instead, to get the most out of the technology, healthcare organizations must connect it to IoT, AI, and data management systems.

When it comes to healthcare software, it can be hard to find skilled developers who know how to use the latest innovations. Similarly, it can take a lot of work to track down cloud experts with experience in the health system.

Choosing the Best Cloud Platform for Your Healthcare Business

Microsoft Cloud, Amazon Web Services (AWS), IBM Cloud, and Google Cloud Platform (GCP) are all well-known platforms for healthcare. However, the best cloud platform for healthcare depends on the needs and budget of the healthcare organization. 

For example, a company might need to store and manage many data in a HIPAA-compliant way. Therefore, a public cloud platform like Amazon Web Services or Microsoft Azure may be the best. On the other hand, if an organization needs a more secure solution, a private cloud solution such as OpenStack or VMware vCloud may be the best option. 

Google Cloud Platform offers healthcare organizations various services, including big data analysis and AI. In comparison, IBM offers cloud-based AI and blockchain solutions.

THE APP SOLUTIONS – CUSTOM HEALTHCARE SOFTWARE DEVELOPMENT COMPANY

How does Cloud Computing Impact Your Healthcare Practice?

Cloud Computing is disrupting the Healthcare Industry. Here are the five significant Impacts of Cloud Adoption in Healthcare:

• Integration: Organizations can easily integrate and securely access patient data across multiple points of origin and storage. This enables healthcare providers to deliver timely, personalized care while also reducing operational costs.
• Access to high-powered analytics: Healthcare organizations can compute relevant patient data from multiple sources and automate processes, providing real-time access to data and helping to reduce medical errors.
• Scalability: Cloud Computing offers affordable and quicker scalability solutions that can be highly customized to healthcare needs. 
• Regularly updated information: Cloud Computing helps healthcare organizations stay updated on the latest Medical Research and Treatments. Cloud computing is changing how data is stored, accessed, and shared in the healthcare industry. This can help them provide the best care and improve patient outcomes.
• Increased collaboration: Cloud solutions have become a crucial part of healthcare organizations’ digital transformation, enabling them to securely share healthcare data and collaborate with other organizations more efficiently.

Conclusion

Cloud computing has significantly impacted the healthcare industry, and the technology is expected to keep growing in the coming years. It has become an important part of the digital transformation of the healthcare system because it lets healthcare organizations store and manage their data more efficiently and securely while giving their customers better services.

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When choosing a cloud healthcare platform or engineers, organizations should consider what they need and how much they can spend. Cloud solutions can help healthcare organizations stay competitive and give their patients the best care possible.

If you still have questions, contact us. The APP Solutions has deep expertise in the field, so don’t hesitate to get us about this or any other issue.  

Do you want to take your healthcare business to the cloud? Check out this article to find out all you need to know about cloud computing solutions in healthcare.

The migration of an application from on-premises infrastructure to a cloud is a challenging step to make for businesses as they’re searching for an effective, fast, and cost-saving approach to moving to the cloud.

In this article, we will cover the meaning and key points of a Lift and Shift cloud migration type, discover whether this type fits your case, and find out how to make the path of migration smooth and easy for implementation.

Do You Actually Need to Migrate?

If you’re reading this article, then Lift and Shift is a subject of interest to you. However, before sharing the concept and pros and cons of Lift and Shift, let’s discover whether it’s really the thing your business needs at this point in time, whether it’s suitable for you to migrate to the cloud, or better to keep things in their current state.

If you have a commercial app with a stable and working infrastructure, Lift and Shift is the right approach to migration. On the other hand, for businesses with big data flow, complex technical architecture with a need for image recognition, it’s better to consider refactoring migration.

Also, it’s worth mentioning that not all on-premise apps will take all the advantages such as ephemeral to compute and autoscaling like a native one. This means that legacy navigation apps can’t benefit while only replacing one server with another. In this case, it’s better either to rebuild an app and install it on a cloud or keep using the current infrastructure.

What Does a Lift and Shift Cloud Migration Mean?

If you’re still reading, let’s dive deeper into rehosting or Lift and Shift – a type of migration that allows moving a business to another server while not changing an app. The answer to the question ‘why Lift and Shift?’ will be because it is believed to be the easiest and most inexpensive way to move to a cloud-based infrastructure.

So what do Lift and Shift migration stand for? First of all, it is a way of moving workload data from one storage type to cloud-based storage while not having to worry about whether all data will be transferred correctly. So, you will not have to re-architect in order to set them into a new environment as is the case when using refactoring.

[Source: TechTarget]

In short, you just lift up the code and shift it to another server.

Moreover, this type of cloud migration allows you to transfer your app to a cloud in its current state without any changes in code or data architecture.

It’s fully applicable when you’re a newcomer to clouds as it’s not as risky in comparison to other types of migration. There is no need to change an application entirely, and one can even migrate configurations that are not documented. 

Migration with Lift and Shift is the best solution to scale a business if:

• you are ready for long-term investments
• you are absolutely sure you want to move to the cloud
• your existing infrastructure is documented and stable
• you’re considering  fast migration and short-term costs
• the installed software can be operated on the clouds 

The Advantages of Choosing Lift and Shift Approach

Here we are going to discover what benefits one can get from Lift and Shift:

Changes are not needed. Businesses don’t have to change their current workflow to be able to use them in the cloud. Data is just rehosted while staying the same.

A fast migration. As you don’t have to change anything, the migration is performed in a much faster way.

Cost Saving. Cloud migration Lift and Shift strategy improves the business’ performance and helps to avoid wasting time and money on inefficient solutions.

[Source: Bain]

For example, using the Lift and Shift approach helped Dow Jones to cut costs by more than 25%. Another example – GE Oil & Gas, which decreased their spending by 52% with Lift and Shift.

Moreover, the Lift and Shift approach doesn’t require a reconstruction of the current IT infrastructure, so you don’t spend additional costs on moving to the cloud.

More security for data. Migration provides you with role-based access control and multi-factor authentication for additional data security.

Who is Going to Benefit from Lift and Shift the Most?

With the advantages that Lift and Shift type of migration offer, we’re going to discover which businesses would benefit the most.

• Businesses that want to move to the cloud faster
• Businesses that are new to the cloud and trying to avoid risky decisions
• Businesses that want to cut their expenses while not spending extra time and money
• Businesses that don’t want to reconstruct their workflow, operations, and data
• Businesses that want to provide more security for their data

Also, to boost migration efficiency we encourage you to use the special tools and technologies offered by the majority of cloud providers. 

If your platform B is AWS storage or Azure, then, you can use NetApp’s Cloud Volumes ONTAP to extend your current database and move them seamlessly to a cloud. 

NetApp SnapMirror is useful for data replication from a NetApp environment to Cloud Volumes ONTAP integrated with an AWS or Azure storage. 

How Much Does it Cost To Move to the Cloud?

You can find plenty of cost calculators depending on what kind of platform you are going to move to. Here’s a list of the most popular platform calculators:

• AWS 
• Google
• Azure
• Rackspace
• IBM

Challenges That Might Be Faced

Any kind of migration, including Lift and Shift, has its own risks. General data says that 42 percent of businesses fail while migrating.  Let’s have a look at what downfalls a business can experience if using Lift and Shift.

Yes, Lift and Shift helps to relocate data while eliminating overhead costs on rebuilding the current data architecture of an app. But, to be able to take full advantage of the packages native clouds offer, sooner or later, businesses can face the need for an additional layer of optimization. Some data should be redesigned to operate as efficiently as is possible. This is relevant for businesses that have large-volume data, a need for big data analysis or the availability of image rendering. For this, they should consider hybrid- or multi-cloud infrastructure.

But, no worries here. On public cloud storage, it’s a simple matter to make some minor changes that won’t influence the proper functioning of the entire cloud. However, it’s recommended to add adjustments before the program is in work to avoid any technical issues.

Lift and Shift with The APP Solutions 

It might look easy to plan and implement migration at first glance, but in truth, it isn’t so. Like any new disruption, the process of migration should be perceived seriously and planned thoroughly. 

A technology partner is the guarantee of your app migration success. The App Solutions company can become your trusted IT partner with migration expertise, and remove your concerns regarding Lift and Shift. Contact us to come up with a strategy for moving to the cloud that will work smoothly under the control of professionals.

We recommend you to follow these simple but straightforward instructions to reduce possible risks and enjoy cloud migration. 

• Analyze your hardware and workflow operations.
• Develop a plan that will help you to move smoothly and enjoy the benefits of native-cloud platforms.
• Start the migration process.
• Test to detect and eliminate bugs as soon as they appear.
• Optimize applications if needed.

[Source: Cloudxchange]

Also, we advise you to consider the following vital timing before taking any action concerning migration:

• The period of time you’re planning to stay in a new server. If it is less than 12 months, there is no point in migration.
• API restrictions. Check whether the chosen cloud has no API restrictions. Otherwise, you might face bottlenecks in your existing API.
• Migration tools. Some clouds offer cloud-based tools to simplify one’s migration process. If there are any, think about how you can integrate them for automation.
• Prioritization of applications. If there is a need for several application relocations, you can write a runbook to transfer those applications that are essential for your business first.
• Compliance between the existing infrastructure and a cloud one. Learn all the requirements of the chosen cloud, and whether some adjustments should be made to be sure all data will be transferred as planned.
• Planning is everything. Act according to a built plan, as the success of migration will depend on your scale of analysis and actions taken.

Final Thoughts

If you’re considering moving to the cloud because of cost, flexibility, speed, and security considerations, then Lift and Shift is good to have on board. 

However, deep analysis with an experienced partner is a must to avoid wasting time and money on tools that not only won’t work for you but on those that will require restarting your migration to a cloud once again. 

A good plan is a power, and you can have such a  plan with The App Solutions with just one click.

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AWS VS AZURE VS GOOGLE: CLOUD COMPARISON

The choice of a cloud platform provider is one of the most important business decisions for a company. 

It is not just a bunch of technical specs that neatly fit the requirements, it’s about possibilities and making things happen in the long-term.

In this article, we will:

• Explain how to choose the right cloud platform provider;
• Compare Three Large Cloud Providers (AWS, Azure, Google Cloud) and their pros and cons.

How to choose a cloud platform?

In one way or another, the choice of which public cloud platform provider is determined by business and technical requirements.

Here’s how:

• The choice is about what you NEED, not what you want. 
• A cloud platform is a means of accomplishing a particular goal.

The key choice factors for cloud platforms are:

• What kind of features do you need?
• What are your application integration needs?
• What is the estimated budget for a cloud solution?
• Is there a need for future expansion or reduction of the features?
• What is the level of expertise of your IT department? Can they handle the proceedings on their own?

For the most part, the choice is less a matter of taste and preference. In fact, it is more a matter of what kind of possibilities this or that cloud platform offers to accomplish a goal. 

Probably, the single most important decisive factor is cost-effectiveness. Yup, it’s all about the money. 

Cloud computing services are not cheap. If used unwisely – they can be a considerable burden on a company’s budget. While the cloud platform itself can provide an efficient service regardless, the way it is used might be draining and ultimately detrimental to the company’s business operation. Because of this, you need to be cautious about your cloud computing spending. 

Let’s take a look at the major factors which influence the choice of a cloud platform.

Cloud Platform Overview

AWS vs. Azure vs. Google: Pricing Models

Pricing is probably the trickiest thing about cloud computing services. Here’s why. The thing about cloud services is that you can measure the exact scope and calculate a unique price for a particular client. 

It is incredibly practical both for companies and cloud providers. You can estimate the budget and negotiate an appropriate deal based on that.

The way cloud providers charge for their services is based on a multi-layered approach. 

This includes: 

• the scope of services (user-based and time-based), 
• the configuration of the platform, 
• related expenses (for example, cloud migration, etc).

Let’s look at how pricing models are organized on the three big cloud providers.

AWS Pricing

Amazon AWS is known for its borderline incomprehensible pricing policies. While on the surface it all seems clear and distinct, by the time you get to the actual price estimation, things get complicated. 

In order to keep things transparent, Amazon provides a cost calculator that includes numerous variables. Because of the sheer number of options, it is really hard to estimate anything in a realistic manner, so the actual prices may be drastically different.  

One of the viable solutions for this issue is the use of third-party cost management tools like Trigger or Hubstaff. 

Azure Pricing

Microsoft Azure’s pricing structure is similarly complicated, albeit more clearly defined. The complexity comes from the numerous software licensing options, variations of configuration and special offers. 

Just like AWS, the effective use of Azure will require a third-party cost management tool to keep things intact.

Google Pricing

Unlike AWS and Azure, Google Cloud keeps things as transparent and accessible as possible. It almost seems like Google Cloud pricing policies were designed in spite of Azure and AWS approaches. 

GCP prices for similar computing and storage services are significantly lower than AWS and Azure. In addition to that, Google provides a wide array of various discounts for their services. It goes as far as contract negotiations, which are considerably more flexible than with AWS and Azure who prefer a more cookie-cutter-styled contract format. 

Now let’s look at the features of AWS, Azure, and Google Cloud and how they compare to one another.

Compute Services

Amazon AWS 

• The main AWS compute asset is Elastic Compute Cloud aka EC2. It is a kind of swiss army knife with different cloud features. One of its main advantages is the immense flexibility of configuration. You can shape it in any way you need it. 
• Then there are computational instances. AWS provides a wide array of bare metal instances, GPU instances, high-performance computing, auto-scaling, et al. Basically, anything you might need in your cloud infrastructure
• Elastic Beanstalk provides efficient autoscaling features for web and mobile applications.
• The other big AWS asset is container services. Docker, Kubernetes, and Fargate provide automatic server management that relieves administrators of pesky headaches. 
• In addition to that, AWS offers a virtual private cloud option titled Lightsail. It might be a good choice in the case of implementing hybrid cloud infrastructure.

Microsoft Azure

• The main advantage of Microsoft Azure over GCP and AWS are Virtual Machines – an emulation of the computer system comprised of disparate parts. 
• Similar to AWS Elastic Beanstalk, Azure’s Virtual Machine Scale Set provides everything the system needs to maintain a proper level of scalability. 
• Virtual Machines are compatible with the majority of commonly used services and applications (including Windows Server, SQL Server, Oracle, IBM, SAP, and more).
• Similar to AWS, Azure provides a large variety of functional instances for GPU and high-performance computing. The other big thing is Artificial Intelligence and Machine Learning features.
• Then there is an exclusive distributed systems platform for applications with a microservice architecture called Service Fabric. It allows streamlining of the application structure while keeping its performance high and reliable.

Google Cloud Platform

• GCP primary service, Compute Engine has custom and predefined machine types with per-second billing. This may come in handy when you need to perform specific workload-intense operations. 
• Since Google was involved in the development of Kubernetes, there is a wide array of Kubernetes container features. 
• The other good thing about GCP is that there are automatic discounts if you are using the service a lot. That comes in handy a lot. 
• GCP Container Registry 

Read also: Cloud Orchestration vs. Cloud Automation

Cloud Storage: AWS vs Azure vs Google 

Amazon AWS 

• AWS provides data storage for different purposes: 
• Simple Storage Service (S3) – object storage;
• Elastic Block Storage (EBS) – persistent block storage; 
• Elastic File System (EFS) – file storage. 
• There is a feature called Storage Gateway. It is used to create a hybrid storage environment
• Then there is Snowball, a piece of hardware that the company can use to transfer immense quantities of data (petabytes and so on) in those cases when the internet transfer is simply not efficient enough. 
• AWS databases include: 
• Aurora for SQL-compatible databases. 
• Relational Database Service (RDS), 
• DynamoDB NoSQL database, 
• ElastiCache in-memory data store, 
• Redshift data warehouse, 
• Neptune graph database 
• Database Migration Service. 
• For archival storage, AWS provides Glacier service. 
• Storage Gateway can be used to easily set up backup and archive processes.

Azure Storage Services

Microsoft Azure Storage features are tailor-made for intrinsic manipulations and large-scale operations. 

• Highlights of Azure storage services include: 
• Blob Storage for REST-based unstructured data object storages, 
• Queue Storage for large-volume workloads, 
• Basic File Storage and Disk Storage. 
• Data Lake Store for big data applications. 
• Data Warehouse service, 
• SQL-based options are wide and compatible with numerous integrations:
• SQL Database
• Database for MySQL 
• Database for PostgreSQL. 
• Cosmos DB 
• Table Storage for NoSQL with Redis Cache as in-memory service 
• Server Stretch Database is its hybrid storage service designed specifically for organizations that use Microsoft SQL Server in their own data centers. 
• Unlike AWS, Microsoft does offer an actual Backup service, as well as Site Recovery service and Archive Storage.

Google Cloud Storage

• Unlike Azure and AWS, Google Cloud provides Unified Storage.
• Similarly to AWS Snowball, there is a Transfer Appliance for extra-sized data transmissions. 
• GCP databases include SQL-based Cloud SQL, Cloud Spanner which is a relational database for mission-critical workloads. There also NoSQL solutions Cloud Bigtable and Cloud Datastore. 
• No backup and archive services. You will need to use third-party solutions.

Cloud Tools

Looking ahead, experts say that emerging technologies like artificial intelligence, machine learning, the Internet of Things (IoT) and serverless computing will become key points of differentiation for cloud vendors. All three leading vendors have begun experimenting with offerings in these areas and are likely to expand their services in the coming year.

Amazon AWS

Amazon AWS has so many different features to offer it is easy to get lost. Among the highlights are services like:

• Sagemaker for training and deployment of machine learning models;
• Lex conversational interface (the one used in Alexa);
• Greengrass IoT messaging service for edge computing and data analytics;
• Lambda serverless computing service.
• DeepLens framework for optical character recognition and image/object recognition. 

Microsoft Azure 

Microsoft Azure is great at all sorts of AI Machine Learnings operations. There are numerous tools that make machine learning model training nice and easy while retaining a decent performance.

Let’s look at the most prominent:

• Bing Web Search API, 
• Text Analytics API, 
• Face API, 
• Computer Vision API 
• Custom Vision Service. 
• Then there are IoT features, including management and analytics

The other good thing is Azure’s serverless computing service Functions. While from a technical standpoint it is similar to the ones on AWS and GCP, the availability of other features on Azure cloud can make it a very versatile and efficient tool.

Google Cloud Platform

Like Azure, Google Cloud also prominently features various Artificial Intelligence and Machine Learning operations. In fact, of all the Big Three of Cloud, Google has the most versatile AI Machine Learning tools of the bunch. While they are less polished than the ones from Microsoft, they provide more room for experimentation and innovation. 

The key ML component of Google Cloud is TensorFlow – a swiss army knife of all machine learning libraries (if you want to know more about Machine Learning tools – here’s an article). 

Google Cloud provides many powerful APIs for natural language processing, machine translation, computer vision, and speech recognition.

Conclusion

The choice of cloud provider mostly depends on the business and technical requirements of a particular company. However, the way Cloud Providers position themselves tells a lot about their target audiences.

Here’s how it is laid down:

• Azure is a good option for companies that use a lot of Microsoft products and have a need for reliable and effective cloud solutions.
• AWS provides the broadest selection of different services and has the biggest reach with its data center all over the world. However, its pricing policies move it towards enterprise-level companies that look for versatile and expansive solutions. 
• In comparison with AWS and Azure, Google Cloud seems almost grassroots. Because of its incredibly flexible pricing models, and a solid service package, Google Cloud is a perfect solution for companies that rely on web-based products, and need simple and efficient grounds for their operation.

Read also:

Cloud Migration Strategy

 

Cloud computing is a kind of infinite pool of possibilities. In one way or another – anything is possible with cloud computing in the mix. This technology makes everything more convenient and less troublesome. Cloud computing helps development teams in solving the following issues:

• Storing a massive amount of data 
• Training machine learning algorithms 
• Constructing a practical business framework 
• Automating and orchestrate the routines
• Handling large workloads

Cloud elasticity and scalability are amongst the integral elements of cloud computing. Despite its widespread use, there is a lot of confusion regarding what is doing what and how exactly. This article will explain what the difference between scalability and elasticity in cloud computing. 

What is Cloud Elasticity?

[Source]

Сloud elasticity is a system’s ability to manage available resources according to the current workload requirements dynamically. 

This is a vital feature of a system infrastructure. It comes in handy when the system is expected to experience sudden spikes of user activity and, as a result, a drastic increase in workload demand. 

Thanks to the pay-per-use pricing model of modern cloud platforms, cloud elasticity is a cost-effective solution for businesses with a dynamic workload like streaming services or e-commerce marketplaces. 

Various seasonal events (like Christmas, Black Friday) and other engagement triggers (like when HBO’s Chernobyl spiked an interest in nuclear-related products) cause spikes in customer activity. These volatile ebbs and flows of workload require flexible resource management to handle the operation consistently. 

Usages of this cloud infrastructure functionality include:

Streaming Services. Netflix is dropping a new season of Mindhunter. The notification triggers many users to get on the service and watch or upload the episodes. Resource-wise, it is an activity spike that requires swift resource allocation. Thanks to elasticity, Netflix can spin up multiple clusters dynamically to address different kinds of workloads.

[Netflix architecture leverages the elasticity of the cloud to scale up and down, source]

E-commerce applications. Amazon has a Prime Day event with many special offers, sell-offs, promotions, and discounts. It attracts a massive amount of customers to the service who are doing different activities. Actions include searching for products, bidding, buying stuff, writing reviews, rating products. This diverse activity requires a very flexible system that can allocate resources to one sector without dragging down others. 

[Amazon infrastructure event management, source]

What is Cloud Scalability?

[Source]

System scalability is the system’s infrastructure to scale for handling growing workload requirements while retaining a consistent performance adequately. 

Unlike elasticity, which is more of makeshift resource allocation – cloud scalability is a part of infrastructure design. 

Scalability is one of the prominent features of cloud computing. In the past, a system’s scalability relied on the company’s hardware, and thus, was severely limited in resources. With the adoption of cloud computing, scalability has become much more available and more effective.  

Automatic scaling opened up numerous possibilities for implementing big data machine learning models and data analytics to the fold. Overall, Cloud Scalability covers expected and predictable workload demands and handles rapid and unpredictable changes in operation scale. The pay-as-you-expand pricing model makes the preparation of the infrastructure and its spending budget in the long term without too much strain.

There are several types of cloud scaling:

• Vertical scale, e.g., Scale-Up – can handle an increasing workload by adding resources to the existing infrastructure. It is a short term solution to cover immediate needs.
• Horizontal scale, e.g., Scale-Out – expands the existing infrastructure with new elements to tackle more significant workload requirements. It is a long term solution aimed to cover present and future resource demands with room for expansion.
• Diagonal scale is a more flexible solution that combines adding and removing resources according to the current workload requirements. It is the most cost-effective scalability solution by far.  

Scalability is an essential factor for a business whose demand for more resources is increasing slowly and predictably. 

Examples of cloud scalability include: 

Call Centers. The typical call center is continuously growing. New employees need more resources to handle an increasing number of customer requests gradually, and new features are introduced to the system (like sentiment analysis, embedded analytics, etc.). In this case, cloud scalability is used to keep the system’s resources as consistent and efficient as possible over an extended time and growth. 

[Application architecture for call centers with cloud scalability, source]

Chatbots are another example of cloud scalability in action. Advanced chatbots with Natural language processing that leverage model training and optimization, which demand increasing capacity. The system starts on a particular scale, and its resources and needs require room for gradual improvement as it is being used. The database expands, and the operating inventory becomes much more intricate.

Consequently, cloud scalability is integral for cloud-based services such as:

• Infrastructure-as-a-Service (IaaS) – Amazon EC2 or Google Compute Engine
• Platform-as-a-Service (PaaS) – Magento Commerce Cloud or AWS Elastic Beanstalk
• Storage-as-a-Service (STaaS) – Google Drive, Microsoft OneDrive, and the likes
• Data-as-a-Service (DaaS) – customer relationship platforms like Salesforce and Hubspot, ERP applications
• Database-as-a-Service (DBaaS) – AWS SimpleDB, Rackspace, Oracle, MongoDB

What is the difference between Elasticity and Scalability?

In the grand scheme of things, cloud elasticity and cloud scalability are two parts of the whole. Both of them are related to handling the system’s workload and resources.  

The fundamental concept of the two is adaptability. It refers to the system environment’s ability to use as many resources as required.

The difference between elasticity vs. scalability lies in their functions: 

• Cloud Elasticity is a tactical resource allocation operation. It provides the necessary resources and capacity required for the current task and handles varying loads for short periods. For example, running a sentiment analysis algorithm, doing database backups, or just taking on user traffic surges on a website. 
• Cloud Scalability is a strategic resource allocation operation. Scalability handles the scaling of resources according to the system’s workload demands. 

Advantages of Cloud Elasticity and Scalability

Both features occur behind the scenes and make the system workflow smooth and seamless. As you can see, it is similar to the “think global – act locally” approach of social activists. The main benefits of elasticity and scalability are the following:

Cost-effectiveness

Cloud scalability and elasticity features constitute an effective resource management strategy:

• The pay-per-use model is the best solutions for sudden surges of workload demand (vital for streaming services and marketplaces)
• The pay-as-you-expand model allows to plan out gradual capacity growth of the infrastructure in sync with growing requirements (convenient for ad tech systems)

Consistent performance

Elasticity and scalability features operate resources in a way that keeps the system’s performance smooth, both for operators and customers.

Service availability

Scalability enables stable growth of the system, while elasticity tackles immediate resource demands.

Elasticity vs. scalability in cloud computing: The final word

Modern business operations live on consistent performance and instant service availability. 

Cloud scalability and elasticity handle these two business aspects in equal measure. 

• Cloud scalability is an effective solution for businesses whose needs and workload requirements are increasing slowly and predictably. 
• Cloud elasticity is a cost-effective solution for organizations with dynamic and unpredictable resource demands. 

These features make scalability and elasticity a viable instrument for the company to hold its ground, grow steadily, and gain a competitive advantage.

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The emergence of IoT devices, self-driving cars, and the likes, opened the floodgates of various user data. IoT devices brought-in so much data that even seemingly boundless computing capabilities of the cloud were not enough to maintain an instantaneous process and timely results. This is bad news in the case of data-reliant devices such as self-driving cars. 

Hopefully, there is a workaround solution – edge computing.

In this article, we will explain: 

• What edge computing is?
• The most prominent examples of edge computing;
• Benefits and challenges of implementing edge computing applications.

What is Edge Computing?

“Edge computing” is a type of distributed architecture in which data processing occurs close to the source of data, i.e., at the “edge” of the system. This approach reduces the need to bounce data back and forth between the cloud and device while maintaining consistent performance. 

With regards to infrastructure, edge computing is a network of local micro data centers for storage and processing purposes. At the same time, the central data center oversees the proceedings and gets valuable insights into the local data processing.

The term “edge” originates from the network diagrams. In it, “edge” is a point at which traffic comes in and goes out of the system. Since its location is at the edges of the diagram – its name reflects this fact.  

Edge Computing vs Cloud Computing: What’s the difference?

Edge computing is a kind of expansion of cloud computing architecture – an optimized solution for decentralized infrastructure. 

The main difference between cloud and edge computing is in the mode of infrastructure. 

• Cloud is centralized.
• Edge is decentralized.

The edge computing framework’s purpose is to be an efficient workaround for the high workload data processing and transmissions that are prone to cause significant system bottlenecks. 

• Since applications and data are closer to the source, the turnaround is quicker, and the system performance is better.

The critical requirement for the implementation of edge computing data processing is the time-sensitivity of data. Here’s what it means:

• When data is required for the proper functioning of the device (such as self-driving cars, drones, et al.);
• When information stream is a requirement for proper data analysis and related activities (such as virtual assistants and wearable IoT devices);

The time-sensitivity factor has formed two significant approaches to edge computing:

• Point of origin processing – when data processing happens within the IoT device itself (for example, as in self-driving cars);
• Intermediary server processing – when data processing is going through a nearby local server (as with virtual assistants). 

In addition to that, there is “non-time-sensitive” data required for all sorts of data analysis and storage that can be sent straight to the cloud-like any other type of data.

The intermediary server method is also used for remote/branch office configurations when the target user base is geographically diverse (in other words – all over the place). 

• In this case, the intermediary server replicates cloud services on the spot, and thus keeps performance consistent and maintains the high performance of the data processing sequence.

Why edge computing matter?

There are several reasons for the growing adoption of edge computing:

• The increasing use of mobile computing and “the internet of things” devices; 
• The decreasing cost of hardware.
• Internet of Things devices requires a high response time and considerable bandwidth for proper operation. 
• Cloud computing is centralized. Transmitting and processing massive quantities of raw data puts a significant load on the network’s bandwidth. 
• In addition to this, the constant movement of large quantities of data back and forth is beyond reasonable cost-effectiveness. 
• On the other hand, processing data on the spot, and then sending valuable data to the center, is a far more efficient solution.

Some edge computing examples

Voice Assistants

Voice assistant conversational interfaces are probably the most prominent example of edge computing at the consumer level. The most prominent examples of this type are Apple Siri, Google Assistant, Amazon Dot Echo, and the likes. 

• These applications combine voice recognition and process automation algorithms. 
• Both processes rely on data processing on the spot for initial proceedings (i.e. decode the request) and connection to the center to further refinement of the model (i.e. send results of the operation).

Self-driving cars 

At the moment, Tesla is one of the leading players in the autonomous vehicle market. The other automotive industry giants like Chrystler and BMW are also trying their hand at self-driving cars. In addition to this, Uber and Lyft are testing autonomous driving systems as a service.

• Self-driving cars process numerous streams of data: road conditions, car conditions, driving, and so on. 
• This data is then worked over by a mesh of different machine learning algorithms. This process requires rapid-fire data processing to gain situational awareness. Edge computing provides a self-driving car with this.

Healthcare

Healthcare is one of those industries that takes the most out of emerging technologies. Mobile edge computing is no different. 

Internet-of-things devices are extremely helpful when it comes to such healthcare data science tasks as patient monitoring and general health management. In addition to organizer features, it is able to check the heart and caloric rates. 

• Wearable IoT devices such as smartwatches are capable of monitoring the user’s state of health and even save lives on occasions if necessary. Apple smartwatch is one of the most prominent examples of a versatile wearable IoT. 
• IoT operation combines data processing on the spot (for initial proceedings) and subsequently on the cloud (for analytical purposes). 

Retail & eCommerce

Retail and eCommerce applies various edge computing applications (like geolocation beacons) to improve and refine customer experience and gather more ground-level business intelligence. 

Edge computing enables streamlined data gathering. 

• The raw data stream is sorted out on the spot (transactions, shopping patterns, etc);
• Known patterns like “toothbrushes and toothpaste being bought together” then go to the central cloud and further optimize the system.

As a result, the data analysis is more focused, which makes for more efficient service personalization and, furthermore, thorough analytics regarding supply, demand, and overall customer satisfaction. 

Here’s how different companies apply edge computing:

• Amazon is operating worldwide. As such, the system needs to be distributed regionally in order to balance out the workload. Because of that, Amazon is using intermediary servers to increase the speed of processing efficiency of the service on the spot.
• Walmart is using edge computing to process payments at the stores. This enables a much faster customer turnaround with lesser chances of getting into a bottleneck at the counter. 
• The target applies edge computing analytics to manage their supply chain. This contributes to their ability: 
• to react quickly to changes in product demand; 
• to offer customers different tiers of discounts, depending on the situation;

Benefits and challenges of edge computing

Edge computing Benefits

The benefits of edge computing form five categories:

1. Speed – edge computing allows processing data on the spot or at a local data center, thus reducing latency. As a result, data processing is faster than it would be when the data is ping-ponged to the cloud and back.
2. Security. There is a fair share of concerns regarding the security of IoT (more on that later). However, there is an upside too. The thing is – standard cloud architecture is centralized. This feature makes it vulnerable for DDoS and other troubles (check out our article on cloud security threats to know more). At the same time, edge computing spreads storage, processing, and related applications on devices and local data centers. This layout neutralizes the disruption of the whole network.  
3. Scalability – a combination of local data centers and dedicated devices can expand computational resources and enable more consistent performance. At the same time, this expansion doesn’t strain the bandwidth of the central network.
4. Versatility – edge computing enables the gathering of vast amounts of diverse valuable data. Edge computing handles raw data and allows the device service. In addition to this, the central network can receive data already prepared for further machine learning or data analysis. 
5. Reliability – with the operation proceedings occurring close to the user, the system is less dependent on the state of the central network. 

Edge computing challenges

Edge computing brings much-needed efficiency to IoT data processing. This aspect helps to maintain its timely and consistent performance. 

However, there are also a couple of challenging issues that come with the good stuff.

Overall, five key challenges come with the implementation of edge computing applications. Let’s take a closer look:

1. Network bandwidth – the traditional resource allocation scheme provides higher bandwidth for data centers, while endpoints receive the lower end. With the implementation of edge computing, these dynamics shift drastically as edge data processing requires significant bandwidth for proper workflow. The challenge is to maintain the balance between the two while maintaining high performance.
2. Geolocation – edge computing increases the role of the area in the data processing. To maintain proper workload and deliver consistent results, companies need to have a presence in local data centers. 
3. Security. Centralized cloud infrastructure enables unified security protocols. On the contrary, edge computing requires enforcing these protocols for remote servers, while security footprint and traffic patterns are harder to analyze.
4. Data Loss Protection and Backups. Centralized cloud infrastructure allows the integration of a system-wide data loss protection system. The decentralized infrastructure of edge computing requires additional monitoring and management systems to handle data from the edge. 
5. The edge computing framework requires a different approach to data storage and access management. While centralized infrastructure allows unified rules, in the case of edge computing, you need to keep an eye on every “edge” point.

In conclusion

The adoption of cloud computing brought data analytics to a new level. The interconnectivity of the cloud enabled a more thorough approach to capturing and analyzing data. 

With edge computing, things have become even more efficient. As a result, the quality of business operations has become higher.

Edge computing is a viable solution for data-driven operations that require lightning-fast results and a high level of flexibility, depending on the current state of things.

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The adoption of cloud computing in the healthcare industry has brought a variety of options to the table – public, private and hybrid solutions, each with its pros and cons. Healthcare cloud computing new possibilities for organizations to refine their workflows and boost the efficiency of the operation. 

The healthcare industry is a good example of the effective implementation of hybrid cloud solutions. The thing is – each medical workflow has its own requirements in terms of needs and goals. Because of this, the solution needs to be both diverse in its application and efficient in providing scalable infrastructure for it.

We have already covered the differences between public, private, and hybrid clouds. This time, we are going to tell you: 

• Why a hybrid cloud model is the best option for healthcare? 
• Explain the benefits of cloud computing in healthcare.  

Why Hybrid Cloud is the best cloud solution for Healthcare?

As was stated previously, healthcare is one of those industries that embraces all sorts of innovations in order to refine operations and make them reliable and effective. Hybrid Cloud is no different in that regard. 

In the past, the healthcare industry used costly legacy infrastructures comprised of disparate elements. For a while, its use was justified by a lack of better options. However, with the emergence of cloud computing in healthcare – things have changed. 

The biggest issue with legacy infrastructures was that they were unable to handle an exponentially growing volume of medical data. 

• The thing is – healthcare operations produce vast amounts of data – patient admissions, diagnosis info, online interactions, discharges, the list goes on. The scope of data only goes on to expand. 

In essence, hybrid cloud infrastructure is a natural solution for the healthcare industry. It can bring the efficiency of the healthcare workflow pipeline to a new level – faster, more scalable, and more productive.

Here’s why.

What is the problem with cloud computing in healthcare?

One of the inherent features of healthcare workflow is its complexity. There are numerous elements involved – all tied together in complex systems. This medical cloud computing needs to handle lots of flows at the same time.

Let’s take patient treatment for example.  

• The patient treatment plan is a customized workflow designed according to the patient’s condition and medical needs. The pipeline involves numerous examinations, medical tests, treatment sessions, data analysis, and further optimization of the treatment strategy according to test results. 
• Every element of this pipeline has a workflow of its own. For example, blood testing facility:
• There is a general workflow. Its operational requirements figure in: 
• the needs of the facility itself for proper functioning (regarding the expertise and use of resources); 
• needs of patients it serves (get accurate results and effective treatment as a result); 
• the needs of healthcare institutions in general (overarching “save people’s lives”). 
• Then there is a workflow for a particular sample. 
• There is a queue of samples. Each set of samples has its own set of requirements (i.e., what kind of tests to make, the urgency of results for a particular case). 
• Overall, the samples are organized according to: 
• the priority in the general pipeline (for the most part, it is either routine or emergency tests); 
• The complexity of the testing; 
• available resources.

That’s just one of the examples. Pretty much every other component of healthcare institutions is operating this way. 

Such process overlap and dependency creates a necessity of having a cost-effective, easily manageable system with clearly defined and transparent processes. Which is exactly what cloud infrastructure is aimed at achieving.

Now let’s look at how the hybrid cloud for healthcare solves all these issues.

Benefits of the Hybrid Cloud Computing

1 Cost-Effectiveness

Cost-effectiveness is one of the key benefits of adopting cloud computing in the healthcare industry. Due to the intricacy of the workflow – It is much less taxing on the budget to use cloud infrastructure than to maintain your own hardware infrastructure. 

The reason why a hybrid cloud is the preferable type of infrastructure for healthcare is simple – it provides more flexibility in terms of arranging and managing operations. The other important aspect is control over data. 

• On one hand, you can use the public cloud for resource-heavy operations and avoid overpaying for cloud services. 
• The volume of resource use for each element differs. This aspect makes it reasonable to keep components on a “pay as you use model”.
• On the other hand, you can keep sensitive data on the private cloud safe with regulated access management. 

The higher level of flexibility allows much better use of resources and, as a result, much more efficient budget spending. With hybrid cloud infrastructure, each element is presented as a self-contained application that interacts with the rest of the system via API. 

2 Manageability 

The other crucial benefit of using hybrid cloud infrastructure is the better manageability of the workflow and its infrastructure. Given the fact of how many moving parts healthcare operation involves – this is one of the key requirements for the medical cloud computing pipeline.

Here’s why.

• In the hybrid cloud configuration, the system is broken down into self-contained components. 
• Each of them is doing its own thing using as many resources as it requires to do it properly. Because of the use of the public cloud, the workload of the particular element is not affecting the other components of the system. 
• At the same time, the interaction between the system components is strictly regulated through a constellation of APIs. 

For example: 

• there is a request for several different tests for the patient – blood test, liver function test, and MRI. Each of them is handled by its own component. 
• There is a central element in the form of patients’ electronic health records. This one is on a private cloud. 
• There are also contributing elements that handle the test. They operate on a public cloud and rely on its autoscaling features. The resulting data is sent back to patient’s EHR on a private cloud. The cycle repeats over the course of treatment. 

With the general pipeline and workflow of the particular elements set apart and clearly defined – it is far easier to oversee the operation in its full scope, analyze its effectiveness and plan its further optimization and transformation.

3 Reliability

Reliability is one of the key requirements for the operational infrastructure of cloud computing in healthcare. For the most part, the reliability requirements manifest themselves in the following needs:

1. Work results of each component need to be accurate and contribute to the accomplishment of the overarching goal of the workflow (to treat patients and ultimately cure them of their ailments).
2. The workflow needs to be uninterrupted and capable of handling as much workload as it needs, whether it is a regular or emergency level. At the same time, the system needs to be optimized and refined according to the ever-changing operational needs (i.e. more or fewer resources, etc)

Because the workflow elements are intertwined and codependent on each other, it is important to keep the consequences of one element failing from spreading to the entire system.

• For example, in a monolithic structure, this means that if one of the elements fails for some reason – this throws a wrench into the entire workflow. The database goes down and you’re busted. The aftermath of such downtime in healthcare might be dire. 
• On the other hand, if something happens to one of the self-contained cloud components – it is contained in the component and not spreading elsewhere (aside from API call error messages).

Here’s how a hybrid cloud for healthcare makes it work like clockwork.

1. The accuracy of results is secured by the use of public cloud resources. Whatever the operation requires to do, the job will be handled with public cloud autoscaling features.
2. The consistency of the workflow and optimization of its elements is maintained through the blue-green deployment approach. Here’s how. In essence, there are two versions of an application. One of them is server A and it is operating at the forefront. Then there is server B with another version that undergoes all sorts of refinement, expansions, optimizations, etc. When it comes to upgrading the component, the servers are seamlessly switched with little to no downtime. While this approach wasn’t originally designed for healthcare, in this industry it can be used to test out experimental features of the application and apply them to real data, without affecting the workflow. 

4 Security / Regulatory compliance / Privacy and confidentiality

Maintaining patient privacy is one of the most problematic aspects of modern healthcare operations. With the increasing scope of digital transformation and cloud adoption, growing cybersecurity threats, and implementation of government regulations regarding patient data usage – this is a considerable challenge. 

We live in the age of big data breaches. Every once in a while some company gets into hot water, either because of some security compromise or because it was downright hacked. 

In the case of healthcare, data breaches and other types of security compromises can be extremely damaging, both for the reputation of the institution, and the safety of its patients.

Here’s how a hybrid cloud solution can handle cybersecurity requirements.

• The structure of the hybrid cloud combines public and private cloud servers. The majority of resource-heavy operation is done on public servers, while sensitive information like patient data is kept on the private cloud with limited access.
• In this configuration, there is more control over data and access to it. This approach provides more transparency regarding who is using sensitive data and where it is used.
• In addition to that, keeping sensitive data on a private cloud allows taking more security and data loss prevention solutions (you can read more about it in our article on DLP). 

Then there is regulatory compliance. Such regional data protection regulations as GDPR (EU), PIPEDA (Canada) and HIPAA (USA) clearly define how patient data should be handled, and describe the consequences of misusing or compromising sensitive data. 

Here’s how a hybrid cloud makes it easier to be compliant with such regulations.

• In the hybrid cloud configuration, sensitive data of any kind is kept on private cloud servers with limited access for the applications operating on public cloud servers. 
• The cloud computing applications in the healthcare process only that data they require for proper functioning (for example, MRI for medical images requires input images and so on). 
• The processed data is then transmitted back to the private cloud and added to the patient’s file.   

5 Digital transformation

Most healthcare institutions have a mix of old and new equipment that uses different software. This aspect complicates the process of digital transformation towards the cloud for healthcare.

For instance, there are elements that you can implement into one system and then there are older elements that are incompatible due to age or software specifications. This is the case with some of the older, larger equipment. 

With a hybrid cloud, it becomes less of a problem as you can balance out the system according to its state. For example, you can tie together the compatible elements into a set of microservice applications. The elements that you can’t transform on the spot can use conversion points in order to feed data into the system and maintain workflow efficiency at the required scope.

In conclusion

Healthcare is probably one of the biggest beneficiaries of cloud adoption as it relies on technical innovation by design. The adoption of cloud computing in healthcare has made each aspect of it bigger, better, and much more efficient in terms of performance and reliability. 

With a hybrid cloud, healthcare operations can handle immense workloads without compromising the integrity and safety of data. At the same time, hybrid cloud infrastructure makes the workflow of each component more balanced and transparent, which makes it easier to manage and refine.

The widespread adoption of cloud computing has changed the way products are created and presented to consumers. With the computing power and infrastructure of cloud computing, companies can deliver a fundamentally different kind of customer experience with a much better feedback loop and higher flexibility to ever-changing customer needs and the business landscape.

The understanding of different types of cloud service model is the key to figuring out the right technical configuration for your business. 

• On the one hand, various cloud services can assist and handle workflow processes.
• Also, the cloud takes out a large chunk of operating expenses related to hardware infrastructure.
• On the other hand, platforms, infrastructures, and databases form a reliable backbone for the product and enable its stable growth and refinement. 

In this article, we will explain the difference between such cloud service models as PaaS and SaaS, or IaaS, and the likes. 

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What is SaaS?

Software as a Service, aka SaaS is a cloud model in cloud computing environment in which the product is hosted by the service provider and delivered to customers over the Internet. 

SaaS is one of the most common approaches to product delivery within a cloud computing configuration. The product itself is more or less the same as an old school software application, except it is now deployed instantaneously directly from the SaaS vendor and with more thorough and responsive product support on the vendor’s part. 

SaaS Delivery

Here’s how the Software-as-a-service cloud computing service model works: 

• The vendor manages the following components: 
• the customer just needs to plug in and use the product.

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Software-as-a-Service advantages

These days, SaaS apps are widely presented as tools that enable particular aspects of the business process. Essential business development tools such as client emails, customer relationship platforms (like Hubspot), sales management (like Salesforce), financial services, human resources management, and so on can operate as SaaS.

One of the most significant benefits of SaaS cloud computing model architecture is its availability. Because the application is distributed through the vendor’s servers, the user can plug into it from whatever computer he uses through his account. The user-generated data is stored both on the vendor’s servers in an encrypted form and also on the user’s device.

The other significant benefit of SaaS is the way it structures a particular business model. Thanks to its deployment approach, the product is open for customization to fit specific user needs. Usually, this approach manifests itself in different product tiers. 

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Software-as-a-Service application examples

One of the most prominent examples of SaaS products is Evernote. 

The cornerstone SaaS business model is freemium. Why so? This configuration usually contains a basic set of features that constitutes the core value proposition of the product. Because of this, freemium is a perfect way to present the product to the target audience. You show how the product addresses their needs and, if they like it enough, they can convert into paying users. 

The basic set of features presented in the freemium version is then supplemented and expanded in the higher tiers. 

Let’s illustrate this with Evernote:

• Evernote core features include note-taking tools, specific task management, and planning tools – the primary value proposition of the product.
• The set of features is greatly expanded in the Premium version. In addition to those mentioned earlier, there are more hardware and software tools to operate with various attachments, broader integrations, and collaboration features.
• Finally, there is a business version that provides even more features with a greater focus on collaborative work and document turnaround. 

What is PaaS

Platform-as-a-service is one of the service models of cloud computing. It operates at a different level. Instead of a dedicated product designed for specific purposes, the PaaS vendor provides a framework in which the customer can do their own thing. For example, develop and deploy an application of their own.

PaaS Solutions

Platform-as-a-service handles cloud-related operations, such as managing operating systems, providing virtualization, maintaining servers, serving storage units, and overseeing networking. At the same time, the customer can focus on the development of the application.

In this case, the PaaS product is a foundation for further building of a specific request, the one that includes all the functional elements and makes it work the way it should. In a way, PaaS serves as a foundation for SaaS solution. 

• PaaS provides a more-or-less ready-made cloud-based framework upon which the application can be developed or hosted.
• PaaS is much more cost-effective than maintaining a dedicated in-house platform. The result is incredibly flexible as the charges only include compute, storage, and network resources consumed.
• PaaS enables smooth scalability as it uses as many resources as required by the current workload.    

Platform-as-a-service examples 

The most representative example of PaaS solutions is AWS Elastic Beanstalk, a compute service designed for deployment and scaling purposes with a wide range of features to maximize the performance of the application. Developers deploy an application on the AWS cloud, and then, Beanstalk takes care of the configuration.

What is IaaS: Cloud Computing Model?

Infrastructure as a Service is another step up in terms of operational scope. In essence, infrastructure as a service provides the whole package for software deployment and related operations – including computing resources and scalability.

As such, it is the most versatile cloud service model:

• Startups and small scale companies use IaaS to avoid hardware and software expenses. 
• Larger companies use the IaaS model to retain control over their applications and infrastructure but also use cloud computing services and resources to maintain their operation. 

One of the key reasons to use IaaS is its scalability features. While PaaS can provide case-specific scalability, IaaS handles it on a strategic scale. It is easier to evolve the product when you don’t need to think about how much your hardware can take.

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In broad terms, IaaS is a self-service environment that substitutes hardware infrastructure while retaining and expanding its features, which include the full spectrum of cloud computing infrastructure: 

• servers; 
• network; 
• operating systems; 
• storages (through virtualization). 

The cloud servers are presented as an interactive dashboard connected with API for respective components. It is like having a data center without actually having to own an actual data center – it is outsourced to the “virtual data center” located on the cloud. 

Infrastructure as a Service examples 

An example of IaaS in cloud computing is the well-known usual suspects – Amazon EC2, Windows Azure, and Google Compute Engine.

IaaS providers handle the servers, hard drives, networking, virtualization, and storage – the things that enable the operation within the infrastructure. At the same time, the client retains a high degree of control over each aspect of the process, including applications, runtime, operational systems, middleware, and the data itself. 

PUBLIC VS. PRIVATE VS. HYBRID CLOUD COMPUTING

As such, one of the critical advantages of IaaS is its flexibility and, as a result, cost-effectiveness. One can customize each component to the current business needs and then expand or reduce the resources according to the consumer demands. 

The other great thing is the automation of routine operations. You don’t need to worry about such things as storage deployment, networking, servers, and processing power.

What is DBaaS, aka Database as a Service?

DBaaS is one of the more case-specific cloud service models. It is a cloud-based service for storing and managing various databases without the need for maintaining physical hardware and handling all sorts of configurations — for example, customer databases of the eCommerce platforms or data coming from a marketing campaign. 

Here’s how database as a service looks: 

• There is a database manager that handles information within the database and monitors operations. The manager provides control over database instances via an API. 
• Database API is accessible to the user through the web-based management dashboard. The user can do all sorts of things with it – provisioning, management, configuration, and other operations within the database.

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In the DBaaS configuration, the majority of administrative tasks are handled by the service provider while the client can focus on using the service. In a way, this is a variation upon a Software-as-a-Service approach but with a more data-driven approach.

The benefits of using DBaaS are similar to SaaS:

• It is a cost-effective approach to handling a broad scope of data.
• DBaaS is available at all times through a rich, interactive dashboard.
• Because of its structure, the backup and security measures can be implemented more thoroughly.
• Cloud features provide the required resources and scalability.  
• Cloud deployment enables continuous refinement of the processes without sacrificing its productivity.

Database as a Service examples 

Examples of DBaaS include:

• Microsoft Azure SQL
• MongoDB Atlas
• Amazon Relational Database Service
• Google BigQuery

Key Differences between IaaS, PaaS and SaaS

	Characteristics	Examples	When to use
Saas	Managed from a central location Hosted on a remote server Accessible over the internet Users not responsible for hardware or software updates	Google Apps, Dropbox, Salesforce, Cisco WebEx, Concur, GoToMeeting, Adobe Creative Cloud	Short-term projects that require quick, easy, and affordable collaboration Applications that aren’t needed too often, such as tax software Applications that need both web and mobile access
PaaS	Builds on virtualization technology, so resources can easily be scaled up or down as your business changes Provides a variety of services to assist with the development, testing, and deployment of apps Accessible to numerous users via the same development application Integrates web services and databases	AWS Elastic Beanstalk, Windows Azure, Heroku, Force.com, Google App Engine, Apache Stratos, OpenShift	When multiple developers are working on the same development project.  You need to create customized applications.  Reduce costs if you are rapidly developing or deploying an app.
IaaS	Resources are available as a service Cost varies depending on consumption Services are highly scalable Multiple users on a single piece of hardware Organization retain complete control of the infrastructure Dynamic and flexible	DigitalOcean, Linode, Rackspace, Amazon Web Services (AWS), Cisco Metapod, Microsoft Azure, Google Compute Engine (GCE)	Startups and small companies may prefer IaaS to save money and time.  Larger companies to retain complete control over their applications and infrastructure Companies experiencing rapid growth

PaaS vs. SaaS vs. IaaS: Final Word

Cloud computing model is the solution for so many things as the sheer computing power of the cloud makes so many things possible:

• Cloud can handle different aspects of a company’s workflow – make it easier and more transparent. 
• Cloud can also serve as a reliable framework for the application work, make it more efficient and available for customers.

The adoption of cloud computing and shift into big data scope has drastically changed business frameworks. With more data to process and integrate into different workflows, it has become apparent that there is a need for a specialized environment – i.e., data lake and data warehouse.

However, despite its widespread use, there is a lot of confusion regarding the differences between the two (especially in terms of their role in the business workflow). Both are viable options for specific cases, and it is crucial to understand which is good for what.

In this article, we will: 

• Explain the differences between lake and warehouse types of architecture.
• Explain in what operations data lakes and data warehouses fit best?
• Show the most viable use cases for data lakes and data warehouses.

Data lake vs data warehouse

What is a Data Lake? Definition

A data lake is a type of storage structure in which data is stored “as it is,” i.e., in its natural format (also known as raw data). 

The data lake concept comes from the abstract, free-flowing, yet homogenous state of information structure. It is lots and lots of data (structured, semi-structured, and unstructured) grouped in one place (in a way, it is a big data lake).

The types of data present on the data lake include the following:

• Operational data (all sorts of analytics and salesmarketing reports);
• Various backup copies of business assets;
• Multiple forms of transformed data (for example, trend predictions, price estimations, market research, and so on);
• Data visualizations; 
• Machine learning datasets and other assets required for model training. 

In essence, the data lake provides an infrastructure for further data processing operations. 

• It stores all data the business pipeline needs for proper functioning. In a way, it is very similar to a highway – it enables getting the job done fast.

The main feature of the data lake is flexibility. 

• It serves the goal of making business workflow-related data instantly available for any required operation. 
• Due to its free-form structure, it can easily adjust to any emerging requirements.
• Here’s how it works: each piece of data is tagged with a set of extended metadata identifiers. This approach enables the swift and smooth search of relevant data in the databases for further use.
• Because of its raw state and consolidated storage, this data is open to repurposing for any required operations without additional preparations or transformations at a moment’s notice. 

This approach is often applied by companies that gather various types of data (for example, user-related data, market data, embedded analytics, etc.) for numerous different purposes. 

• For example, the same data is used to form an analytics report and then make some sort of forecasting regarding where the numbers are moving in the foreseeable future.

What is a Data Warehouse? Definition

The data warehouse is a type of data storage designed for structured data with highly regulated workflows. 

The highly structured nature of data warehouses makes it a natural fit for organizations that operate in clearly defined workflows and a reasonably predetermined scope. 

The purpose of the big data warehouse is to gather data from different sources and organize it according to business requirements so that it is accessible for specific workflows (like analysis and reporting).

• The warehouse is designed by a database management system (DBMS) in the form of different containers. Each section is dedicated to a specific type of data related to a particular business process. 
• The infrastructure of the warehouse revolves around a specific data model. The goal of the model is to transform incoming data and prepare it for further transformation and, subsequently, preservation. 

As such, the data warehouse encompasses a broad scope of different types of data (current and historical). Such data as: 

• Operational data like embedded analytics of the products, 
• All sorts of website and mobile analytics, 
• Customer data
• Transformed data such as wrangled datasets.

The main fields of use for data warehouse applications are business intelligence, data analysis, various types of reporting, decision support, and structured maintenance of business assets. Such as:

• Gain new insights by data mining databases; 
• The same approach is viable for retrospective analysis;
• Performing market research or competitor research by plowing through large datasets of observatory data. 
• Applying user behavior analysis and user modeling techniques to adjust business strategy and provide flexibility for the decision-making process (you can read about user modeling here).

In terms of business requirements, data warehouse architecture is a good fit in the following cases:

• To provide an accessible working environment for business analysts and data scientists.
• To accommodate high performance for an immense amount of queries for large volumes of data.
• To streamline the workflow to increase the efficiency of data exploration.

To enable strategic analysis with structured historical/archival data over multiple periods and sources.

What’s the difference between a data lake and a data warehouse?

Now, let’s take a closer look at the key differences between data lake vs data warehouse.

Data Storage & Processing

• Data Lake is for all sorts of unstructured, semi-structured, structured, unprocessed, and processed data. Because of this, it requires more storage space.
• Data Warehouse focuses on processed, highly structured data generated by specific business processes. This approach makes it cost-efficient in terms of using storage space.

Purpose of data processing

The way data is handled is the biggest differential when comparing data warehouse vs data lake.

Here’s how:

• The data lake is multi-purposed. It is a compendium of raw data used for whatever business operation currently needs. 
• In contrast, data warehouses are designed with a specific purpose in mind. For example, gathering data for sentiment analysis or analyzing user behavior patterns to improve user experience.

Accessibility

Due to their unstructured, abstract nature, data lakes are difficult to navigate without a specialist at hand. Because of this, data lake workflow requires data scientists and analysts for proper usage. 

This is a significant roadblock for smaller companies and startups that might not have enough resources to employ enough data scientists and analysts to handle the needs of the workflow.

On the other hand, Data Warehouse is highly structured, and thus its assets are far more accessible than a data lake. Processed data is presented in various charts, spreadsheets, tables – all available for the employees of the organization. The only real requirement for the user is to be aware of what kind of data he is looking for. 

Development complexity

Due to its abstract structure, the data lake requires an intrinsic data processing pipeline with a configuration of data inputs from multiple sources. This operation needs an understanding of what kind of data is going in, and the scope of data processing operation to configure the scalability features of the storage correctly.

Data Warehouse needs a lot of heavy lifting to conceptualize the data model and build the warehouse around it. This process requires a clear vision of what the organization wants to do in the warehouse, synced with the appropriate technological solution (sounds like a job for a solution architect). 

Security

For the sake of security and workflow clarity – a data lake needs to be a thorough log protocol that documents what kind of data is coming from where and how it is used and transformed. 

In addition to this, Data Lake needs external operational interfaces to perform data analytics and data science operations

Because of its accessibility, the central security component of the data warehouse is an access management system with a credential check and activity logs. 

This system needs to delineate which data is open to who and to what extent (for example, middle managers get one thing, while seniors get a bigger picture, etc.).

Data Lake Use Case Examples

IoT data processing

Internet-of-things device data is a tricky beast. 

• On the one hand, it needs to be available for real-time or near-real-time analysis. 
• On the other hand, it needs to be stored all in one place. 

The abstract nature of the data lake makes it a perfect vessel for gathering all sorts of incoming IoT data – (stuff like equipment readings, telemetry data, activity logs, streaming information). 

Proof of Value data analysis

The scope of big data provides data processing operation (“extract, load, transform” approach in particular) with a need to determine the value of specific information before embarking on further processing. 

Data Lake architecture allows us to perform this operation faster and thus enables the faster progression of the processing workflow.

Advanced analytics support, aka Analytics Sandbox

The “all at once” structure of the data lake is a good “playing field” for data scientists to experiment with data.

Analytics Sandbox leverages the freeform nature of the data lake. 

Because of that, it is a perfect environment for performing all sorts of experimental research, i.e., shaping and reshaping data assets to extract new or different kinds of insights.

Archival and historical data storage

Historical data (especially in a long term perspective) often has insights for what the future holds. 

This feature makes it valuable for all sorts of forecasting and predictive analytics. 

Since historical data is less frequently in use, it makes sense to separate it from the current information, but retain similar architecture to keep at arm’s length if further analysis is required.

Organizational data storage for reporting and analysis

In some cases, it makes sense for an organization to streamline its data repository into a singular space with all types of data included. 

In this case, the data lake serves as a freeform warehouse with different assets currently in use. 

To keep things in order – this approach uses an internal tagging system that streamlines location and access to data for specific employees.

Application support

In certain cloud infrastructure approaches (you can read more about it here), front-end applications can serve through a data lake. 

For the most part, this approach is a viable option if there are requirements for embedded analytics and streaming data back and forth. 

Companion to a data warehouse

A data lake can serve a virtualized outlet of a data warehouse designed for unstructured or multi-purpose data. 

This combination is often used to increase the efficiency of the workflow with high data processing requirements.

Preparation for data warehouse transformation

Because of its abstractness, the data lake is a good platform for the transformation of the data warehouse. 

It can be a starting point for the creation of the warehouse, or it can facilitate the reorganization of the existing warehouse according to new business requirements. 

Either way, the data lake allows to preserve all data and provides a clean slate to build a new kind of structure on top of it.

Data Warehouse Use Cases

IoT Data Summarizing and Filtering

While data lakes are a great operational environment for IoT devices (for example, for individual sensor readings via Apache Hadoop), the data needs to be further processed and made sense of – and that’s a job for a data warehouse. 

The role of a data warehouse, in this case, is to aggregate and filter the signals and also provide a framework on which the system performs reporting, logging, and retrospective analysis. Tools like Apache Spark are good at doing these kinds of tasks.

Current and historical data merging

The availability of the Big Picture is crucial for strategic analysis. A combination of current and historical data enables a broad view of the state of things then and now in a convenient visualization. 

Current data presents what is going on at the moment, while historical data puts things into context. Such tools as Apache Kafka can do this with ease.

Predictive analytics 

The other benefit of merging live and historical data is that it enables a thorough comparison of then and now data states. This approach provides a foundation for in-depth forecasting and predictive analytics, which augments the decision-making process.

Machine Learning ETL (aka Extract, Transform, Load)

Web analytics requires smooth data segmenting pipelines that sort out incoming information and point out the stuff that matters inside of it. 

It is one of the cornerstones of digital marketing and its presentation of relevant content to the targeted audience segments. 

On the other hand, the very same approach is at the heart of recommender engines. 

Data Sessionization

Presenting a continuity of product use is an important source of information to improve the product and its key aspects (such as UI). It is one of the ways to interpret embedded analytics. 

Sessionization groups incoming events into a cohesive narrative and shows the statistics at selected metrics. Parallel processing tools cover its high-volume requirements like Apache Spark.

Conclusion

Both data lakes and data warehouses are complicated projects that require thorough expertise in the subject matter. On the other hand, there’s a need to bring together business requirements and technological solutions. 

If you have a project like this or need help rearranging an existing project – call us, we can help you.

Cloud computing is increasingly becoming the most relevant technology of the modern age. Take a look at these three key facts:

1. Cloud platform scalability features provide companies with enough resources to implement complex big data machine learning neural networks with immense databases;
2. The use of the cloud is much more cost-effective, as you are only paying for the resources you are actually using and not the whole package. 
3. Cloud automation and orchestration tools allow organizing workflow to maximum efficiency.

Workflow efficiency is especially important, as this is an element that makes cloud technologies so valuable. The ability to automate as much of the workflow as possible brings in more insights, and as a result, a bigger competitive advantage for the company.

However, despite the prominent use of automation and orchestration in business, there is a lot of confusion regarding what is doing what and why. 

In this article, we will explain the difference between cloud automation and cloud orchestration and their benefits to the workflow.

The difference between cloud automation and cloud orchestration

Let’s start from the basics:

• Automation is an arranged routine that performs a single specific task (like doing stats updates every 5 minutes);
• Orchestration is the fine-tuning of several automation routines into a cohesive workflow (for example, gathering data from sources, updating the stats, and sending notification regarding changes);

Together, cloud automation and orchestration pave the way for greater things. 

Now let’s go into the specifics.

What is Cloud Automation?

Cloud automation is a term that describes a set of tools and processes used to replace manual work with automated routines within a cloud environment.

Why? The reason for that is simple – maintaining an operating system requires the performing of multiple repetitive manual processes. 

The thing with “repetitive” and “manual” parts of the tasks is that it is inefficient and time-consuming. Also, it paves the way for various errors and oversights. 

The emergence of errors in the system leads to setbacks that hold back the availability of the system and negatively affect the overall performance, or simply breach sensitive information. In other words, all-around bad stuff.

Cloud automation allows avoiding these kinds of problems while making the system more transparent and reliable. It is a key methodology of the agile development approach and DevOps proceedings that enables rapid resource deployment and scalability for continuous delivery and continuous integration.

Here’s how cloud automation works:

• You have a set of connectors responsible for different tasks (for example, monthly database back-up). 
• Upon triggering, the connectors perform a specific course of action.

The automation routines cover all sorts of provisioning or managing tasks with a high level of organization and clearly stated requirements. It is very reminiscent of putting the workflow on railroad tracks. 

Let’s go through the most common use cases:

1. One of the key fields of use for cloud automation is the establishment of an Infrastructure as Code (IAC) environment that streamlines the system’s resource management and enables more efficient workflow. 
2. On the other hand, cloud automation is used for workload management. Such tools can be configured to monitor the proceedings in the system and allocate resources according to the situation (i.e. getting more or fewer resources on the spot). 
3. Cloud automation is also used for workflow version control. In this case, the automation tools are used to audit the processes and monitor changes in the system.
4. Cloud automation is one of the integral components of the hybrid cloud environment. In this case, it is used to tie disparate elements (i.e. applications on public and private clouds) together into a cohesive system.
5. Finally, cloud automation tools are used in Data Loss Prevention tools to provide frequent data back-ups.

The automation itself is made possible with an array of orchestration (more on that later) and automation tools that manage the operation.

• Orchestration tools are used to write down the deployment process and its management procedures.
• Automation tools are used to perform the aforementioned routines.

A good example of cloud automation is Facebook. This social media platform uses cloud automation to scale the system and maintain its availability to users without significant hiccups. The system monitors the workload and allocates resources accordingly. 

Now let’s explain what cloud orchestration is. 

What is Cloud Orchestration?

Cloud orchestration is the framework that ties together all of the automation routines across various services and clouds. Combined with automation, orchestration enables efficient service of the cloud infrastructure.

In essence, cloud orchestration is a coordination of the automated routines. 

• The automation is the tactical operation;
• The orchestration is the strategy, the big picture of how things should co-operate with one another.

Cloud orchestration is the automation of the system on a global level, encompassing every element and process. Among other things, this includes:

1. Resource management, including: 

• Service availability, 
• System scalability, 
• Failure recovery, 

2. Dependency management of the infrastructure elements.

• Security protocols; 
• compliance activities for automated processes;

A good example of cloud orchestration is the regional adaptation of privacy policies. Amazon is an avid practitioner of this due to its worldwide presence. Here’s how it works – there is a location-dependent protocol going on top of the system. It identifies the location of the particular user and changes the privacy policy accordingly.

Benefits of Automation and Orchestration

Automation and orchestration are the cornerstones of efficient and productive cloud computing workflow. They streamline complex systems with multiple moving parts into a tight knot of processes that work together like an orchestra. 

Automation takes over the routine tasks and leaves human resources for more important aspects of the development process. 

Orchestration creates a system out of numerous automation processes and arranges their operation in clearly defined and optimized sequences.

Together, automation and orchestration sustain a high level of productivity and enable the growth of the product and system behind it.

The benefits of implementing automation and orchestration in the cloud infrastructure are as follows:

• Cost-effectiveness – lowering IT costs and saving resources for innovation and new projects;
• Automation requires clearly defined and regulated processes. This brings more transparency and clarity to the workflow;
• Higher productivity – automated routines deliver consistent and reliable results with fewer resources and supervision;

How to make an effective cloud automation/orchestration?

Effective implementation of cloud automation, and subsequently cloud orchestration, require an understanding of the capabilities of the system and its potential for growth.

There are several ways to identify how to do this in the most effective manner:

• The automation task can be identified by its role in the workflow. The key credential is the recurrence and highly organized structure of the particular process. For example, you have a routine database back-up. It happens each month. The process is pretty straightforward and doesn’t involve significant variations – thus it can be automated. 
• Consider measuring the impact of task automation. Does it simplify the workflow? Is it more effective than the manual approach? The key factors are speed and accuracy. Let’s take data preparation for example. Wrangling data by hand takes a lot of time and manpower but doesn’t guarantee the data is accurate through and through. There might be missed errors. Automated data mining turns this process into a walk in the park. One algorithm scrapes the data from the source, a second algorithm clusters it, a third classify it, and so on.
• Once the individual tasks are automated, you can integrate the automation routines into sequences, thus enacting orchestration. The sequences form logically. For example, you have a data mining routine. It is followed by pattern evaluation. Then comes the visual presentation of results. 

What’s next?

The implementation of cloud automation and its expansion into cloud orchestration streamlines the workflow and makes it more efficient. 

By taking over and refining the routines, automation, and orchestration free resources for improvement and innovation. This allows the company to concentrate on what really matters, instead of being held back by a poorly organized system.

This leads to a more cost-effective business pipeline and much better use of system resources and, as a result, a significant advantage over the competition.