Microsoft Cloud For Healthcare: How MS Cloud Solutions are benefiting Healthcare Organizations

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?



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.


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.

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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. 


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.



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.


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.


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



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.


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. 


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.


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.





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.


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.

Key Differences between Data Lake and Data Warehouse

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.


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). 


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.

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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.


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.

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Why You Should Migrate to Amazon Web Services

The past decade has seen Amazon Web Services (AWS) grow to become the leader in cloud computing. The internet retailer has managed to gain over $12.2 billion in revenue as of 2016, after working with some of the biggest organizations including the C.I.A and Netflix.

So large is Amazon’s growth that the fourth quarter of 2016 saw the AWS account for at least 40% of the public cloud service market in the entire world. Amazon’s competitors such as Microsoft only accounted for 11% while Google and IBM had 6% each as reported by the Synergy Research Group.

The Race for Public Cloud Leadership

Considering that cloud computing is still in its early growth stages all over the world, the choice of services is still the one most businesses find hard to make. There are some things that you should consider and which make AWS data migration services the number one choice for the provision of these services.

What do most businesses look for when they need cloud migration?

Recent times have forced almost all businesses to consider data migration. There are many benefits to be accrued from such a move. However, successful migration requires a well-planned strategy to avoid having the business’ physical infrastructure crammed into a virtual environment with no plan for their optimal use.

When looking for cloud migration services, businesses consider:

  • Return on investment.
  • The individual requirements of each asset being moved to the cloud – most businesses would have knowledge of the assets they have and would seek to move some of them to the cloud; prioritizing which are most critical. During cloud migration, the business would be looking for a partner who can prioritize the assets and applications in order of their Recovery Time Objective (RTO).
  • The amount of support that the service provider can avail. Each step taken by a company while moving to the cloud is unique, most businesses would look for a service provider that is versatile enough to accommodate the unique requirements.

The best process of migrating to the cloud is one that is personalized to accommodate the unique challenges of the business. A partner who understands that there is no one-size-fits-all migration strategy is usually the best choice.

AWS migration tools that are guaranteed to benefit your business

From privately linking your data center to an AWS region directly and migrating data to the cloud in batches to working with S3 for different geographical distances, Amazon’s data migration services provide the following tools:

  • Unmanaged Cloud data migration tools that are simple methods of moving data to Amazon’s cloud in small batches. They include:
    • Glacier command-line interface (CLI) which moves customer data to Glacier vaults
    • RSYNC which when opened along with a 3rd party filing system, copies data directly to S3 buckets
    • S3 command-line interface (CLI) which moves data directly to S3 buckets using commands
  • Cloud data migration tools managed by AWS. There are some AWS migration tools that are made by Amazon to enable your business to manage the move more effectively. They are grouped into two:
    • Optimizing the Internet, including methods that enable the movement of large archives, oceans of data, and for businesses that have unrealistic data volumes and bandwidth requirements.
    • Interfaces that are friendly to S3 include methods that simplify the use of S3 along with the company’s existing applications. As opposed to simply shifting huge sets of data at one time, these interfaces assist a business to be able to integrate their existing processes directly using cloud storage.

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Why you should choose Amazon’s cloud migration services for your business

AWS migration service offers a wide range of benefits to businesses looking to migrate their data. The following are some key benefits of migrating to the cloud using AWS:

  • Ease of use. AWS cloud migration is specifically structured to enable application providers, vendors, and ISVs to host all your business’ applications efficiently and safely regardless of whether they are native or new and SaaS-based. AWS also has a management console that can be used in accessing AWS’s hosting platform.
  • As earlier mentioned, each business requires a unique strategy. AWS allows you to choose not only the programming language but also the operating systems, database, and web application platform you require when performing your migration to AWS cloud.
  • Cost-effectiveness. AWS has made its services as cost-effective as possible for all businesses. Each client is only required to pay for computing power and storage along with any resources used without having to get into any contracts or making commitments up-front.
  • has been a leading online business for more than a decade, accumulating a secure infrastructure for global computing that is not only reliable but highly scalable and influences AWS reliability as well.
  • This is always a number one concern for most businesses that are looking to migrate data. AWS employs an extremely secure end-to-end strategy that includes operational, physical, and software measures to secure infrastructure. By choosing AWS, you are guaranteed that your data is in safe hands.
  • Scalability and high-performance. A combination of Auto Scaling, AWS tools, and Elastic Load Balancing allows all your applications to scale either up or down by the demand. This along with AWS’s infrastructure gives you unrestricted access to many resources for computing and storage whenever you need them.

From all of these benefits, it is easy to see how AWS has managed to grow so rapidly over the years and continue to provide excellent services to its clients from moving data to the cloud to cloud transfer.

Research from IDC, 451 research, Forrester, and Gartner all reveal that 50% of companies that attempt cloud migration end up exceeding their budget, take a longer time than expected, and result in the disruption of business. Choose to migrate to AWS today and enjoy scalability, efficiency, and reliability all at the most affordable prices.

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Business Process Benefits of DevOps

The modern market is a thing full of twists and turns at every corner that requires flexibility and the ability to adapt to the ever-changing state of things. “Agility” is the word that best describes what it takes to be competitive in the modern world.

You simply won’t get anywhere if you aren’t ready to adjust according to the situation and bend it to your benefit. It is true for most industries, but especially so in software development. Cue DevOps. 

If you are thinking about developing a web or mobile product, agility is the means to tangible results: speed of the work process, implementation of the new features, team efficiency, optimization of the product, and so on. Basically, being agile becomes a strategic advantage for a company and the product both in the short run and in the long run. 

It is even more apparent when it comes to an outsourcing segment where everything lives and dies on how they are able to adapt and go beyond. DevOps approach implementation is one of the means to be more flexible and adaptable thus more competitive.

But let’s begin with definitions. 

What is DevOps?

DevOps is a cross-functional approach to the process. DevOps model is a combination of two distinct parts of the software development process – development and operations (AKA infrastructure management).

Basically, it is a result of streamlining the organization in order to make it more flexible, dynamic, and ultimately effective. This streamlining was necessitated due to ever-growing, sprawling organizations that take too many resources and hold down the overall flexibility of the development team.

Devops meme, devops diagram, devops images

As such, DevOps is more of a mindset than anything else. It is about tight collaboration, being on the same page and delivering to the common goal — improving every element of the product and acting as fast as possible to the emerging situations and morphing requirements.

In other words, let’s quote Daft Punk: “Work it, make it, do it. Make it harder, better, faster, stronger”. That’s what DevOps is about.

Why is DevOps is needed?

Two words — mobility and flexibility. Customer feedback and testing is a big deal when it comes to making a good product that will outlast the initial splash. Because of that, it makes sense to adapt according to order to keep the product adequate and capable of doing its work. However, it is not exactly the easiest thing to pull off due to long iteration cycles and scaling large team to the cause.

The main task of a DevOps engineer or specialist: make sure the software works both from the developers’ standpoint as well as an infrastructure standpoint.

When you implement a DevOps culture, it enables to implement changes effectively and on time. The result — overall better product that does better business.

DevOps Benefits

Dynamic Iteration Cycle / Continuous Integration and Continuous Delivery

The biggest benefit of DevOps from a business point of view is obvious — it is all about the speed of delivery.

Due to significant streamlining and reorganization of the workflow — the very process transforms into more dynamic and efficient. That, in turn, makes iteration shorter and much more responsive while avoiding the danger of breaking things while moving too fast.

A combination of automation and thorough testing drastically charges the pace while lessening the overall workload.

Basically, it means faster moving in shorter steps i.e Continuous Integration and Continuous Delivery (aka CI/CD). As such, it allows to gradually implement small changes that contribute to the whole.

In addition – shorter iterations mean that even if there are some fails – their scope will be much smaller and that means it would easier to deal with them. The nightmarish notion that everything will break down at once is practically non-existent with this approach.

A Better Environment for Technical Scalability

Scalability is one of the top priorities for any kind of project. If the product is able to take a load and get on with it — it is a sign that it works well. If not — you know what it means. With the rise of cloud computing, it became a big deal.

DevOps implements certain practices to secure better scalability. In essence, scalability is not just what servers and networks are able to carry on — it is also the tools that make it happen.

It is important to configure the system in a flexible manner so that when necessary it will be able to increase the resource consumption and also scale it down when the load is lesser.

The thing with these tools is that they need continuous optimization — changes in server, bandwidth, and storage capacity.

See also: Best PHP Frameworks to Use 

Superior Communication: Everyone on the Same Page

One of the most obvious benefits of implement DevOps principles is a significant streamlining of communications. It is always a good thing when everybody is on the same page and every member of the team is able to contribute to the process.

Since collaboration and communication are at the center of the DevOps approach — implementation of it manages to set a much more creative environment that can positively affect the quality of the product.

For instance, streamlined communication eases getting the team on the same page. It also helps with onboarding new members of the team. It is also helpful in describing the priorities of the current moment.

DevOps automates certain routine elements of the development process and allows developers to focus on other more demanding and important elements.

DevOps Process Means Better Team Scalability

As a direct result of tighter communication — team scalability makes a significant leap forward. Most of the time, people need some time to get acquainted with the project. When the DevOps approach is implemented right, it shortens the time people need for adjustment because everything works like a well-oiled machine. 

Because of that, you don’t have to worry about the fact that you face the need to scale your team and it might break the workflow. DevOps makes this process much more efficient and easier. 

Read more about DevOps Team Structure.

Process Automation

The development process is riddled with repetitive routine tasks that just need to be done. It takes time and greatly affects the motivation of those who are tasked with such things. While important, these routines often take precious time that could’ve been used for something more important.

DevOps makes it almost a non-issue with the help of automation. Not only it creates a more efficient workflow but it also helps with keeping everything monitored and reported. It is especially important for testers who can’t afford to miss something in the sea of code.

The decrease of manual actions leads to much more time to dedicate to more important things.

Documentation / Code Synchronicity

Writing coherent project documentation is something that some businesses neglect, but at The APP Solutions, we put stress on this part of the project’s lifecycle. But no matter how good your initial technical specification is, it is often an evolving entity, especially for bigger and more complex projects.  

No matter how hard you will try to describe everything to a tee — things change when they get done and that should be reflected in the technical documentation. Otherwise, nothing will make sense and ultimately everything might fall apart. Because of that, there is a lot of backtracking and adjusting the code and documentation to one another.

How can the DevOps approach and specialists help? Due to the transparent and highly organized structure of the code — there is lesser dependence on the documentation. Everything can be understood through the code itself. Which makes tech documentation more of solidification of the fact that a herald of things to come.

The Transparency of the Infrastructure

The other big advantage of a DevOps approach is a significant clarification of the code infrastructure.

The code is what makes the product. However, the product is made by humans, many humans, sometimes they err and sometimes the parts they made don’t fit well together (despite the fact the developers are seniors and Quality Assurance people are professionals as well.)

How DevOps make the save? DevOps enables the unification of the code: it cleans the code up, makes it more transparent and easier to operate with. It also solves any emerging issue connected with the legacy elements.

On a side-note — transparency also greatly simplified the onboarding of new members to the fold. When everything is clear as a whistle, it is easier to get involved, which is a strategic advantage in terms of team scalability (see the point above.)

Infrastructure as Code

Infrastructure is what ties together numerous elements of operation — networks, virtual machines, load balancers, and so on into a comprehensive mechanism that ticks like clockwork. 

The project’s infrastructure, like the tech specification, evolves with the product and often gets muddied up over time if no specific measures are taken to prevent this. As a result, this might seriously affect the quality of performance and effectiveness of the operation. This is the case not only for cloud storage but also for dedicated infrastructure.

However, manual configuring of infrastructure is time and resource-consuming. DevOps makes it a non-issue by switching from manual interaction to programmatic and implementing several methodologies, such as continuous integration and version control. This drastically decreases the possibility of getting funny bad things in the system and eliminates the element of unpredictability.

Programmatic interaction with the infrastructure is standardized and streamlined to the essentials — there is a set of patterns the system follows. It enables testing as soon as possible which enables adjustments and fixes early on.

Another important element of the Infrastructure as Code approach is Code Review. This gives clarity of the situation and provides a perspective for the team over the infrastructure changes. This is important in keeping everybody on the same page perfectly synchronized.

Simpler Security Maintenance

Last, but far from least benefit that comes from transparency and the organized code is a vast simplification of implementing security measures.

Usually, security is the hardest element to pull off as it is always somewhat detached from the main system. This process starts from assets inventory and goes all the way through access inventory to the implementation of security measures such as system scans.

However, with the structure crispy clean accessible and most of the processes automated — it is not a big deal to keep the thing safe.

In conclusion

According to the statistics, it is clear that DevOps has lived up to all the expectations the developers had. The only thing that hasn’t quite hit the mark is the increase in income but this is expected to change.

DevOps Benefits seen or anticipated

DevOps is one of the most exciting practices of the current moment. It is slowly but surely spreading its influence over the software development industry and establish itself as a standard operation.

It is a good thing because order and clarity are amongst the things every project is striving for. That is why DevOps methodology matters and why you should implement its practices in your project.

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The Best Practices for Cloud Security You Can Choose from

Ever since the great discovery of the Internet, the world has never been the same. A lot of technology has made available due to this single discovery, with the most recent one being the cloud. Cloud technology has aided some business transaction and entertainment opportunities thanks to the dynamic cloud computing strategies and the numerous file-sharing opportunities. However, security in the cloud remains one of the major concerns businesses and organizations have to face at one point in time.

The idea of running your business operations and storing data on a virtual network that you have little control of is not only economical but also manageable. However, this is not the case. According to last year’s statistics on cloud security, CloudPassage found out that cloud security is still the number one concern in this industry. According to the research findings, at least 53% of the individuals interviewed noted that “general security risks” is the number one impeding factor for adopting cloud technologies. 91% percent of those surveyed were either “very concerned” or “averagely concerned” about this technology.

From the above observation, it is evident that cloud security is a pain to most businesses and organizations. Therefore, to ensure that everything runs smoothly, business CEOs and CTOs are advised to adopt best practices for cloud security in their business. The following article seeks to explain why this is important and also educate all stakeholders on the best cloud security strategies to adopt in 2017.

Why is the security of cloud computing so important to business?

Currently, at least 90% of businesses have taken their businesses to the cloud. While this number is slightly higher for large- and medium-scale businesses compared to the small business enterprise, the benefits of cloud computing are equally important to both divides if the security of cloud computing is guaranteed. The following are some of these benefits:

Helps your business reduce IT costs

Most businesses are moving their operations to the cloud to reduce the huge costs associated with running a business. Move your operations to the cloud only if you have invested in the latest cloud security models. Thanks to the secure cloud computing services, you can save money by:

  • Reducing the wages of staff as you will employ fewer IT experts compared to the manual system
  • Minimizing your energy expenses as you will use fewer computers as storage systems
  • Reducing operational time lags in your systems
  • Reducing the frequency of upgrading your IT systems


One of the main objectives of the business is to grow and increase both in size and in operations. Thanks to cloud computing, businesses can achieve this with much ease. A secure cloud hosting platform means that your business can adjust to its growth thus helping it save money, time and other resources that could have been spent on improving the manual IT systems.

Promotes flexibility

A secure cloud computing service means that you can work from anywhere and at any time. Thanks to cloud technologies, data is stored online and can be accessed from any place using any device. Cloud database security, on the other hand, guarantees that the data you are storing online is always secure and cannot be corrupted or interfered with at any time.

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The best cloud security practices to adopt

Cloud technologies are rapidly changing nowadays. Due to the rapid changes in technologies and the numerous cloud computing vulnerabilities, businesses are left with no option but to improve their cloud security strategies as well. The following are some of the practices you will find handy in 2017.

Understand your model

When planning cloud security, this is one of the most important factors to consider. Arguably, security in the cloud is a shared responsibility that both the business owner and the service provider need to pay attention to.

Different individuals define cloud computing technology differently. The following diagram is a representation of how you need to approach your computing security issues as explained by Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Microsoft Azure, and Amazon Web Services (AWS).

Cloud Security Best Practices

Data encryption

Data encryption is one of the most recent security features for cloud computing you will come across in 2017. With the many instant messaging applications that exist in the market today, your data might not be safe. Make use of the latest encryptions and encrypt your data while in storage and also during transit.

Carry out audit and test your strategy

When considering cloud computing and information security, you need to know that even the most robust strategy is highly vulnerable to the ever-evolving hackers. Therefore, once you have chosen the most suitable security of cloud computing, you will need to check and ensure that you are duly covered. Test your strategy and then stop at the strategy that offers you maximum protection.

With the direction most businesses are taking towards cloud computing, it is undeniably true to say that cloud technology is the future of business. While this is true, threats from different angles are continually causing a challenge to most businesses. To be able to enjoy the numerous benefits of cloud computing, cloud security is key.

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