Cloud Service Models Explained: PaaS vs. SaaS vs. IaaS vs. DBaaS

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. 


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 application itself; 
  • Its runtimes and all sorts of internal and cross-product integrations; 
  • security measures; 
  • application databases; 
  • server hardware maintenance; 
  • storage units and networking 
  • the customer just needs to plug in and use the product.


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.

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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. 


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.


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. 

What solutions can we offer?

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. 


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.


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



When to use 


  • 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


  • 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,, 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.


  • 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.

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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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Cloud Computing Security Risks in 2021, and How to Avoid Them

Cloud technology turned cybersecurity on its head. The availability and scope of data, and its interconnectedness, also made it extremely vulnerable to many threats. And it took a while for companies to take this issue seriously. 

The transition to the cloud has brought new security challenges. Since cloud computing services are available online, this means anyone with the right credentials can access it. The availability of enterprise data attracts many hackers who attempt to study the systems, find flaws in them, and exploit them for their benefit.  

One of the main problems that come with assessing the security risks of cloud computing is understanding the consequences of letting these things happen within your system. 

In this article, we will look at six major cloud security threats, and also explain how to minimize risks and avoid them.

What are the main cloud computing security issues? 

1. Poor Access Management

Access management is one of the most common cloud computing security risks. The point of access is the key to everything. That’s why hackers are targeting it so much. 

In 2016 LinkedIn experienced a massive breach of user data, including account credentials (approximately 164 million). 

The reasons were:

  • insufficient crisis management 
  • ineffective information campaign 
  • the cunningness of the hackers

As a result, some of the accounts were hijacked, and this caused quite a hunt for their system admins in the coming months. 

Here’s another example of cloud security threats. A couple of months ago, the news broke that Facebook and Google stored user passwords in plaintext. While there were no leaks, this practice is almost begging to cause some. 

These are just a few of the many examples. 

So how to handle this issue?

Multi-factor authentication is the critical security component on the user’s side. It adds a layer to system access. In addition to a regular password, the user gets a disposable key on a private device. The account is locked down, and the user is sent a notification in case of an attempted break-in.  

Distinct layout for access management on the service side. This layout means determining the availability of information for different types of users. For example, the marketing department doesn’t need to have access to the quality assurance department protocols and vice versa. 

2. Data Breach and Data Leak – the main cloud security concerns

The cloud security risk of a data breach is a cause and effect thing. If the data breach happens – this means the company had neglected some of the cloud security flaws, and this caused a natural consequence.

What is a data breach? 

It is an accident in which the information is accessed and extracted without authorization. This event usually results in a data leak (aka data located where it is not supposed to be). 

Confidential information can be open to the public, but usually, it is sold on the black market or held for ransom. 

While the extent of the consequences depends on the crisis management skills of the particular company, the event itself is a blemish on a company’s reputation. 

How data breaches occur? 

The information in the cloud storage is under multiple levels of access. You can’t just stumble upon it under normal circumstances. However, it is available from various devices and accounts with cryptographic keys. In other words, a hacker can get into it if he knows someone who has access to it. 

Here’s how a data breach operation can go down:

  • It all starts with a hacker studying the company’s structure for weaknesses (aka exploits). This process includes both people and technology. 
  • Upon identifying a victim, the hacker finds a way to approach a targeted individual. This operation includes identifying social media accounts, interests, and possible flaws of the individual.
  • After that, the victim is tricked into giving access to the company’s network. There are two ways of doing that:
  • Technological, via malware sneakily installed on a victim’s computer;
  • Social engineering, by gaining trust and persuading someone to give out their login credentials;

That’s how a cybercriminal exploits a security threat in cloud computing, gets access to the system, and extracts the data.

The most prominent recent data breach is the one that happened in Equifax in 2017. It resulted in a leak of personal data of over 143 million consumers. Why? Equifax’s developers hadn’t updated their software to fix the reported vulnerability. Hackers took advantage of this and the breach happened.

How to avoid data breaches from happening? 

A cloud security system must have a multi-layered approach that checks and covers the whole extent of user activity every step of the way. This practice includes:

Multi-factor Authentication – The user must present more than evidence of his identity and access credentials. For example, typing a password and then receiving a notification on a mobile phone with a randomly generated single-use string of numbers active for a short period. This has become one of cloud security standards nowadays. 

Data-at-Rest Encryption. Data-at-rest is a type of data that is stored in the system but not actively used on different devices. This process includes logs, databases, datasets, etc. 

Perimeter firewall between a private and public network that controls in and out traffic in the system;

Internal firewall to monitor  authorized traffic and detect anomalies; 

3. Data Loss

If a data breach wasn’t bad enough, there is an even worse cloud security threat – it can get irreversibly lost like tears in the rain. Data loss is one of the cloud security risks that are hard to predict, and even harder to handle. 

Let’s look at three of the most common reasons for data loss:

Data alteration – when information is in some way changed, and cannot be reverted to the previous state. This issue may happen with dynamic databases.

Unreliable storage medium outage – when data gets lost due to problems on the cloud service provider’s side.

Data deletion – i.e.,  accidental or wrongful erasure of information from the system with no backups to restore. The reason is usually a human error, messy database structure, system glitch, or malicious intent. 

Loss of access – when information is still in the system but unavailable due to lack of encryption keys and other credentials (for example, personal account data)

How to prevent data loss from happening? 


Frequent data backups are the most effective way of avoiding data loss in the majority of its forms. You need a schedule for the operation and a clear delineation of what kind of data is eligible for backups and what is not. Use data loss prevention software to automate the process. 

Geodiversity – i.e., when the physical location of the cloud servers in data centers is scattered and not dependent on a particular spot. This feature helps in dealing with the aftermath of natural disasters and power outages. 

One of the most infamous examples of data loss is the recent MySpace debacle

It resulted in 12 years of user activity and uploaded content getting lost. Here’s what happened. During a cloud migration process in 2015, it turned out that a significant amount of user data, (including media uploads like images and music), got lost due to data corruption. Since MySpace wasn’t doing backups – there was no way to restore it. When users started asking questions, customer support said that the company is working on the issue, and a couple of months later, the truth came out. This incident is considered to be another nail in the coffin of an already dying social network. 

Don’t be like MySpace, do backups.

4. Insecure API

Application User Interface (aka API) is the primary instrument used to operate the system within the cloud infrastructure. 

This process includes internal use by the company’s employees and external use by consumers via products like mobile or web applications. The external side is critical due to all data transmission enabling the service and, in return, providing all sorts of analytics. The availability of API makes it a significant cloud security risk. In addition to that, API is involved in gathering data from edge computing devices.

Multi-factor authentication and encryption are two significant factors that keep the system regulated and safe from harm.

However, sometimes the configuration of the API is not up to requirements and contains severe flaws that can compromise its integrity. The most common problems that occur are:

  • Anonymous access (i.e., access without Authentication) 
  • Lack of access controls (may also occur due to negligence)
  • Reusable tokens and passwords (frequently used in brute force attacks)
  • Clear-text Authentication (when you can see input on the screen)

The most prominent example of insecure API in action is the Cambridge Analytica scandal. Facebook API had deep access to user data and Cambridge Analytica used it for its own benefit. 

How to avoid problems with API? 

There are several ways:

  • Penetration testing that emulates an external attack targeting specific API endpoints, and attempting to break the security and gain access to the company’s internal information.
  • General system security audits
  • Secure Socket Layer / Transport Layer Security encryption for data transmission
  • Multi-factor Authentication to prevent unauthorized access due to security compromises. 

5. Misconfigured Cloud Storage

Misconfigured Cloud Storage is a continuation of an insecure API cloud security threat. For the most part, security issues with cloud computing happen due to an oversight and subsequent superficial audits.  

Here’s what happens.

Cloud misconfiguration is a setting for cloud servers (for storage or computing purposes) that makes it vulnerable to breaches. 

The most common types of misconfiguration include: 

Default cloud security settings of the server with standard access management and availability of data; 

Mismatched access management – when an unauthorized person unintentionally gets access to sensitive data;

Mangled data access – when confidential data is left out in the open and requires no authorization. 

A good example of cloud misconfiguration is the National Security Agency’s recent mishap. A stash of secure documents was available to screen from an external browser.  

Here’s how to avoid it.

Double-check cloud security configurations upon setting up a particular cloud server. While it seems obvious, it gets passed by for the sake of more important things like putting stuff into storage without second thoughts regarding its safety.

Use specialized tools to check security configurations. There are third-party tools like CloudSploit and Dome9 that can check the state of security configurations on a schedule and identify possible problems before it is too late.  

6. DoS Attack – Denial-of-service attack

Scalability is one of the significant benefits of transitioning to the cloud. The system can carry a considerable workload. 

But that doesn’t mean it can handle more unexpectedly. It can overload and stop working. That’s a significant cloud security threat. 

Sometimes, the goal is not to get into the system but to make it unusable for customers. That’s called a denial-of-service attack. In essence, DoS is an old-fashioned system overload with a rocket pack on the back. 

The purpose of the denial-of-service attack is to prevent users from accessing the applications or disrupting their workflow. 

DoS is a way of messing with the service-level agreement (SLA) between the company and the customer. This intervention results in damaging the credibility of the company. The thing is – one of the SLA requirements is the quality of the service and its availability. 

Denial-of-Service puts an end to that. 

There are two major types of DoS attack:

  • Brute force attack from multiple sources (classic DDoS),
  • More elaborate attacks targeted at specific system exploits (like image rendering, feed streaming, or content delivery) 

During a DoS attack, the system resources are stretched thin. Lack of resources to scale causes multiple speed and stability issues across the board. Sometimes it means an app works slow or it simply cannot load properly. For users, it seems like getting stuck in a traffic jam. For the company, it is a quest to identify and neuter the sources of the disruption, and also increased spending on the increased use of resources. 

2014 Sony PlayStation Network attack is one of the most prominent examples of denial-of-service attacks. It is aimed at frustrating consumers by crashing the system by both brute forces and being kept down for almost a day.

How to avoid a DoS attack?

Up-to-date Intrusion Detection System. The system needs to be able to identify anomalous traffic and provide an early warning based on credentials and behavioral factors. It is a cloud security break-in alarm.

Firewall Traffic Type Inspection features to check the source and destination of incoming traffic, and also assess its possible nature by IDS tools. This feature helps to sort out good and bad traffic and swiftly cut out the bad.

Source Rate Limiting – one of the critical goals of DoS is to consume bandwidth. 

Blocking of the IP addresses, that are considered to be a source of an attack, helps to keep the situation under control.

Other security risks and threats 

To get a clear picture, you should be aware of the following network security threats and risks that may appear on the cloud, as well as on-premise servers. 

Cloud-Unique Threats and Risks

  • Reduced Visibility and Control from customers
  • Separation Among Multiple Tenants Fails
  • Data Deletion is Incomplete

Cloud and On-Premise Threats and Risks

  • Credentials are Stolen
  • Vendor Lock-In Complicates Moving to Other CSPs
  • Increased Complexity Strains IT Staff
  • CSP Supply Chain is Compromised
  • Insufficient Due Diligence Increases Cybersecurity Risk

How to make your IT project secured?

Download Secure Coding Guide

In conclusion

The adoption of cloud technology was a game-changer both for companies and hackers. It brought a whole new set of security risks for cloud computing and created numerous cloud security issues. 

The shift to cloud technology gave companies much-needed scalability and flexibility to remain competitive and innovative in the ever-changing business operations. At the same time, it made enterprise data vulnerable to leaks and losses due to a variety of factors. 

Following the standards of cloud security is the best way to protect your company from reputational and monetary losses.

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Data Security and Privacy 

7 Types of Data Breaches and How to Prevent Them

 These days data breaches are as common as natural events like rain and snow. Every week you hear a story about it. The result is all the same – databases hacked and exposed. 

The consequences of company data breaches are pretty dire.

  • Sometimes it is the company’s reputation that suffers. 
  • Other times, the breach results in product shut down, as happened with Google+ when the news broke that there were some critical security issues.

Oddly enough, until very recently, companies weren’t taking the threat of data breaches seriously. Awareness of the real danger of data breaches started to grow after the frequency of data breach events began to grow exponentially.

In this article, we will explain: 

  • Why data breaches are happening? 
  • What are the seven major types of data leaks? 
  • How to avoid data breaches? 

What is a data breach?

A data breach is an abnormal event caused by a variety of factors connected by one common factor – inherent flaws of the security system that can be exploited.

The standard definition of a data breach is “a security event in which data intended for internal use is for some reason available for unauthorized access.” 

The nature of the so-called “internal data” may vary, but it is always something related to business operation. It might be:

  • Customer or employee personal data (for example, name, address, social security number, other identifiable data)
  • Payment or credit information (for example, in-app payments)
  • Access data (login, password, et al.) 
  • Corporate information (any internal documentation regarding projects or estimates, workflow process, status reports, audits, performance reviews, any financial or legal information, etc.)
  • Communication logs.

Why data breaches occur?

There are five most common causes of data breaches. Let’s look at them one by one:

1. Human error

Believe it or not, human error and oversight are usually among the main reasons why data breaches happen. 

Here’s why. 

The imposing nature of the corporate structure provides a false sense of security and instills confidence that nothing bad is going to happen inside of it. 

This detail paves the way for some slight carelessness in employee behavior. Technically, human error is an unintentional misconfiguration of document access. It may refer to: 

  • general storage accessibility (for example, private data being publicly available) 
  • the accessibility of specific documents (for example, sending data to the wrong person by accident).

2. Insider Threat

In this case, you have an employee with an agenda who intentionally breaches confidential and otherwise sensitive data. 

Why does it happen? 

Disgruntled employees are one of the reasons. One worker may feel wronged about his treatment and position in the company and this may lead to leaking information to the public or competition. 

Then there is corporate spying. The competition may convince one of the employees to disclose insider information for some benefits. 

In both cases, it is important to identify the source of data leaks (more on that later on).  

3. Social Engineering / Phishing

Social engineering is probably the gentleman’s way of doing company data breaches

This is when a criminal, who pretends to be an authorized person gains access to data or other sensitive information by duping the victim. 

Old-fashioned SE is when a criminal poses as somebody else and exploits the trust of the victim, as when Kevin Mitnick accessed the source code of the Motorola mobile phone by simply asking for it. 

Social engineering in electronic communication is known as phishing. 

In this case, the perpetrator imitates trustworthy credentials (the style of the letter, email address, logos, corporate jargon, etc.) to gain access to the information. Phishing is usually accompanied by malware injections to gain further access to the company’s assets (more on phishing later on.)

4. Physical action

Physical action data breach (aka “old school data breach”) – when the papers or device (laptop, smartphone, tablet, etc.) with access to sensitive information is stolen. 

Since companies encourage employee omnipresence and work on the go, this is a severe threat. How does it happen? A combination of sleight of hand and employee inattentiveness. 

However, due to increased security practices, and multi-factor authentication, the threat of stolen devices has significantly decreased. 

5. Privilege Misuse Data Breach

What is data privilege misuse? It is the use of sensitive data for purposes beyond the original corporate intent (like subscribing a corporate email list to a personal newsletter or changing the documents without following the procedure). 

Improper use of information is one of the most common ways corporate data breaches occur. The difference between privilege misuse and human error is the intention. However, privilege misuse is not always due to malicious intent. Sometimes the cause is inadequate access management and misconfigured storage settings. 

Privilege misuse results in various forms of data mishandling – like copying, sharing, and accessing data by unauthorized personnel. Ultimately, this may lead to a data leak to the public or a black market. 

How to Prevent Data Breaches? Solutions for 7 types of Breaches

In this section, we will describe the 7 most common types of data breaches and explain the most effective methods of preventing cyber breaches.

1.  Spyware malware

Spyware is a type of malicious software application designed to gather information from the system in a sneaky way. In a nutshell, spyware is keeping logs on user activity. This type of information includes: 

  • Input information – access credentials like logins and passwords. This type of spyware is also known as keyloggers.
  • Data manipulation of all sorts (working on documents, screening analytics, etc.) 
  • In addition, this can be used to capture an employee’s communication. 
  • Also, spyware can monitor video and audio input (specific for communication applications). Skype was known to have this vulnerability a couple of years ago. 
  • Files opened – to analyze the structure of the information and understand specific business processes; 

How does it happen?

The most common way of getting a piece of spyware is by unknowingly downloading a software program with a bit of spyware bundled with it. Also, spyware can be automatically uploaded through a pop-up window or redirect sequence. In a way, tracking cookies and pixels are similar to the spyware that acts almost in broad daylight. However, actual spyware is much more penetrative and damaging.

Usually, spyware is used in the initial stages of a hacking attack to gain necessary intelligence. In addition to that, spyware is one of the tools used for corporate spying.

An excellent example of a spyware attack is the WhatsApp messenger incident.  In May 2019, Pegasus spyware attacked WhatsApp. As a result, the malware had access to user’s ID information, calls, texts, camera, and microphone. 

How to fight spyware? 

  • Two-factor authentication to prevent straight-up account compromise;
  • Keep login history with details regarding IP, time, and device ID to identify and neutralize the source of unauthorized access;
  • Limit the list of authorized devices;
  • Install anti-malware software to monitor the system; 

2. Ransomware

Ransomware is a type of malware used to encrypt data and hold it for ransom in exchange for the decryption key. The ransom is usually paid in cryptocurrency because it is harder to trace. 

Ransomware is a no-brainer hacking option – its goal is to profit from the user’s need to regain access to the sensitive data. Since modern cryptography is hard to break with brute force, in the majority of cases, victims have to comply.

Usually, ransomware is spread by phishing emails with suspicious attachments or links. It can proceed due to careless clicking. Ransomware is also distributed through so-called drive-by downloads when a piece of malware is bundled with the software application or automatically uploaded by visiting an infected webpage.

For years, ransomware attacks were happening to individual users. Recently, ransomware attacks became frequent on larger structures. 

In March and May of 2019, ransomware virus, RobbinHood, attacked the government computer systems of Atlanta and Baltimore. It encrypted some of the cities databases and virtually paralyzed some aspects of the infrastructure. 

The cities governments were forced to pay the ransom to regain control over their systems. As it turned out, a combination of the following factors made the breach and subsequent ransomware attack possible:

  • lack of cybersecurity awareness of the personnel; 
  • outdated anti-malware software; 
  • general carelessness regarding web surfing.

How to avoid getting ransomware?

  • Use anti-malware software and keep it regularly updated.
  • Make a white list of allowed file extensions and exclude everything else.
  • Keep data backups in case of emergencies like ransomware infections. This detail will keep the damage to a minimum.
  • Set up a schedule for updating restoration points.
  • Segment network access and provide it with different entry credentials to limit the spread of malware. 

3. SQL Injection

These days, SQL injection is probably one of the most dangerous types of malware. It aims for data-driven applications. The use of such tools in business operations makes SQL injection a legitimate threat to a company’s assets. Data analytics, machine learning datasets, knowledge base – all can be in danger.

SQL is one of the oldest programming languages. Its field is data management in relational databases (i.e., the ones with data that relates to certain factors, like user IDs, prices for products, time-series data, etc.). These are the majority of databases. 

It is still in use because of its versatility and simplicity. These very same factors exploited by cybercriminals. The SQL injection is used to perform malicious SQL operations in the database and extract valuable information.

Here’s how it works:

  • To begin with, there is a flaw in the security of the page, and exploit. Usually, it is when the page involves a user’s direct input into the SQL query. The perpetrator identifies it and creates his input query known as the malicious payload.
  • Due to the simplicity of the system, this type of command is executed in the database.
  • With the help of the malicious payload, a hacker can access all sorts of data ranging from user credentials to targeting data. In more sophisticated cases, it is possible to gain an administrator-level of control over the server and run roughshod over it. 
  • In addition to this, a hacker can alter and delete data, which is a piece of awful news when it comes to financial and legal information.

One of the most infamous incidents of SQL injections, that led to a massive data breach, is the 2012 LinkedIn incident. It resulted in a data leak of over six million passwords. 

Curiously, LinkedIn had never confirmed that the leak was caused by SQL injection despite all the facts pointing to it. The reason for this is simple. SQL injections happen because they are allowed to occur by negligence and overconfidence. They are straightforward to predict – if there is the possibility, sooner or later it will be exploited. This nuance makes SQL injection a very embarrassing type of breach.

How to prevent data breaches with SQL injection? There are several ways:

  • Apply the principle of least privilege (POLP) – each account has access limited to one specific function and nothing more. In the case of a web account, it may be a read-only mode for the databases with no writing or editing features by design.
  • Use stored procedures (aka prepared statements) to limit SQL command variables. This feature excludes the possibility of exploiting the input query. 

4. Unencrypted backup data breaches

Backup storage is one of the critical elements in the disaster recovery strategy. It is always a good thing to have a copy of your data just in case something terrible happens to it. 

On the other hand, encryption is one of the critical requirements of modern asset management. It is a reasonable approach. If the data is encrypted, it hurts less if it leaks since it not useful in that state. And, it seems obvious to have storage and transmission channels encrypted by default. 

However, backups are usually left out of the equation. Why? Because, by their nature, backups seem to be a precaution in and of themselves and thus treated as a lesser asset for the company’s current affairs. 

Add to that the aforementioned false feeling of safety behind a corporate firewall. Also, backup encryption is an additional weight on the security budget, which is often already strained. The latter is usually the reason why encrypted backups are not a persistent practice. 

This is a big mistake because one party’s carelessness is another person’s precious discovery. 

The most common problem with backups is weak authentication like a simple combo of login and password without any additional steps. 

How to prevent data breaches due to unencrypted backups? There are several ways:

  • Encrypt your backups with specialized software
  • Keep the backup storages to the same security standard as main servers (i.e., internal network-only type of access with two-factor authentication by default)

The most egregious example of a data breach via unencrypted backup happened in 2018. The Spanish survey software company Typeform had experienced a massive data breach due to unencrypted backups being exposed and downloaded by cybercriminals. Numerous companies and even government organizations were using the service. That made surveys a rather diverse source of sensitive information, including person-identifying data and payment-related information. 

The breach had severe repercussions for the company. In addition to being forced to apologize to the customers, Typeform started losing their clients. Many of the companies had decided to opt-out of the service. Don’t be like Typeform, encrypt your backups.

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5. API Breaches due to unrestricted calls 

In some ways, API is almost like Pandora’s box. You know what it is supposed to do, but you never really know what kind of trick can be pulled off with its help. As one of the essential tools for the application operation, API is a treasure trove of information for those who know where to look.

That is how the whole Cambridge Analytica debacle happened with Facebook. Hackers had exploited the progressive structure of Facebook API (which provided rather deep access to user data) and turned it into a powerful tool for diverse data mining. 

As a result, they managed to collect the data of more than 50 million users. Among the data gathered were such things as likes, expressed interests, location data, interpersonal relationship data, and much more.  

What happened next? The scandal got so big, Facebook CEO Mark Zuckerberg was forced to discuss the matter at Senate hearings. In addition to that, the company received a permanent stain on their reputation and a massive user withdrawal. The subsequent investigation led to a whopping $5 billion fine by the Federal Trade Commission. 

Such API breaches could have been avoided if it was a bit more thought-through. 

How can API become a data breach risk? 

  • Anonymous access (i.e., access without authentication) 
  • Lack of access monitoring (may also occur due to negligence)
  • Reusable tokens and passwords (frequently used in brute force attacks)
  • Clear-text authentication (when you can see input on the screen)

How to prevent data breaches and make API safe and secure? There are several ways:

  • Provide thorough access restriction and delimit what kind of data is accessible via API and that which is not; 
  • Use rate-limiting to keep data transmission under reasonable boundaries. This feature will prevent API from being used in a data mining operation. 
  • Use anomaly and fraud detection tools to identify suspicious behavior in the API to block it.  
  • Perform audit trail to understand what kind of request is going through API 
  • Clearly explain to users which types of data you are sharing with third parties via API

6. Access Management and Misconfigured cloud storage

Cloud security is probably the most robust field of cybersecurity as it requires a lot of auditing and constant testing of the system for all sorts of weaknesses. One of the biggest problems with cloud storage is access management due to misconfigured cloud storage settings. 

Here’s what it means: 

  • Access management in cloud infrastructure is a mess. All users in the system have certain levels of access to certain kinds of data. 
  • Because there is a need to share information to enable business operation, there is a high volume of access turnaround. 
  • Sometimes it goes unchecked, and unauthorized users may end up having access to sensitive data they are not supposed to have access to. 

At the same time, there is a thing with cloud security settings. Maintaining databases and storage in the cloud means you need to keep an eye on the accessibility of the information. Since there is a lot of data coming in and out, it is essential to keep things strict. 

Here’s what may happen. 

  • Some of the data may end up on the public side due to oversight and inadequate default accessibility settings. 
  • The data may be visible on the outside, and it is a significant exploit for cybercriminals. 
  • With a little help of specialized search engine requests, one can get a lot of exciting stuff. 

A good example of cloud misconfiguration is the U.S. Army’s Intelligence and Security Command AWS server security mishap. A stash of classified NSA documents was publicly accessible due to an access configuration oversight. It was that simple. Upon sharing the folder, someone failed to check the accessibility status and made the thing public.

  • Here’s how to avoid this kind of data breach
  • Check the cloud security configurations upon setting up particular storage. Be sure it is strictly private. 
  • Use access management tools to keep an eye on security configuration. There are third-party tools that can routinely check the state of security configurations and detect issues upon their occurrence.  

7. Malicious Insider Threat

Insider Threat is probably the most persistent source of data breaches. You never know what may trigger this kind of behavior. While the aforementioned types of data breaches are all about the technology, this one is about a person being nasty and acting maliciously. 

Aside from human error and negligence (that leads to such types of data breaches as malware and access misconduct), there are three main types of malicious insider threat:

  • Disgruntled Employees – this kind of insider threat is all about getting back at those who did the particular employee wrong. According to a study by Gartner, 29 percent of employees have stolen corporate data for personal gain after quitting. Then there is the 9% of those who just wanted to sabotage the process one last time. 
  • Second streamers are much more serious trouble. These are the people who systematically disclose sensitive information for personal gain and supplementary income. According to the Gartner study, these are 62% of all insider threats. Second streamers are dangerous because they know what they are doing and they try to remain in the system for as long as possible without getting caught. In this case, data breaches occur in a slow, barely detectable manner, disguised as a casual business process. 

There are several ways to avoid data breaches caused by insider threat:

  • Implement strict access control over sensitive data. If there is a document to be shared with an unauthorized person – set a limit of accessibility and disable copying of the document.
  • Keep thorough activity logs on what is going on within a system. Set an alarm for suspicious activity like unusually large data exports and copying (like the whole contact database transfer and so on) or unauthorized access. Every cloud platform has its own logging tools. For example, here’s how this thing works on Google Cloud.
  • Perform an audit trail to identify the source and determine the context and content of the anomalous event, and identify the source of anomalous activity. This can be handled by Data Loss Protection software like McAfee DLP.


In the age of big data and exponentially growing cloud services – data breach is just one aspect of everyday life. It is definitely an unfortunate thing if it happens, but as it was explained above – it is far from inevitable. 

All it takes to avoid data breaches from happening is keeping a close eye on what is going on with the data and where it is going. Knowledge is half the battle won – you need to be cautious about the value of your data and the ways it can be exposed. 

In this article, we have shown you exactly how to lessen the risks of data breaches and wholly avoid such events. 

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10 Steps for Building a Successful Cloud Migration Strategy

Imagine that you recently launched a social networking app. To host the app’s infrastructure, you decided to use the existing on-premise server because you do not expect it to handle many users immediately. Your app is going viral and, during just one month, over 1000, 000 users downloaded and used it on a daily basis. Do you know what will happen next? Since your server infrastructure was not ready for such huge loads, it will now not work correctly. Thus, instead of your apps’ interface, users will see an error message and you will lose a significant amount of them because your app failed to live up to their expectations. 

To avoid situations where you jeopardize user trust, use cloud platforms for both hosting databases and running app infrastructure. 

Such data giants as Facebook, Netflix, and Airbnb, already adopted a migration strategy to the cloud due to cheap costs, auto-scaling features, and addons as real-time analytics. Oracle research says 90% of enterprises will run their workloads on the cloud by 2025. If you already run data centers or infrastructure with an on-premise environment, and you will need more capacity in the future, consider migrating to the cloud as a solution.  

Yet, to migrate to the cloud is not as simple as it seems. To successfully migrate to the cloud you need, not only an experienced developer but also a solid cloud application migration strategy. 

If you are ready to leverage cloud solutions for your business, read this article to the end. 

By the end of this blog post, you will know about cloud platform types and how to successfully migrate to cloud computing.

Cloud migration strategies: essential types

Migration to the cloud means transferring your data from physical servers to a cloud hosting environment. This definition is also applicable for migrating data from one cloud to another platform. Migration in cloud computing includes different types, due to the number of code changes developers need to conduct. The main reason is that not all data is ready to be moved to the cloud by default.

Let’s go through the main types of application migration to the cloud one by one. 

  • Rehosting. This is the process of moving data from on-premise storage and redeploying it on cloud servers. 
  • Restructuring. Such a migration requires changes in the initial code to meet the cloud requirements. Only then can you move the system to a platform-as-a-service (PaaS) cloud model. 
  • Replacement migration means switching from existing native apps to third-party apps. An example of replacement is migrating data from custom CRM to Salesforce CRM. 
  • Revisionist migration. During such a migration, you make global changes in the infrastructure to allow the app to leverage cloud services. By ‘cloud services’ we mean auto-scaling, data analytics, and virtual machines. 
  • Rebuild is the most drastic type of cloud migration. This type means discarding the existing code base and building a new one on the cloud. Apply this strategy if the current system architecture does not meet your goals. 

How to nail cloud computing migration: essential steps

For successful migration to the cloud, you need to go through the following steps of the cloud computing migration strategy. 

Step 1. Build a cloud migration team 

First, you need to hire the necessary specialists and employ the distribution of roles. In our experience, a cloud migration team should include: 

  • Executive Sponsor, a person who handles creating a cloud data migration strategy. If you have enough tech experience, you can take this role. If not, your CTO or a certified cloud developer will ideally suit you. 
  • Field General handles project management and migration strategy execution. This role will suit your project manager if you have one. If not, you can hire a dedicated specialist with the necessary skills. 
  • Solution Architect is an experienced developer who has completed several cloud migration projects. This person will build and maintain the architecture of your cloud. 
  • Cloud Administrator ensures that your organization has enough cloud resources. You need an expert in virtual machines, cloud networking, development, and deployment on IaaS and PaaS. 
  • Cloud Security Manager will set up and manage access to cloud resources via groups, users, and accounts. This team member configures, maintains, and deploys security baselines to a cloud platform. 
  • Compliance Specialist ensures that your organization meets the privacy requirements. 

Step 2.Choose cloud service model 

There are several types of cloud platforms. Each of them provides different services to meet various business needs. Thus, you need to define your requirements for a cloud solution and select the one with the intended set of workflows. However, this step is challenging, especially if you have no previous experience with cloud platforms. To make the right decision, receive a consultation from experienced cloud developers. But, to be on the same page with your cloud migration team, you need to be aware of essential types of cloud platform services, such as SaaS, PaaS, IaaS, and the differences between them.

  • SaaS (Software as a Service)

Chose SaaS to receive advantages of running apps without maintaining and updating infrastructure. SaaS providers also offer you cloud-based software, programs, and applications. SaaS platforms charge a monthly or yearly subscription fee. 

  • IaaS (Infrastructure as a Service)

This cloud model suits businesses that need more computing power to run variable workloads with fewer costs. With IaaS, you will receive a ready-made computing infrastructure, networking resources, servers, and storage. IaaS solutions apply a pay-as-you-go pricing policy. Thus, you can increase the cloud solution’s capacity anytime you need it. 

  • PaaS (Platform as a service)

Chose this cloud platform type for adopting agile methodology in your development team, since PaaS allows the faster release of app updates. You will also receive an infrastructure environment to develop, test, and deploy your apps, thus increasing the performance of your development team.

cloud migration cloud service models

Step 3. Define cloud solution type

Now you need to select the nature of your cloud solution from among the following:

  • Public Cloud is the best option when you need a developing and testing environment for the app’s code. Yet, the public cloud migration strategy is not the best option for moving sensitive data. Public clouds include high risks of data breaches. 
  • Private Cloud providers give you complete control over your system and its security. Thus, private clouds are the best choice for storing sensitive data.
  • The hybrid cloud migration strategy combines both public and private cloud solutions characteristics. Chose a hybrid cloud to use using a SaaS app and get advanced security. Thus, you can operate your data in the most suitable environment. The main drawback is tracking various security infrastructures at once, which is challenging.

Step 4. Decide the level of cloud integration

Before moving to cloud solutions you need to choose the level of cloud integration among shallow and deep integration. Let’s find out what the difference is between them. 

  • Shallow cloud integration (lift-and-shift). To complete shallow cloud migration, developers need to conduct minimal changes to the server infrastructure. However, you can not use the extra services of cloud providers. 
  • Deep cloud integration means adding changes to an app’s infrastructure. Chose this strategy if you need serverless computing capabilities (Google Cloud Platform services), and cloud-specific data storage (Google Cloud Bigtable, Google Cloud Storage).

Step 5. Select a single cloud or multi-cloud environment

You need to choose whether to migrate your application on one cloud platform or use several cloud providers at once. Your choice will impact the time required for infrastructure preparation for cloud migration. Let’s look at both options in more detail. 

Running an app on one cloud is a more straightforward option. Your team will need to optimize it to work with the selected cloud provider and learn one set of cloud API. But, this approach has a drawback – a vendor lock-in. It means that it will be impossible to change the cloud provider. 

If you want to leverage multiple cloud providers, choose among the following options: 

  • To run one application set on one cloud, and another app’s components on another cloud platform. The benefit is that you can try different cloud providers at once, and choose where to migrate apps in the future. 
  • To split applications across many different cloud platforms is another option. Thus, you can use the critical advantages of each cloud platform. However, consider that the poor performance of just one cloud provider may increase your app’s downtime. 
  • To build a cloud-agnostic application is another option that allows you to run the app’s data on any cloud. The main drawback is the complicated process of app development and feature validation.

Step 6. Prioritize app services

You can move all your app components at once, or migrate them gradually. To find out which approach suits you the best, you need to detect the dependencies of your app. You can identify the connections between components and services manually or generate a dependencies diagram via a service map. 

Now, select services with the fewest dependencies to migrate them first. Next, migrate services with more dependencies that are closest to users.

Step 7. Perform refactoring

In some cases, you will need to make code refactoring before moving to the cloud. In this way, you ensure all your services will work in the cloud environment. The most common reasons for code refactoring are: 

  • Ensuring the app performs well with different running instances and supports dynamic scaling 
  • Defining the apps’ resource use dynamic-cloud capabilities, rather than allocating them beforehand

Step 8. Create a cloud migration project plan

Now, you and your team can outline a migration roadmap with milestones. Schedule the migration according to your data location and the number of dependencies. Also, consider that, despite the migration, you need to keep your app accessible to users. 

Step 9. Establish cloud KPIs

Before moving data to a cloud, you need to define Key Performance Indicators. These indicators will help you to measure how well it performs in the new cloud environment. 

In our experience, most businesses track the following KPI’s:

  • Page loading speed
  • Response time
  • Session length
  • Number of errors
  • Disc performance
  • Memory usage

And others. You can also measure your industry-specific KPIs, like the average purchase order value for mobile e-commerce apps.

Step 10. Test, review, and make adjustments as needed

After you’ve migrated several components, run tests, and compare results with pre-defined KPIs. If the migrated services have positive KPIs, migrate other parts. After migrating all elements, conduct testing to ensure that your app architecture runs smoothly. 

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Cloud migration checklist from The APP Solutions

Cloud providers provide different services to meet the needs of various businesses. You need help from professionals to choose the right cloud solution. 

We often meet clients who have trouble with selecting a cloud provider. In these cases, we do an audit of a ready-made project’s infrastructure. Next, we help clients to define their expectations for the new cloud environment. To achieve this, we show a comparison of different cloud providers and their pros and cons. Then, we adopt a project for a cloud infrastructure, which is essential for a successful migration. 

When looking for a cloud provider, consider the following parameters: 

  • Your budget, which means, not only the cost of cloud solutions but also the budget for cloud migration
  • The location of your project, target audience, and security regulations (HIPAA, GDPR)
  • The number of extra features you want to receive, including CDN, autoscaling, backup requirements, etc. 

Migration to a cloud platform is the next step for all business infrastructures. However, you need to consider that cloud migration is a comprehensive process. It requires, not only time and money but also a solid cloud migration strategy. To ensure your cloud migration is going right, you need to establish and track KPIs. Fill in the contact form to receive a consultation or hire a certified cloud developer.

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EMR Integration in 2023: What You Need to Know

EMR integration’s significance is undeniable; it enables better decision-making, reduces medical errors, and boosts patient engagement. Electronic Medical Systems function independently, but for optimal results, they need to interact. Regrettably, many hospitals don’t practice this.

Our experience with Bueno clarified the issue. Bueno applies machine learning to analyze user’s EHR data, ensuring timely preventive care. The app shares this data with the healthcare team to advise patients on check-ups, lab tests, or symptom watch.

But there was a hurdle. Healthcare providers could see the data, but accessing records from different platforms was a struggle. To solve this, we merged various solutions, consolidating all data in one spot. We used platforms like Orb Health, Validic, and Mayo Clinic.

Today, we’re aware that EMR integration issues still persist in many medical firms. In this article, we’ll guide you on connecting different EMRs, explain its necessity, and discuss potential challenges.

What is the EMR system?

An EMR system is a digital platform stored in the cloud, holding patient medical data. In the not-so-distant past, medical data was etched on paper, stored in bulky folders, and piled high on shelves. Clinicians had to leaf through these volumes, laboriously seeking the information they needed to make swift diagnoses. However, with EMR systems, this relic of a practice is no longer a necessity.

Imagine no longer battling with ink and paper, but rather smoothly navigating a sleek digital platform. This digital library, or EMR, neatly organizes and securely stores patient data. It’s a resource for medical history, diagnostic data, lab test results, appointments, billing details, and more.

It’s not only doctors who have access to this knowledge. Patients, too, can step into this library. Through a digital door known as a patient portal, they can glance at their health story unfolded.

Every prescribed medicine, every immunization, every treatment plan is at their fingertips, as well as the doctors’. Informed decisions can then be made, not only based on a single page of information but the entire medical narrative of the patient. The EMR system, hence, is a potent tool empowering both the healthcare provider and the recipient, and typically includes:

  • Medical history
  • Diagnostic information
  • Lab test results
  • Appointments
  • Billing details
  • Prescription and refill data from pharmacies
  • Patient portals
  • Treatment plans
  • Immunization records

What Are Examples of EMR Platforms?

There are over 600 EMR vendors according to review sites. However, we’ll focus on discussing those we’ve successfully integrated at The APP Solutions. We’ll share our experiences with CernerAmbulatory, Epic EMR, DrChrono, and eClinicalWorks.


Cerner, a US medical software titan, delivers digital health data solutions. It caters to multispeciality and smaller practices. Key offerings include Cerner Powerchart, Caretracker, and Cerner Millenium.

Key Features: population health, revenue cycle, medical reporting, lab information, and patient portal.

Cerner Pros:

  • Strong interoperability promotes collaboration.
  • Cost-effective for small practices.
  • Advanced patient portal for health information.
  • Software can mimic practice’s branding.

Cerner Cons:

  • Fewer integrations, such as CRM.
  • Regular updates can pose learning challenges.

Epic EMR

Epic EMR is a hospital favorite, holding medical records for over 253 million Americans. It shines in large settings. Notable features are telemedicine, billing, e-prescription, templates, and analytics.

Epic EMR Pros:

  • Detailed patient information reports.
  • Telehealth for remote consultations.
  • AI and analytics to enhance decision-making.

Epic EMR Cons:


DrChrono provides web and app-based EMR systems. It assists with appointments, reminders, and billing, automating routine tasks.

Key Features: patient charting, telehealth, appointment scheduling, and reminders.

DrChrono Pros:

  • Affordability benefits small or new practices.
  • Comprehensive training for software admins.
  • Secure direct messaging for patients and doctors.

DrChrono Cons:

  • No Android app for doctors.
  • Limitation on appointment reminder methods.


eClinicalWorks supplies digital health records, patient management, and population health solutions. It caters to over 4000 US clients. Key features are revenue cycle management, patient portal, wellness tracking, activity dashboard, and telehealth.

eClinicalWorks Pros:

  • Operates on multiple platforms like Mac and Windows.
  • User-friendly interface.
  • Interoperability connects with other systems.

eClinicalWorks Cons:

  • Pricey for small practices.
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Why Is EMR Integration Important for Healthcare Companies?

The healthcare sector is one of the world’s top data generators. It is critical that the data generated is collected and accessible from a single point. The main reasons why integrating EMR is important will be discussed below.

Securing Sensitive Information 

Healthcare, a prime target for cyberattacks, experienced 5.8% of total cyber-attacks in 2022. They focused on health records. EMR systems, HIPAA-compliant, strengthen data security. They safeguard patient records against cyber threats and natural disasters.

Streamlining Data Access

EMR integration offers a solution for data fragmentation. It consolidates patient records, making them easily accessible. So, doctors can view complete patient histories at a glance. This aids in accurate diagnoses.

Enhancing Workflow

Consider the effect of a unified report system. It would compile laboratory, pharmaceutical, and dental department data. This results in efficiency. Doctors make quicker decisions. They don’t wait for paper-based results. Automated record collection lightens staff workload too.

Safeguarding Patient Safety

Keeping patient data in different systems can cause errors. In fact, medical mistakes are the third leading cause of death in the U.S. EMR integration helps. It detects errors in record keeping, thereby promoting patient safety.

Improving Healthcare Outcomes

Access to complete patient information benefits healthcare providers. It leads to better understanding of patients’ conditions and helps doctors diagnose accurately. Also, timely access to records informs the design of preventive measures.

Boosting Patient Engagement

EMR systems do not just serve healthcare professionals. Patients also access their information. This breeds interest and empowerment. Patients become proactive in managing their health. Plus, easy doctor access via telehealth lessens the stress of physical consultations.

Are There Any Challenges?

Healthcare providers often hesitate to integrate Electronic Medical Records due to its complexity. Let’s explore the most common issues.

Cost Barrier: How Affordable Are EMR Solutions?

Deploying an EMR system can burn a hole in your pocket. Initial implementation may require you to shell out around $100,000. Small-sized practices might find this cost daunting. But, don’t worry. More wallet-friendly options like pre-built systems exist. Take Dchrono for instance. With a monthly fee of just $19, it’s a suitable pick for growing establishments.

However, be mindful if you’re eyeing free EHRs. In fact, we don’t recommend open source systems. They usually come with restrictions – lack of customization and a ceiling on patient data storage. Moreover, the choices for free EHR systems are slim. Due to their vital role in healthcare – with lives at stake – most prefer not to risk relying on a totally free, open-source EMR.

Compatibility with Legacy Systems 

Facilities already having EMR systems might wish to unite them through a single solution. However, finding one that fits all systems like a glove is a considerable challenge. The different systems might store data in diverse formats, complicating the integration process.

Transitioning Data

Migrating data from paper to digital, while linking it all, demands considerable effort. It might take weeks or months to transfer all health information completely. During this phase, potential information loss could shake patient trust. Careful planning and adequate time allocation can help manage this issue effectively.

Data Protection 

A tough nut to crack in EMR integration is securing private data. With medical records susceptible to breaches, it’s crucial to ensure watertight security. As an illustration, in 2021 alone, cyber-attacks exposed over 45 million records. To combat this, opt for a HIPAA-compliant vendor with a strong security framework.

Human Errors (Training and Adaptation)

Human-related challenges could put a spoke in the wheel of EMR integration. Resistance from staff towards the new system, incorrect data entry, and lack of training are common obstacles. Implementing a thorough training regimen can help staff adjust to the EMR software, ensuring accurate health record entry.

Navigating Interoperability

Interoperability lets healthcare providers share patient data. For interoperability to be comprehensive, FHIR and HL7, and other interoperability standards come into play.  

If you want to know more about them, check out our post on the differences between HL7 and FHIR

That said, achieving smooth data exchange isn’t that simple.

Firstly, not all systems speak the same ‘language’. We’ve got multiple data formats to deal with. Translating them so they align is a Herculean task.

Additionally, ensuring data safety while exchanging it between systems is tough. Security has to be top-notch. A single leak can breach patient privacy.

It’s also about change – old habits die hard. Many healthcare providers are still adjusting to new protocols. It takes time to shift from traditional methods.

Step-By-Step Guide to EMR Implementation 

Here’s a roadmap to help you through integrating your EMR.

Phase 1: Blueprint of Preparation

Begin your EMR integration journey with meticulous planning. Identify the needs of your practice, devise your strategy, set goals, and allocate time for staff training and the overall implementation process. The size of your practice and the volume of data to handle are crucial to your planning.

Phase 2: Structuring the Design

The next stage is design. You’ll need to consider the features you want in your EMR system. Focus on developing a tailor-made solution that connects all your EMRs and ensures an easy-to-navigate interface for your staff.

Should you desire a patient portal and telehealth functionalities, incorporate a mobile-friendly design. Consider engaging a development team to help with coding architecture at this stage.

Phase 3: Building the Infrastructure

Next, transform your design into functional software. This phase entails converting data from diverse formats across various EMRs. Given the potential risk of errors, which could compromise patient safety, it’s paramount to ensure accurate conversion of data. Always double-check to mitigate mistakes.

Phase 4: Testing the Functionality

Post-construction, the system needs to be rigorously tested. This step aims to identify any bugs, gauge user interactions, and evaluate the system’s reliability, data precision, and impact on your operations.

Phase 5: Activation and Launch

Finally, you’re ready to go live. Ensure your system complies with HIPAA regulations for health data security. Be open to feedback from users to facilitate continuous improvement.

Upon successful implementation, your new system should improve operational efficiency for your staff and enhance patient health outcomes.

Phase 6: Empowering through Training

Staff training is a critical aspect of EMR integration. Compile a comprehensive training manual to guide your staff through the new system. As not all employees may be tech-savvy, split the training into manageable segments for ease of comprehension.

EHR Vs. EMR Integration: Which Is Right for Your Practice?

Before going digital, you must pick. Is EHR or EMR right for you? Here’s how they compare. 



Data Scope

Records patient data in one practice.

Stores patient data from all providers.


Shares data within one practice.

Shares data with multiple health professionals.

Data Transfer

Transferring data is difficult.

Transferring data is easier.

Data Focus

Focuses on diagnosis and treatment.

Gives a broad view of patient’s care.

Patient Access

Mainly for providers’ use.

Patients can also access their records.

Care Continuity

Good for tracking data in one practice.

Better for sharing updates with other caregivers.

Your choice between an EHR and an EMR depends on the needs of your practice and your patients. If you value a comprehensive, shareable, and patient-involved approach, an EHR might be a better fit. On the other hand, if you’re a single practice focusing on diagnosis and treatment, an EMR may suit you best.

Choosing a Healthcare Integration Service

Healthcare integration services, like EMR, ERP, and EHR, manage health information. When selecting one, you need to consider several key factors:

Growth Capability

When setting up an integration system, think long-term. Partner with an experienced vendor. They can help you grow your operations without losing data.

Data Safety

You will handle private data. So, your vendor must prioritize security. They should have proper industry certification. Also, they must understand HIPAA and other compliance needs.


Don’t entrust data management to an inexperienced vendor. Read reviews of different vendors. Talk to their current or past clients to judge their skills.


Avoid vendors with slow, inflexible systems. Choose a vendor that can adapt to your specific needs. This prevents unnecessary additions and keeps costs down.

Customer Service

Your vendor should provide excellent support. Fast responses to issues can prevent major downtime. This keeps your patients satisfied.


Implementing an EMR system brings great benefits to healthcare providers. Despite challenges like costs, the rewards are greater. To implement the steps we discussed, you need a skilled software development company.

The APP Solutions is that company. We’re qualified to build your EMR integration system. We support you through every development stage, from defining business goals to selecting the best vendor for your practice.

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