Both scalability and compatibility are a fundamental component of enterprise software. Designating them as “top priority” from the start leads to lower maintenance costs, better user experience, and higher agility. In every software, tradeoffs must be made. It is often done to meet a project’s requirements, whether those are technical or financial and usually cost is prioritized over scalability or compatibility concerns. Unfortunately, the practice is common in big data initiatives where such decisions usually sink the project.

Scalability and compatibility should never be an afterthought. In this post, we will walk you through the ways you can solve scalability and compatibility issues with modern development methods, along with some other tips.


Application incompatibility across different platforms poses a major problem for developers due to the contrasting features in computing environments. Missing libraries, non-existent dependencies, and other obstacles reveal incompatibilities that will impede or lengthen the development process.

“Problems arise when the supporting software environment is not identical. You’re going to test using Python 2.7, and then it’s going to run on Python 3 in production and something weird will happen. Or you’ll rely on the behavior of a certain version of an SSL library and another one will be installed. You’ll run your tests on Debian and production is on Red Hat and all sorts of weird things happen. The network topology might be different, or the security policies and storage might be different but the software has to run on it.” – Solomon Hykes, Creator of Docker

In many cases, software will be used on a variety of unanticipated platforms. The optimal solution is to deploy the software in independent packages that are self-sufficient and contain their own run-time environment. When dependencies are platform agnostic, you will be able to achieve portability and flexibility.

Solutions such as containers and virtualization tackle the compatibility problem, but each has a different approach.

Containers enable software to run reliably when moved from one computing environment to another. It consists of an entire runtime environment: an application with all of its dependencies, libraries, configuration files, and other binaries. By containerizing applications, software developers abstract the differences in infrastructure and the underlying operating system distributions.

Virtualization works in a similar way but is less efficient than containerizations. Each package in a virtualized application requires its own OS in order to operate. We will cover virtualization in more detail ahead.

Scalability and Efficiency:

A VM package (app+os) is a “guest” on the physical machine on which it runs and relies on a hypervisor to allocate physical computing resources. Since physical machines run multiple applications simultaneously, VMs can quickly become resource hogs.

It is clear that running a complex set of applications on a single physical server would incur huge server bills and likely cause maintenance issues.

The problems caused by inefficient usage of resources are multiplied when an app needs to serve a larger user base. There are a lot of questions that arise when designing an app for a large or fluctuating audience. Virtualization simply cannot be the solution! It would require too many resources if the user base exceeds expectations. How are we going to save costs on server bills if we over-estimate?

The solution is readily available in the form of cloud-computing resources – which can scale as per need. Providers including Amazon, Google, and Microsoft all have scalable cloud infrastructure which addresses the issue head-on. Want more resources during the Super Bowl to handle all that traffic from the ad campaign seen by millions? You got it! Slowing down on promotions in off-season and wanting to conserve server power? Scale down in one click. Scalability allows you to cater to small audiences whilst having the capability to handle millions of users.

To achieve app scalability on such cloud platforms, your application needs to be designed to handle it. Monolithic apps, for example, which are developed as a single unit, are not built with scalability in mind.

Breaking down a monolithic app into small functional chunks makes the process much simpler. These chunks, or microservices, can be achieved through containerization and are smaller parts of a huge app, making them easier to manage, maintain and deploy. Problems with individual micro-servers can be isolated so that they do not impact the entire platform.

Containers package code and its dependencies, such as configuration files, into standard units of software. This enables applications to run reliably from one computing environment to another, such as a local desktop, physical server, virtual server, production environment, or any type of cloud infrastructure. This portability enables organizational flexibility, speeds up the development process, and makes it easier to switch between vendors if need be.

Another major benefit of containers is that they support horizontal scaling, meaning you can stack identical containers within a cluster. Smart scaling allows you to run only the necessary container at any given time thus minimizing costs drastically and maximizing ROI. Container technology and horizontal scaling have been used by major vendors like Google, Twitter and Netflix for years now.

Containers solve the same problems as virtualization while utilizing a fraction of the resources. This is because containerized apps are built with efficiency in mind.

Since containers do not require separate operating systems, they use up far fewer resources. While a VM is often several gigabytes in size, a container is usually only a few dozen megabytes. Thus, it is possible to run many more containers than VMs on a single server without compromising on speed or performance. Since containers require lower hardware utilisation, this will result in a reduction of bare metal costs and well as data center costs as well.

In software development, code efficiency is a form of currency. A program written in 100 lines might work with 50. While this might not seem significant, a large app can contain millions of lines of code and these savings translate directly into ROI when you analyse the associated server costs, maintenance costs, testing costs, and so on. At the same time, this code can be shared with multiple services. Write once, run anywhere.

Reusing the code saves time and resources. It leveraging existing tools, libraries or other code. A software component which has taken weeks to develop has the potential to serve other projects, saving the organization several weeks if another project reuses that component. This helps reduce budgets on big and small projects. In other cases, code reuse makes it possible to complete projects that would have been impossible if the team were forced to start from scratch. Containers also facilitate reusability. It can be difficult and wasteful for IT professionals to move an application to a new platform or operating system using traditional methods. With containers, the same code can be shared or deployed to multiple platforms without being re-written.

Other benefits of containers:

  • Productivity: Containers allow teams to focus solely on their key day-to-day concerns while developers concentrate on dependencies and logic.
  • Security: Containers are well suited for situations where use in software where security is a primary concern. Containers are decoupled and do not interact with each other. That means if your business is running a series of containers and one of them crashes, the others will keep running without interruption. Furthermore, if one container is hacked, the impact is easily – as the name suggests – contained.
  • Automation: Automation is baked into the way containers work. While processes such as creation and tear down of OS images can be automated in VMs as well, it is usually an afterthought and not something inherent to VMs. Orchestration is a major differentiator for Docker compared to VMs. They are architectured to be created on demand, shutting down when not needed. Containers can be controlled by automated systems via APIs, with larger computing environments utilizing management and automation layers to manage and automate procedures in real-time. New instances of applications can be created or deleted with no human intervention required.