Data-centric applications are not typical, and the environment where they’re deployed are also not typical. Most often, they’re deployed into large, distributed network of machines, which compels the question of whether DevOps practices and tools can be applied to distributed applications.
To be successful, DevOps practices for data-centric applications must work smoothly even when production environment is a distributed network. For that to happen, developers and testers must define best practices for DevOps as well as continuous deployment into these distributed network environments.
Can DevOps Work in Distributed Environments?
Let’s say you’ve adopted a distributed network environment for your data-centric application production environment. Unless you’re able to successfully and rapidly develop your application for such an environment, it’s a wasted effort. Although DevOps is the solution, how many DevOps tools allow you to develop applications in distributed network environments?
Here are three critical steps to ensure DevOps success in a distributed network environment:
- Containerize applications
- Use DevOps tools to enable a continuous DevOps life cycle
- Use sandboxes throughout the DevOps life cycle
Why are they important? The above three steps help you address the what, how and where aspects of your data-centric DevOps problem:
- The What (Containers) —A distributed network environment, most likely, is heterogenous and consists of different types of systems and applications. Putting your application within containers will make them look uniform and allow you to easily move them from non-production to production environments, as well as from on-premises systems to cloud.
- The How (DevOps Tools) — To rapidly develop your application you’ll need to quickly iterate through the program-test-deploy cycle. DevOps tools help automate these steps.
- The Where (Sandboxes) — Sandboxes help you decide where you develop your application so that its infrastructure and environment looks the same whether you access it from development lab, or testing lab, or production data center or anywhere else.
Of these, two steps are common to both data-centric and non-data-centric environments. Containerizing applications is definitely important to data-centric applications whose requirements don’t seem to affect DevOps tools much. Nevertheless, the DevOps practices still apply fully.
However, the data-centric environment is quite different from a typical application environment. This makes sandboxing for distributed network environment all the more essential for data-centric applications.
Playing in the Sandbox
Sandboxes (also called uber containers) are basically self-contained infrastructure environments that can be not only configured to look identical to your final target deployment but also created and run from anywhere.
Developers, for example, can create a sandbox that looks exactly like their production environment—hardware, network, OS versions, software and even cloud APIs. Sandboxes can be spun up so developers can do their development and then torn down when they are done.
Similarly, testers can create a sandbox that looks like their internal IT environment to run a bunch of tests and then re-configure it on the fly to look like their external cloud environment so they can run some more tests. This will enable them to test all possible environments that the application could run in, without actually disrupting the production infrastructure.
So, what, technically, is a sandbox?
A physical sandbox is a protected space where you have complete control and others are allowed only by your invitation. You can build whatever you want with the sand and even bring your own toys into it. If you don’t like it, you can always start over.
Similarly, a technological sandbox provides a controlled environment where you can build and test what you want without disrupting what’s outside the environment. You can even install your own software (bring your own toys) to create the desired environment for your application.
In fact, a number of vendors are now offering sandbox solutions (also called “environment as a service”) that provide a simple interface for creating any target infrastructure environment and configuring it with any control you want. You can bring applications, tests, tools and automated processes to your sandbox. User access to your sandbox is highly controlled so no one can mess up the infrastructure that you are currently using in your sandbox.
You can even reserve and schedule sandboxes so that whole teams of developers and testers can share the infrastructure for hours, days and even weeks at a time. These sandboxes can be triggered even from the outside, right from your DevOps tools.
If you’re developing a data-centric application such as an internet of things (IoT) application, it needs to be deployable on distributed network infrastructure. Sandboxes provide a great way to mimic these complex, large-scale environments and allow developers to build applications that can run in this type of environment.
Ideally, it is possible to combine containers, DevOps tools and sandboxes to enable continuous deployment in a distributed network environment. The key is to package your application in containers, use DevOps tools to automate the process of moving through the various stages of your development cycle and create sandboxes to mimic the target product environment required to run those applications.
About the Author / Sreeram Sreenivasan
For more than eight years, Sreeram Sreenivasan has worked with various Fortune 500 companies in areas of business intelligence and sales and marketing strategy. He’s the Founder of Ubiq BI, a cloud-based BI platform for SMBs and enterprises. He’s also the editor of Fedingo blog, which covers a wide range of business growth topics.