Containerization is an emerging technology that promises to solve the problem of software discrepancies when moved from one computing environment to another. This blog post will take a look at the business challenges that are solved by way of Containerization, how it differs from the application of Virtual Machines, and how we at Knowledge Lens have developed an in-house Containerization solution.
When a software is migrated from a test environment to a staging environment, into production, or even into the cloud, it may not operate as planned. Due to varying software environments, security topology, or network limits, the software may fail to run, or be executed in a proper manner.
The Role of Containers:
Containerization is an OS-level virtualization method used to launch and run distributed applications. Using this technology, several separate applications or services can run on a single host and access the same OS kernel. Containers work on bare-metal systems and cloud interfaces, across Linux and a few Windows and Mac OSes.
As Containers comprise of the whole runtime environment- the application, its code libraries, and configurations, they help in cancelling out differences in OS and other infrastructural elements.
But how is Containerization different from its older counterpart, Virtualization?
A virtual machine or VM, fully emulates a computer system. It allows you to run separate computers on hardware that is actually one computer. The operating systems and their respective applications share hardware resources from a single host server, or a combination of host servers. However, each VM requires its own OS, to enable the virtualization of the hardware.
VMs have significantly helped in lowering costs and increasing efficiencies, but they are not without their flaws. They consume a lot of system resources because they run full copies of all the hardware that the OS needs to run.
This table illustrates how containers differ from, and outweigh VMs on multiple counts:
|Limited performance||Native performance|
|Hardware-level virtualization||OS virtualization|
|Requires lot of memory space||Requires minimal memory space|
|Slow provisioning||Real-time provisioning|
Containers- The Sweet Spot
Unlike VMs, containers virtualize just the OS, and not the entire underlying computer. By sharing the OS resources such as libraries and code files, containers bypass the necessity of reproducing (or virtualizing) the OS code. This allows a server to run multiple workloads on a single OS installation.
In effect, you can put three times as many applications on a single server with containers than you can with a VM. Thus, you can emulate a handy, consistent operating environment for development, testing, and launch.
Also, VMs take considerably longer than Containers to boot up their OS and run the applications they host. What’s more, with containerization, an application can be split into modules through what is known as the ‘microservices’ approach. This greatly simplifies the overall management of applications, and also allows for modifications to be made without an entire rebuilding of the said app.
Containers are a great choice when you wish to optimize maximum number of applications on minimal number of servers. Being just a few megabytes in size, they are almost instantly available, and easy to set up.
While there are plenty of commercial container solutions in the industry, Docker Enterprise being one of the biggest players, we at Knowledge Lens have developed a competitive Containerization solution. With the onset of the cloud era, and a renewed focus on automation and acceleration, our containerization solution has proven to function at high speed, accuracy and scalability.
Let us take a closer look at Knowledge Lens’ containerization solution. We used a combination of AI and Containerization technologies to implement a Big Data Lake and Advanced Analytics Platform for one of our esteemed clients:
Our client is a ride sharing application company that required a Big Data Lake and Advanced Analytics Platform to gain business insights, and optimize their cost & efficiency.
- Using advanced AI and Containerization technologies, we implemented a Big Data Lake and a centralized platform for analytics. They were enabled to make a smooth transition from their traditional data processing systems and transform their business processes.
- The Big Data Lake allowed them to process large volumes of data as well as the new data being added onto the system.
- With the AI-powered analytics, they could improve their customer engagement, and overall efficiency.
At Knowledge Lens, we constantly work towards improving our product technologies, so your business can do more for you. Visit us here to learn how you can grow your business operations through data- driven decision making, starting today.