How Docker and Kubernetes’ Open Source Technology is Winning Over Businesses

By Young Mavericks’ Data Engineer Don de Lange

First Things First: Container Technology

Today, there is an endless number of digital technologies, applications and systems, all with different purposes and uses. More and more companies are utilizing container technology to make sense of this labyrinth of application technologies. Containers are, essentially, very efficient and detailed packages with a fixed layout, consisting of the application and any technical components (libraries, utilities and configuration files) that can function independently off of the cloud. Unlike virtual machines, containers isolate the execution environments of mutual applications and share the underlying OS kernel. Container technology is open sourced and embraced by Amazon, Google and Microsoft – the world’s largest tech giants.

Due to their uniform design and the standardized adoption of them by all cloud and tech providers, the containers’ applications can run across any platform and operating system. As a result, developers spend less time fixing bugs and can focus on improving development processes and optimization, which can lead to significant cost savings. Other benefits of container technology include minimal start-up time, simple maintenance, fast (and automatic) upscaling and lateral scaling capabilities, and the option to run multiple versions of the same application simultaneously, thus enabling A/B testing.

Managing Container Technology with Docker and Kubernetes

The many benefits of container technology make it an increasingly exciting form of application management for businesses. One disadvantage, however, lies in its complexity of management: few companies have the in-house expertise to get and stay ahead of the curve. That is why so many have turned to platforms such as Docker and Kubernetes for the expertise and competencies required to implement all systems and applications and then keep them running smoothly.

Kubernetes and Docker are two of the largest and best-known software solutions when it comes to orchestrating containers. Both tools enable companies – each in its own way – to manage a cluster of servers with one or more containers on them. With Kubernetes and Docker, companies are guaranteed that their products and services will be continuously updated and always work as intended.

Container Management with Docker

Docker is the most popular open source container. For many, it creates the central hub for developing, deploying and executing applications within a single, virtual platform. Among other operations, Docker allows companies to cluster components resulting in groups of multiple containers, which minimizes the risk of failure because there is a scaffolding of containers to fall back on. Docker has an extensive range of tools but focuses primarily on the distribution and leaves the applications’ implementation primarily to the consumer. Kubernetes, on the other hand, runs the application for the consumer to a certain extent. Simply put, Docker is like the supermarket around the corner where the shopper can select among various departments, while Kubernetes is like a delivery service that delivers your groceries to your home whenever your cabinets are empty.

Kubernetes as a Superlative

The Docker application does not necessarily depend on Kubernetes; Kubernetes mainly works as a supplement to it. Kubernetes – developed by Google – is, just like Docker, an open source orchestration platform, but goes well beyond that. You can see the platform as an elevated platform, targeted towards complex systems where the workload is often distributed over different environments (clouds). More and more (internet) companies are incorporating Kubernetes to cluster, manage and scale up large quantities of containers with ease and speed.

Advantages of Docker and Kubernetes for Companies

The diverse range of companies choosing Docker’s and Kubernetes’ software solutions is growing every day. Choosing these platforms guarantees companies the quality and distribution of products and helps them implement, replicate, move and back up applications faster and easier. Telephone companies, for example, use Docker’s software to optimize processing incoming calls. Should anything still go wrong, Kubernetes’ tools can ensure that the greater container tool continues to run. Internet companies also appreciate Docker and Kubernetes’ tools, especially when scaling up and down their websites’ capacity. In order to optimally serve their fluctuating website traffic, software from Docker and Kubernetes gives them access to multiple networks. If one network becomes overloaded, it will automatically switch to another network, always ensuring uninterrupted websites.

Applying Docker and Kubernetes Yourself

Kubernetes and Docker are free platforms that anyone can install and operate. You can use these platforms in a relatively superficial way, but you could also take it as far – technically speaking – as your creativity and ambition demands. In order to maximize each of these platforms, it is important to understand their individual function and modus operandi on a conceptual level – meaning you know the details and goals of your script, and also Docker’s and Kubernetes’ different functions and applications.

Each Docker container begins with a Docker file. This is a text file written in an instructional syntax for creating a Docker image. The Docker file indicates the underlying operating system, languages, environment variables, file locations, network ports and other relevant components. Of course, it also states what the container’s steps towards execution. After writing and entering the file, Docker creates a corresponding Docker image, which specifies for the container which software components will be executed and how. Then Docker can start executing and a container is started. Kubernetes takes this one step further and offers more advanced container orchestration technologies. 

Useful Tools for Data Engineers 

Docker and Kubernetes offer a range of useful applications for engineers and companies. Whether it’s security software, deployment tools, supporting apps to simplify coding or configuration tools – the supply is endless. For example, you could deploy a scheduler that runs your code every day at a specific time, or use apps to ensure that two networks are always on, so that if one fails, the other automatically takes over. Other handy tools from Docker and Kubernetes help Data Engineers not only solve code problems ‘locally’, but also remotely, so that companies can rely on their applications anytime, anywhere.

Docker and Kubernetes’ tools are especially valuable for companies that already have their data and data systems in order. To get the most out of Docker and Kubernetes, it is important to already be fairly advanced in terms of data optimization. Once a company has a good foundation, the advantages of Docker and Kubernetes on both a strategic and operational level are boundless: fewer bugs and inconsistencies, the ability to switch on and off at lightning speed, and overall, a shorter time-to-market for significant cost savings.


Discover how Young Maverick can help your company with advice on or implementation of Docker and Kubernetes. Contact us here.

Data Pipeline Implementation: how to do it yourself

These instructions build on what has been discussed in ELT, ETL and Data Pipelines. In that guide, we discussed the problems that arise in storing and using data for a company. In response to those problems, we introduced the concept of Data Pipelines, which helps the company become better aware of the data loading steps and incorporating these steps in the most optimal way to create a Data-driven Culture. We also discussed some specific tooling that can be used to properly deploy Data Pipelines. 

Now that we understand the concepts behind Data Pipelines, we will now apply them to implement a functioning Data Pipeline. Just like most of our data engineering processes, we follow a step-by-step plan and provide an implementation strategy for each step. 

Hopefully a step-by-step plan will give you a solid foundation when you are constructing your own Data Pipeline as well as the implementation methods.  You can find the whole code on our Giftlab.

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Data Scientist Jason: “Young Mavericks taught me a lot of great technical skills”

Jason (27) was one of Young Mavericks’ first generation of Data Science trainees. “As a
Young Mavericks trainee, I could do all sorts of projects at the Chamber of Commerce and
the Consumers Association with other companies. The traineeship taught me so much – I
developed data and programming skills and learned how to arrive at customer-focused
solutions.”

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