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Kubernetes Vs. Docker: Primary Differences You Should Know

Kubernetes vs Docker is an essential topic of debate among professionals. Both of them are related to containerization, and both of them have their sets of features. So, the community is divided into two sections, which can lead to confusion.

That’s why you should read this article as we’ve discussed all the significant differences between these two solutions. Let’s get started.

What is Kubernetes?

Kubernetes is an open-source container management solution based on the Google platform. It allows you to handle containerized applications in virtual, physical, as well as cloud environments. Kubernetes is a flexible tool, and it can deliver multiple complex applications. 

In Kubernetes, you can run the containers across various compute nodes. After Kubernetes has control over a cluster, it can spin up or tear down containers according to your requirements. 

Kubernetes has nodes and pods. Nodes are VMs and bare-metal servers in Kubernetes; pods, on the other hand, are the fundamental unit of deployment. Nodes in Kubernetes are of two types: Master Node and Worker Nodes.  Learn more about Kubernetes and it’s networking.

Features

Kubernetes has plenty of features. Here are the prominent ones:

  • Its infrastructure is auto-scalable
  • It provides you with automated rollbacks and rollouts
  • It has self-healing capabilities
  • It enables you to deploy software at scale
  • It offers a consistent environment for development and testing
  • It has load balancing and horizontal scaling
  • You can use Kubernetes to create a predictable infrastructure
  • It gives you automated scheduling
  • It has application-focused management
  • You get declarative configuration

As you can see, there are plenty of features in Kubernetes. Thanks to those features, you get to avail a lot of benefits when you use this solution. 

Read: Kubernetes Interview Questions

Benefits

  • It has the largest community for container orchestration solutions which is filled with experienced and helpful experts
  • It follows the principles of immutable infrastructure
  • It is a product of Google which ensures you get a high-quality solution
  • You can easily organize the containers through pods
  • You get multiple options for storage including clouds and on-premise storage

What is Docker?

Docker is an open-source containerization solution. It enables you to automate application deployment in lightweight containers. It’s widely popular in the cloud industry. Professionals use Docker for virtualization and to run various Operating Systems on a host. It’s a product of Docker Inc. 

You can look at Docker as a software packaging platform and its containers as lightweight virtual machines. Docker gives you a virtual OS so you can run a VM there. You can also run multiple applications you wouldn’t be able to run otherwise. Every application would be running oblivious to other containers. You can install Docker on Windows, Mac OS, or any other. 

Read: Full Stack Project Ideas for Beginners

Features 

Docker is a popular containerization solution because of multiple reasons. And one of the most prominent reasons for its popularity is its features:

  • It has version control
  • You have easy modeling techniques and options
  • It enhances developer productivity
  • It gives you isolated environments to run and manage your applications
  • It increases the operational efficiency
  • It provides you with agile applications

Just like Kubernetes, Docker has plenty of benefits too. 

Benefits

  • The initial set up is easy and efficient 
  • You can use your new Docker tool with existing ones without any difficulty
  • You can track your container versions and examine any lack of compatibility between them
  • You can describe the lifecycle of your application in detail
  • You can boost your organization’s productivity through its comfortable and agile configuration
  • You can use Docker Compose for simple configuration
  • You get to start a virtual machine and run apps within it quickly through its fast-paced environment
  • Docker makes sure that your applications remain isolated and oblivious to each other

Kubernetes vs Docker- Similarities

Docker and Kubernetes have multiple similarities as they operate in the same sector.

Moreover, their qualities overlap considerably in the following areas:

  • They both are open-source products. This means, they get constant updates and have thriving communities of developers and programmers
  • They both are written in Go (a programming language)
  • You can ship both of them as small, lightweight binaries
  • Both Kubernetes and Docker use YAML files to specify application deployments and stacks
  • They both focus on microservices-based architectures

If you want to learn both of them, it would be better to start with Docker and move onto Kubernetes after you’re familiar with the former. 

Kubernetes vs Docker – Differences

Even though they seem similar, they are very different. The following points will help you in understanding how Kubernetes is different from Docker and vice versa.

  • The first difference is developers as Docker’s developer is Docker Inc., whereas the developer of Kubernetes is Google Inc.
  • The cluster setup in Docker is quite complicated and challenging, but its strength is strong. On the other hand, the set up of clusters in Kubernetes is quite simple and lacks strength. 
  • Installation of Docker is accessible, but that’s not the case with Kubernetes. 
  • Docker is a year older than Kubernetes. It entered the market in 2013, and Kubernetes did in 2014. 
  • Docker has agent updates whereas Kubernetes has cluster updates
  • Docker doesn’t have auto-scaling, but Kubernetes does
  • Scalability of Docker is faster, but the cluster strength, in this case, isn’t much robust. On the other hand, scaling up in Kubernetes is slow but with more robust clusters. You’ll need to perform service configuration manually for load balancing in Kubernetes.
  • Docker is optimized to work on single and large clusters, whereas Kubernetes is optimized for multiple small clusters.
  • Docker has high fault tolerance, and Kubernetes has low fault tolerance.
  • You can use a third-party tool for logging and monitoring in Docker. Kubernetes provides you with a built-in mechanism for this purpose.
  • Docker can support up to 2000 nodes, and Kubernetes can help around 5000 nodes
  • Docker has a container limit of 95,000 and Kubernetes has a limit of 300,000 containers

There are plenty of differences between Kubernetes and Docker. Each one is suitable for different tasks, as you must’ve seen in the points above. 

Conclusion

As you must’ve noticed, there are many similarities and differences between Kubernetes and Docker. We hope you liked this article. If you have any questions about this topic, feel free to let us know. We’d love to hear from you.

If you want to learn and master Kubernetes, DevOps, and more, check out IIIT-B & upGrad’s PG Diploma in Full Stack Software Development Program.

What is the difference between testing and training in machine learning?

Machine learning is a branch of AI that allows computers to learn without having to be explicitly programmed. Machine learning algorithms can learn from their mistakes and improve over time, becoming more accurate and efficient in predicting outcomes. There are a number of distinct types of machine learning algorithms, but supervised and unsupervised learning are the most prevalent. The method is trained with a set of data that includes the intended outputs, allowing it to learn how to predict the outputs for fresh data. Unsupervised learning algorithms are given data to study rather than desired outputs so that they can learn to group and categorize data on their own. Testing is the process of confirming the correctness of a machine learning algorithm's predictions on a set of data that was not used to train the system. The process of teaching a machine-learning algorithm to make predictions by altering its parameters to maximize its performance on a set of training data is referred to as training.

Why should I learn deep learning?

There are numerous reasons to master deep learning. Deep learning is a machine learning subfield that uses neural networks to learn data patterns. Object detection, speech recognition, and natural language processing have all been demonstrated to be quite effective with it. Deep learning algorithms may also be trained on enormous data sets to learn how to execute complex tasks like detecting objects in photos or comprehending human speech. If you want to improve your machine learning skills or work in a sector that requires machine learning algorithms, you should master deep learning. If you already know how to use machine learning, learning deep learning will provide you access to even more powerful algorithms and methodologies.

What are the benefits of using reinforcement learning?

Reinforcement learning has a number of advantages. One of the most significant advantages is that it aids people in learning new material faster and more efficiently. Furthermore, reinforcement learning can aid in information retention for longer periods of time. Furthermore, reinforcement learning can aid in the rapid acquisition of new skills.

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