Kubernetes Vs. Docker: Primary Differences You Should Know
By Kechit Goyal
Updated on Nov 23, 2022 | 7 min read | 5.83K+ views
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By Kechit Goyal
Updated on Nov 23, 2022 | 7 min read | 5.83K+ views
Share:
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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.
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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.
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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.
Kubernetes has plenty of features. Here are the prominent ones:
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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.
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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.
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Docker is a popular containerization solution because of multiple reasons. And one of the most prominent reasons for its popularity is its features:
Just like Kubernetes, Docker has plenty of benefits too.
Docker and Kubernetes have multiple similarities as they operate in the same sector.
Moreover, their qualities overlap considerably in the following areas:
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.
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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.
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.
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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
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.
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.
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.
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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Kechit Goyal is a Technology Leader at Azent Overseas Education with a background in software development and leadership in fast-paced startups. He holds a B.Tech in Computer Science from the Indian I...
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