Big Data on Kubernetes: The End For Hadoop

The name Big data is familiar to everyone today. Big data is nothing but an extensive data set that traditional software is unable to deal with it. Therefore, many business entities across the globe need to use big data to deal with large projects. Data flows seamlessly with the increase in the business and its scale of operations. In this regard, Hadoop emerged as an open-source ecosystem that helps to process massive amounts of data cost-effectively and efficiently. 

Then the question may arise of why you should use Kubernetes to process big data. It is because Kubernetes offers many benefits to Big Data software. It makes it more accessible for the operations and infrastructure in an organization. Its container architecture gives many options for the persistent storage of data across different jobs. Also, its structure helps to host stateless and temporary apps. Moreover, K8s is enhancing its networking and data security architecture well.

Further, Big Data on Kubernetes (K8s) helps smooth data movement. Therefore, many big data platforms plan to deploy and run workloads on the cloud using Kubernetes. It will give more scalability to these platforms. 

So, in this article, you will learn how Big Data works on K8s and its various aspects. But if you want to explore something more about the Kubernetes containers and their uses in real-time. Then you can opt for Kubernetes Training with expert guidance where they will help you to guide in detail. Also, you can update your skills well.

Before moving to see the use of Kubernetes in Big data, you should know about Hadoop in brief.

What is Hadoop in Big Data?

Hadoop is a framework based on Java that stores large data sets and allows distributed processing on the same. It is an open-source framework that can run on widely available commodity hardware. Moreover, it can scale from a single server to many servers. Apache Hadoop offers very cost-efficient and faster data analytics. For this, it uses distributed processing power across the network. This framework has a better solution for different types of businesses, such as-

  • Data Management
  • Data Operations
  • Information Security
  • Accessing & Integration of Data and many more.

Moreover, Hadoop can detect the application layer failure and handle it efficiently. The various benefits of Apache Hadoop include-

  • Less expensive
  • Automatic data backup
  • Easy accessibility
  • Data processing with good storage capacity. 

Thus, there is much use of Hadoop, but at the same time, it has some limitations also. Such as low data security, unsuitable for small data sets, less native support for real-time analytics, etc.

Big Data on Kubernetes

Today’s business world requires cloud-based solutions and the help of cloud storage providers. They do massive computing operations on the cloud. In this regard, it is suitable to use Kubernetes as a cloud-based container platform to tackle big data. 

Kubernetes is one of the alternatives for Hadoop for big data processing. Moreover, Kubernetes is a container-based orchestration now gaining much popularity among the data analytics teams. Many recent researchers found K8s to be the most helpful tool for big datasets. 

Kubernetes is an open-source container-based platform that helps to build cloud-native apps. Also, it is effectively used to deploy, store, and manage many containtainerized apps. 

Why use Kubernetes in Big Data?

The use of Kubernetes helps in the smooth running of container-based app deployment and management. It also offers excellent flexibility and reliability to the IT operations team. Therefore, using K8s in Big Data is easy for smooth operations. Let us know more about why Kubernetes is suitable for Big data operations.


The first benefit of using Kubernetes in Big Data is its cost-effectiveness. Kubernetes allows business enterprises to utilize its cloud advantages fully. Automation plays a significant role in dealing with the basic tasks, or the cloud provider may take care of them. K8s, on the other hand, also share resources to make the process efficient. Moreover, its containerization feature allows it to run different apps on a single OS. Further, it avoids dependency and resource conflicts. 

This way, K8s provide a cost-effective approach for processing big data sets. 

Easy Development

Developing powerful data software becomes more manageable with the help of K8s and their containers. It saves much time and cost for the DevOps team of an entity by making the processes more repeatable and reliable. Moreover, it allows the development team to use the containerized images easily. Also, it makes the process of updating and deploying apps much smoother. It will enable the DevOps team to test various editions of apps using containers much more safely. S

Therefore, using K8s is a practical approach to building powerful data software. It also saves high growing costs for the business entity. 

Highly Portable

K8s offer portability features. Using this platform DevOps team can quickly deploy apps anywhere. Further, it stops the need for components’ recomposition to make them compatible with different software and hardware support. 

Moreover, some best tools to enable Big Data on the K8s container platform are Kubectl and Docker. Thus, businesses can significantly benefit from K8s by reducing considerable investments in big data processing. Also, the data storage costs will get reduced due to cloud-native apps. Thus, these are the possible benefits of using K8s on Big data. 


There is a thought that K8s are taking over Hadoop, but there is no sign of it. We can’t say that it’s the end of Hadoop, but the flexibility features of K8s are more excellent than Hadoop. Further, K8s allow using of any programming language. Also, its containerized app usage will enable it to move quickly to another cloud storage. 

There is no doubt that Hadoop is a cost-effective and efficient big data analytics tool. But with the changing technology trends, many enterprises rely on K8s for great flexibility and reliability. The DevOps teams can also reduce the most repetitive tasks and their complaints. Further, it makes most tedious tasks much easier where the stack makes all the difference. So, we can see that organizations will move to K8s to deal with big data tasks and smooth operations.

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