How Kubernetes Can Help Big Data Applications

How Kubernetes Can Help Big Data Applications

Kubernetes in Big Data

Every organization would love to operate in an environment that is simple and free of clutter, as opposed to one that is all lined up with confusion and chaos. However, things in life are never a piece of cake. What you think and want rarely lives up to your choices, and this is also applicable to large companies that churn a massive amount of data every single day.

This is the point. Data governs the era we all live in. It is these data piles that prove to be a burden to a peaceful working process in companies. Every new day, an incredible amount of streaming and transactional data gets into enterprises. No matter how cumbersome it all may be, this data needs to be collected, interpreted, shared and worked on.

Technologies which are assisted by cloud computing offer an unmatchable scale, and also proclaim to be the providers of increased speed. Both of them are very crucial today especially when things are becoming more data sensitive every single day. These cloud-based technologies have brought us to a critical point that can have a long term effect on the ways which we use to take care of enterprise data.

Kubernetes in Big Data

Why Kubernetes?

Known for an excellent orchestration framework, Kubernetes has in recent times become the best platform for container orchestration to help the teams of data engineering. Kubernetes has been widely adopted during the last year or so when it comes to the processing of big data. Enterprises are already utilizing Kubernetes for different kinds of workloads. 

Contemporary applications and micro-services are the two places where Kubernetes has indeed made its presence felt strongly. Moreover, if the present trends are anything to go by, micro-services which are containerized and run on Kubernetes have the future in their hands.

Data workloads which work on the reliance of Kubernetes have a lot of advantages when compared to the machine based data workloads-

  • Superior utilization of cluster resources
  • Better portability between on-premises and cloud
  • Instant upgrades that are selective and simple
  • Quicker cycles of development and deployment
  • A single, unified interface for all kinds of workloads

 

How Big Data entered the Enterprise Data Centers

To have an idea about the statement above, we need to revisit the days of Hadoop.

When Hadoop was first introduced to the world, one thing soon became evident. It was not capable enough to manage the emerging data sources effectively and the needs of real-time analytics. The primary motive for building Hadoop was to enable batch-processing. This shortcoming of Hadoop was taken care of with the introduction of analytics networks like Spark.

The ever-increasing ecosystem did take care of a lot of significant data needs but also played an essential role in creating chaos in the outcome. A lot of applications that worked with analytics tended to be very volatile and did not follow the rules of traditional uses. Consequently, data analytics applications were kept separately from other enterprise applications.

However, this is the time we can surely say that things headed in the right direction where cloud-native technologies that are open sourced like Kubernetes, prove to be a robust platform to manage both the applications as well as data. Also, explanations are under development which helps to allow the workloads of analytics to run on IT infrastructures which are containerized or virtualized.

During the days of Hadoop, it was data locality which acted as a formula that worked. The data was made available for distribution and then close for computation. In today’s scenario, storage is getting decoupled by computer. From the distribution of data to the delivery of access, the merging of these data analytics workloads and on-demand clusters based on Kubernetes is also on us.

Shared storage repositories are vital for managing workload isolation, providing speed, and enabling the prohibition of data duplication. This helps the teams leading analytics in setting up elaborate customized clusters which meet their requirements without recreating or moving larger sets of data.

Also, data managers and developers can raise queries to structured and unstructured data sources without the assistance of costly and chaotic data movement. The time taken for development gets accelerated, helping the products to enter into markets quickly. This efficiency which brought through a distributed access in a shared repository for storage will result in lesser costs and thorough utilization.

 

Unlocking Innovations through Data

With the use of a shared data context for isolation of multi-tenant workloads, the data is unlocked and easy to access by anybody who wishes to utilize it. The data engineers can also variably provide these clusters with the right set of resources and data. Teams on data platforms can strive for achieving consistency among multiple groups of analytics, while groups for IT infrastructure can be provided access to the clusters to use in the overall foundations which so far is being used for different traditional kinds of workloads as well.

Applications and data are ultimately getting merged to become one again, leading to the creation of a comprehensive and standardized source to manage both on the same infrastructural level. While this entire process might have used up a few years, today we have finally succeeded in ushering an era where companies can successfully deploy a single infrastructure for the management of big data and many other needed and related resources.

This is possible only because of open-source technologies, which are also based on a cloud system. There is no doubt that such techniques will continue to pave the way ahead, acting as a stepping stone for the evolution of more advanced and concise technologies in the future to come.