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.
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.
In the past, deploying and operating Kubernetes in the cloud was not for the faint of heart. However, it’s become significantly easier to install and operate clusters and now all major cloud providers have launched Kubernetes as a Service including Google, Microsoft Azure, AWS, IBM, Oracle and many others.
Kubernetes is a game changer for the cloud; in the past there’s been no standard virtual machine image format, and applications built for one cloud provider could not easily be deployed to other clouds. At the same time, a containerized application built for Kubernetes can be deployed to any Kubernetes service, regardless of its underlying infrastructure – whether on-prem, cloud or federated.
Many popular Big Data and Data Science projects like Spark, Kafka, Zeppelin, Jupyter and AI frameworks like Tenserflow are all now benefitting from, or being built on, core Kubernetes building blocks. Essential features like scheduling and consistency, service discovery and infrastructure management were designed as a core part of the platform from day one.
Storage is of paramount importance to Big Data. At the end of the day, people can change compute frameworks and engines with relative ease, but data has the most gravitational drag. The open source data governance work done by vendors, the increased popularity, and cheap, redundant storage options of cloud-based object stores, make cloud storage the de facto data lake for enterprises.
Conclusion
Kubernetes is an exciting project that allows users to run scalable, highly available containerized workloads on a highly abstracted platform. The technology is embraced by all major Cloud and Platform providers and many Big Data, Data Science and AI projects are built on core Kubernetes building blocks.
