Big data developers are expanding the range of technologies that they rely on to create new applications. Kubernetes is one of the new solutions that data programmers are incorporating into their projects.
George Anadiotis of ZDNet discussed the growing number of data developers transitioning to Kubernetes. There are a number of advantages to using this docker orchestrator. One of the biggest benefits is that Kubernetes can be used to streamline development of big data applications of varying complexity.
Other experts have discussed the benefits as well. These include:
- Engineering for better site reliability
- Enhancing the DevOps lifecycle for data sets
- Curtailing the need for data silos
- Developing serverless projects to leverage existing data without the need to create larger storage capacities
Kubernetes is going to play a big role in shifting the focus away from scaling big data to maximizing data quality. Big data developers are learning more about it, so they can leverage its effectiveness. They should learn about some of the basics, so they incorporate it into their existing Hadoop infrastructure. There is still a shortage of data engineers with a competency in Kubernetes, so they may need to turn overseas. There are a number of top outsourcing countries that can assist with this.
Kubernetes Framework and Fundamentals for Big Data Engineers
Kubernetes, better known as k8s, is a Docker Orchestrator. This means that Kubernetes can manage the life of containers and perform different tasks with respect to those containers. It is an OpenSource project created by Google. Google uses Kubernetes for almost all its products such as: Gmail, Maps and Drive. There are other Docker Orchestrators, such as Swarm, but Kubernetes is much more mature than its alternatives.
Both one of the advantages and disadvantages of Kubernetes is that it is a very lively project. Each version comes with a list of entirely new features.
Another great advantage of Kubernetes is that it can manage the entire infrastructure from its APIs.
One of the first things developers need to understand is the nomenclature. Some of the most important terms and their respective definitions are listed below:
- Cluster: A set of physical or virtual machines that are used by Kubernetes
- Pod: The pod is the smallest component of Kubernetes. It is essentially a Docker jargon container.
- Labels and selectors: These are pairs of keys and values, which can be applied to pods, services and replication controllers. They will be able to identify them to be able to manage these other units.
- Node: A node is either the server virtual or physical hardware that hosts the Kubernetes system and where we will deploy our pods (containers). If you look for information on the Internet, formerly called Minions.
- Replication Controller: This is responsible for managing the life of the pods and the person in charge of keeping the pods that have been indicated in the configuration up and running. It allows the systems to be scaled in a very simple way and manages the recreation of a pod when some kind of failure occurs.
- Replica Sets: This is the new generation of the Replication Controller, with new functionalities. One of the outstanding functionalities is that it allows us to deploy pods depending on the labels and selectors.
- Deployments: Deployments are where the number of replica pods for the system are specified. A deployment is a more advanced functionality than the Replication Controller and very similar to the Replication Sets, but with other features.
- Namespaces: they are groupings that differentiate workspaces for different situations. For example, a developer could make a Namespace for production and another for development and each Namespace would have its own pods, replication controllers, etc….
- Volumes: It is the access to a storage system.
- Secrets: Is where confidential information is stored as users and passwords, in order to access resources.
- Service: It is the policy of access to the pods. We could define it as the abstraction that defines a set of pods and the logic to access them.
Before starting to work with Kubernetes, it is important to have clear concepts. If you haven’t been very clear about the concepts, the internet is full of documentation on the subject, but I wanted to do my bit.
