How Kubernetes is revolutionizing data management
Kubernetes, also known as k8's, is an open-source container management tool. It automates the deployment and management of containers. These containers are more lightweight than the virtual machines often used when deploying applications. That's because all the apps within a container can share an operating system.
Kubernetes is within a class of tools called container schedulers. They sit on top of containers and can automate many parts of the process that humans used to do manually.
When using Kubernetes, people can also deploy containers to clusters, which are networks of virtual machines. Here are four ways that Kubernetes could upend and improve data management.
1. It Avoids Data-Related Slowdowns
A container holds pieces of application code. Those get combined into pods. The pods include other containers that use common data file systems. This setup promotes efficiency when sharing data between applications. Kubernetes is a smart choice for data management because it takes care of load balancing and other management needs that could cause slowness if not addressed.
Kubernetes can automatically scale the number and type of containers required for particular tasks, as well as determine the available capacity. Since it balances the load between all applications, their containers and the respective users, it enables reliable performance even when workloads become more demanding.
Users can set a minimum number of pods for Kubernetes to use, then allow the tool to increase the resources as activity levels peak. This works well in cases where companies may have particularly data-intensive workloads to handle on certain days of the week. Kubernetes allows implementing diverse and fluctuating workloads, and it could keep them running efficiently.
2. It Caters to Running Data Workloads in the Cloud
Analysts point out that big data is changing. It's not becoming different concerning the amount of data that companies analyze. However, the technologies and infrastructures associated with big data have evolved. Today, big data applications often run in the cloud. Big data has become flexible data. Some people even view Kubernetes as the likey operating system for the cloud era.
Because Kubernetes allows serverless application architectures, it could change how back-end users interact with databases. Moreover, there may come a time where people port Hadoop or other tools associated with big data to Kubernetes.
Those paying attention to developments in this area caution that the time has not yet come where people can or should view Kubernetes as an operating system that supports their cloud-based workloads. Indeed, Kubernetes was never intended to work as an operating system, and it has some fundamental differences compared to what people normally think of when operating systems come to mind.
But, big data is evolving. Kubernetes may help fill in a gap by bringing the flexibility that big data now requires.
3. It Applies to Data Management Security
Security is an aspect that must always be at the forefront of data management. Fortunately, Kubernetes users can make some tweaks to define security policies. For example, a Pod Security Policy (PSP) applies to a pod and its respective containers. An admissions controller enforces the rules set forth in the PSP. Thus, if a request to the API server gets authenticated and authorized, changes don't happen unless they meet the parameters of the PSP.
Additionally, there are ways to specify what privileges people have when using Kubernetes within a company setting. For example, controls can apply to dictate what they can or cannot do when viewing or modifying a cluster state.
These possibilities make Kubernetes even more attractive. They mean that people do not have to overlook security if they consider letting Kubernetes meet some of their data management needs. Security is a built-in component.
4. It Offers More Flexibility for Machine Learning Applications
Similarly to how some people anticipate Kubernetes paving the way for greater flexibility with big data, the tool can streamline the process for deploying machine learning in the cloud. The cloud environment is already an appealing place to build or train machine learning models because of how it supports scaling up as needed. And, some of the problems Kubernetes solves with other workloads applies to machine learning, too.
For example, besides providing scalability, Kubernetes allows for repeatability and the resource allocation that's well suited to machine learning. It's also possible to isolate the cluster resources used by each team member responsible for training a machine learning algorithm. That kind of distributed training tends to make the overall cycle required to teach the algorithm much shorter and cost-effective.
Then, after training, the machine learning algorithm can benefit from the Kubernetes capabilities mentioned here earlier, such as load balancing.
Plus, people who want to start using Kubernetes when working with machine learning algorithms may not need to make as many changes as they think. That's because some of the tools typically used for machine learning development have Kubernetes support. There are also new options that work with Kubernetes now.
Kubernetes did not begin as a tool that people may think about using for data management. That's changing, though, due to the points clarified here and others. As such, IT decision-makers should keep it in mind whether they use Kubernetes now or may start depending on it soon.