The container managing software Kubernetes is one of the best open-source projects and its adoption has continuously grown in recent years. More and more important companies have chosen Kubernetes for their modernization Let’s see who uses this platform in practice and what benefits it provides.
Fast time-to-market with development efficiency
Kubernetes allows you to centrally manage containers, which means that it can speed up and simplify the launch of new products to the market, and build an effective development and testing process.
In addition, it allows developers to use many out-of-the-box solutions and standardize processes, which also speeds up Time-to-Market, which is the time to bring services to market.
DevOps engineers and developers can use a variety of Kubernetes-compatible tools for monitoring and analysis, application management, CI / CD building, and more. It is convenient for the IT department to work, and the company receives all the benefits of new technologies.
Here are some examples:
Airbnb has moved from monolithic architecture to microservices. The goal was to scale the continuous application development and delivery process so that about 1,000 engineers in the company could simultaneously configure and deploy more than 250 critical services. As a result, Airbnb’s IT team can perform on average over 500 deployments per day.
The New York Times – Today, most of the company’s client applications run on Kubernetes. Increasing deployment speed and performance were critical in the selection. Previously, deployments took up to 45 minutes, but now they are completed in just a few minutes. Developers can independently send updates when needed, rather than requesting resources in advance and not waiting for a weekly deployment on a schedule, as was the case in the past.
J.P. Morgan – the largest American bank has ported over 3,000 applications to Kubernetes in NET and Java. This resulted in improved mean time to market, increased productivity by 700%, reduced infrastructure utilization by 300%, and reduced costs by 45%.
Adidas – moved the entire online store to Kubernetes in six months. Site load times have been cut in half. Updates were deployed 3-4 times a day instead of every 4-6 weeks.
Automatic resource allocation to survive the growth of traffic and users without problems
When the load on IT services changes, for example, traffic grows during a sale, the system needs to be scaled – add additional resources to it to cope with the new load.
Kubernetes can automatically scale the IT system depending on the needs of the application: incoming traffic and processed load. This means that the application will almost instantly get the resources it needs during peak periods, and it will not waste resources during less busy times.
The company does not overpay for capacity when it does not need it, that is, it optimizes IT costs, including by improving utilization by 2-3 times, and does not risk losing customers due to the fact that the application hangs during the growth of requests.
Here are some real-world examples:
Tinder. Due to the high volume of traffic, Tinder’s engineering team faced challenges in increasing capacity and stability. Kubernetes became a way out of the situation: 200 services were transferred to it – that’s 48,000 working containers. The migration helped keep the business running smoothly. In an outdated infrastructure, as traffic grew, it took several minutes to wait for new resources to connect. With Kubernetes, this happens in a matter of seconds.
Spotify is an audio platform with millions of users and one of the first to use Kubernetes. And now it benefits from automatic scaling, optimizing IT costs. In addition, Kubernetes allows you to launch new services in seconds and minutes – previously, this process took more than an hour.
Pokemon Go quickly grew to 500 million downloads and over 20 million daily active users. The company’s engineers never thought that their user base would grow exponentially in such a short time, the servers couldn’t keep up with the traffic. Resources were scarce due to real-time activity of millions of users around the world. Kubernetes helped keep applications running smoothly, and developers were able to focus on new game features.
The ability to quickly implement new technologies: cloud services, machine learning, big data, AI
Kubernetes simplifies the implementation and use of many technologies that are now helping companies to further improve the efficiency of service development and service quality. And also to attract the most advanced IT-specialists to the staff of the company, who strive to work with modern systems.
This is what Kubernetes goes well with:
Cloud services, work with cloud applications and hybrid clouds. Kubernetes makes it easier to move applications to the cloud, and you can manage them both on premises and in the cloud. This is especially true if the company uses a hybrid infrastructure, for example, test environments in the cloud, and the main development on its servers.
In addition, now many companies use multicloud, that is, several clouds. This allows you to take advantage of the public cloud while maintaining your infrastructure, or use the most suitable solutions from several providers.
Machine learning and artificial intelligence. To build and train neural networks, large computing power is required. With Kubernetes, you can efficiently use resources and carry out all calculations within the cluster. A data scientist or ML engineer only needs to clear the data and write the code. Kubernetes will do the rest.
For example, Babylon Health, a company that develops medical services based on machine learning and artificial intelligence, lacked its own computing resources. Therefore, custom applications were ported to the Kubernetes platform to use machine learning tooling. Now engineers can instantly perform the necessary operations, and inspection time has also been reduced – previously it took 10 hours, now 20 minutes.
Big data. Kubernetes is designed specifically for high workloads, so it is ideal for Data Science and working with big data, it easily integrates with cloud Big Data solutions. It will be easier for the company’s analysts to build pipelines for working with big data and extract the necessary information from them.