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Unlocking the Power of Containerization: Why Data Scientists Need to Learn Dockers and Kubernetes?

Updated: Jun 22, 2023

As the field of data science continues to evolve, it’s becoming increasingly important for practitioners to stay up-to-date with the latest tools and technologies. Two of the most important of these are Docker and Kubernetes, which have transformed the way software applications are developed and deployed. But what exactly are these tools, and how can they benefit data scientists?

In this article, we’ll provide a comprehensive introduction to both Docker and Kubernetes, including an overview of their key features and benefits. We’ll also explore the differences between these two technologies, and examine why data scientists should consider learning both of them. By the end of this article, you’ll have a solid understanding of how containerization and orchestration can help you work more efficiently and effectively as a data scientist.

Unlocking the Power of Containerization: Why Data Scientists Need to Learn Dockers and Kubernetes?

Table of Contents:

  1. Comprehensive Introduction to Docker 1.1. What is Docker? 1.2. What is a Container? 1.3. Docker tools and terms 1.4. Advantage of Dockers

  2. Comprehensive Introduction to Kubernetes 2.1. What is Kubernetes? 2.2. Container orchestration with Kubernetes 2.3. What does Kubernetes do? 2.4. Advantages of Kubernetes

  3. Difference between Docker & Kubernetes

  4. Why you should learn Kubernetes & Docker as a data scientist? 4.1. Why you should learn Docker as a data scientist? 4.2. Should data scientists learn Docker in the presence of a DevOps team 4.3. Benefits of learning Docker as a data scientist 4.4. Should data scientists learn Kubernetes?

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1. Comprehensive Introduction to Docker

Unlocking the Power of Containerization: Why Data Scientists Need to Learn Dockers and Kubernetes?

1.1. What is Docker?

Docker is a commercial containerization platform and runtime that helps developers build, deploy, and run containers. It uses a client-server architecture with simple commands and automation through a single API.

With Docker, developers can create containerized applications by writing a Dockerfile, which is essentially a recipe for building a container image. Docker then provides a set of tools to build and manage these container images, making it easier for developers to package and deploy their applications in a consistent and reproducible way.

These container images can be run on any platform that supports containers, such as Kubernetes, Docker Swarm, Mesos, or HashiCorp Nomad. Docker’s platform makes it easier for developers to create and manage these container images, simplifying the process of building and deploying applications across different environments.

Although Docker offers an efficient means to package and distribute containerized applications, managing and running containers at scale can present significant challenges. Coordinating and scheduling containers across multiple servers or clusters, deploying applications without causing downtime, and monitoring container health are just a few of the complexities that must be addressed.

To tackle these challenges and more, orchestration solutions like Kubernetes, Docker Swarm, Mesos, HashiCorp Nomad, and others have emerged. These platforms provide organizations with the ability to manage a large number of containers and users, optimize resource allocation, provide authentication and security, support multi-platform deployment, and more. By leveraging container orchestration solutions, organizations can effectively manage their containerized applications and infrastructure with greater ease and efficiency.

Today Docker containerization also works with Microsoft Windows and Apple MacOS. Developers can run Docker containers on any operating system, and most leading cloud providers, including Amazon Web Services (AWS), Microsoft Azure, and IBM Cloud offer specific services to help developers build, deploy and run applications containerized with Docker.

1.2. What is a Container?

visualization - Unlocking the Power of Containerization: Why Data Scientists Need to Learn Dockers and Kubernetes?

A container is a lightweight and portable executable software package that includes everything an application needs to run, including code, libraries, system tools, and settings. Containers are created from images that define the contents and configuration of the container, and they are isolated from the host operating system and other containers on the same system. This isolation is made possible by the use of virtualization and process isolation technologies, which enable containers to share the resources of a single instance of the host operating system while providing a secure and predictable environment for running applications.

Containers are commonly used in software development and deployment because they can simplify application management, improve scalability, and reduce infrastructure costs. The Linux kernel provides process isolation and virtualization features, which make containers feasible. These features include control groups (Cgroups) that allocate resources among processes, and namespaces that restrict a process’s access to other system resources or areas. By leveraging these capabilities, various application components can share a host operating system instance’s resources, similar to how a hypervisor allows multiple virtual machines (VMs) to share the CPU, memory, and other resources of a single hardware server. As a result, container technology offers all the functionality and benefits of VMs — including application isolation, cost-effective scalability, and disposability — plus important additional advantages:

  • Lighter weight: Containers have a significant difference compared to virtual machines (VMs) in that they contain only the necessary operating system processes and dependencies to execute the application code, without the entire operating system instance and hypervisor. This results in containers being much smaller in size, usually measured in megabytes, whereas VMs can require gigabytes of storage. Furthermore, containers are more efficient in utilizing hardware resources and can start up much more quickly than VMs.

  • Improved developer productivity: Containers allow applications to be developed once and deployed seamlessly across diverse environments, resulting in exceptional portability and adaptability. When compared to virtual machines (VMs), containers are simpler and faster to deploy, provision, and restart. Consequently, they are an outstanding option for implementing continuous integration and continuous delivery (CI/CD) pipelines, which can help teams swiftly develop, test, and release applications. Containers are an excellent fit for development teams that practice Agile and DevOps methodologies because they promote rapid software development, testing, and deployment, with minimum interruptions and optimal productivity.

  • Greater resource efficiency: With containers, developers can run several times as many copies of an application on the same hardware as they can using VMs. This can reduce cloud spending.

1.3. Docker tools and terms

  • DockerFile: Every Docker container starts with a simple text file containing instructions for how to build the Docker container image. DockerFile automates the process of Docker image creation. It’s essentially a list of command-line interface (CLI) instructions that Docker Engine will run in order to assemble the image. The list of Docker commands is huge but standardized: Docker operations work the same regardless of contents, infrastructure, or other environment variables.

  • Docker Images: Docker images contain executable application source code as well as all the tools, libraries, and dependencies that the application code needs to run as a container. When you run the Docker image, it becomes one instance (or multiple instances) of the container. It’s possible to build a Docker image from scratch, but most developers pull them down from common repositories. Multiple Docker images can be created from a single base image, and they’ll share the commonalities of their stack.

  • Docker Containers: Docker containers are the live, running instances of Docker images. While Docker images are read-only files, containers are life, ephemeral, executable content. Users can interact with them, and administrators can adjust their settings and conditions using Docker commands.

  • Docker Hub: Docker Hub is a public repository of Docker images, boasting of being the “largest community and library of container images” in the world. It contains an excess of 100,000 container images sourced from multiple resources, including commercial software vendors, open-source projects, and individual developers. Docker Hub consists of images generated by Docker, Inc., verified images from the Docker Trusted Registry, and numerous other images.

  • Docker Desktop: Docker Desktop is a software application compatible with both Mac and Windows systems, comprising several tools such as Docker Engine, Docker CLI client, Docker Compose, Kubernetes, and more. Docker Desktop provides access to Docker Hub as well.

  • Docker Daemon: The Docker daemon is a utility that generates and handles Docker images by processing client-side commands. In essence, the Docker daemon functions as the operational hub of your Docker deployment. The system running the Docker daemon is referred to as the Docker host.

  • Docker Registry: A Docker registry is a highly scalable storage and distribution solution for Docker images that operate on an open-source model. Through the registry, you can track image versions stored in repositories and utilize tagging to distinguish them, with the help of git, a tool for version control.

1.4. Advantage of Dockers

Docker lets developers access these native containerization capabilities using simple commands, and automate them through a work-saving application programming interface (API). Docker offers:

  • Improved and seamless container portability: Docker containers run without modification across any desktop, data center, or cloud environment.

  • Even lighter weight and more granular updates: Multiple processes can be combined within a single container. This makes it possible to build an application that can continue running while one of its parts is taken down for an update or repair.

  • Automated container creation: Docker can automatically build a container based on application source code.

  • Container versioning: Docker can track versions of a container image, roll back to previous versions, and trace who built a version and how. It can even upload only the deltas between an existing version and a new one.

  • Container reuse: Existing containers can be used as base images — essentially like templates for building new containers.

  • Shared container libraries: Developers can access an open-source registry containing thousands of user-contributed containers.

2. Comprehensive Introduction to Kubernetes

Unlocking the Power of Containerization: Why Data Scientists Need to Learn Dockers and Kubernetes?

2.1. What is Kubernetes?

Kubernetes, also known as K8s, is a renowned open-source platform designed to orchestrate container runtime systems across a cluster of networked resources. It can function independently or in conjunction with other containerization tools, such as Docker. Initially developed by Google, Kubernetes was created to address the challenge of running billions of containers at scale. Google made the platform available as an open-source tool in 2014, and it has since emerged as the market leader and industry-standard solution for orchestrating containers and deploying distributed applications.

According to Google, Kubernetes is designed to simplify the deployment and management of complex distributed systems while leveraging the benefits of containerization to optimize resource utilization. With its widespread adoption, Kubernetes has become an essential tool for managing and scaling containerized applications in production environments.

Kubernetes offers a practical solution for managing a group of containers on a single machine to reduce network overhead and optimize resource utilization. For example, a container set can consist of an application server, Redis cache, and SQL database. In contrast, Docker containers are designed to run a single process per container. Kubernetes is particularly valuable for DevOps teams because it provides a range of features, including service discovery, load balancing within the cluster, automated rollouts and rollbacks, self-healing for failed containers, and configuration management. Additionally, Kubernetes is an essential tool for building robust DevOps continuous integration/continuous delivery (CI/CD) pipelines. However, Kubernetes is not a complete platform-as-a-service (PaaS), and there are several considerations to keep in mind when building and managing Kubernetes clusters. The complexity of managing Kubernetes is a significant factor in why many customers prefer using managed Kubernetes services offered by cloud vendors.

2.2. Container orchestration with Kubernetes

With the proliferation of containers, organizations may end up with hundreds or even thousands of them, which makes it essential for operations teams to automate container deployment, networking, scalability, and availability. This led to the emergence of the container orchestration market. Although other container orchestration options, such as Docker Swarm and Apache Mesos, gained some popularity initially, Kubernetes quickly became the most widely adopted. In fact, it was the fastest-growing project in the history of open-source software at one point. Developers chose Kubernetes for its extensive functionality, a vast and constantly growing ecosystem of open-source supporting tools, and its ability to support and work across various cloud service providers. All major public cloud providers, including Amazon Web Services (AWS), Google Cloud, IBM Cloud, and Microsoft Azure, offer fully managed Kubernetes services, which underscores its industry-wide popularity.

2.3. What does Kubernetes do?

Kubernetes schedules and automates container-related tasks throughout the application lifecycle, including:

  • Deployment: Deploy a specified number of containers to a specified host and keep them running in a desired state.

  • Rollouts: A rollout is a change to a deployment. Kubernetes lets you initiate, pause, resume, or roll back rollouts.

  • Service discovery: Kubernetes can automatically expose a container to the internet or to other containers using a DNS name or IP address.

  • Storage provisioning: Set Kubernetes to mount persistent local or cloud storage for your containers as needed.

  • Load balancing: Based on CPU utilization or custom metrics, Kubernetes load balancing can distribute the workload across the network to maintain performance and stability.

  • Autoscaling: When traffic spikes, Kubernetes autoscaling can spin up new clusters as needed to handle the additional workload.

  • Self-healing for high availability: When a container fails, Kubernetes can restart or replace it automatically to prevent downtime. It can also take down containers that don’t meet your health-check requirements.

2.4. Advantages of Kubernetes

  • Automated operations: Kubernetes is equipped with a potent API and command-line utility, known as kubectl, that efficiently manages container operations by enabling automation. With the controller pattern, Kubernetes guarantees that applications/containers execute precisely according to their specifications.

  • Infrastructure abstraction: Kubernetes takes charge of the resources allocated to it on your behalf, allowing developers to concentrate on crafting application code rather than the underlying infrastructure of computing, networking, or storage.

  • Service health monitoring: Kubernetes constantly observes the operating environment and matches it against the intended configuration. It automatically performs health assessments on services and initiates container restarts in case of failure or stoppage. Kubernetes only exposes services when they are up and operational.

3. Differences between Docker & Kubernetes

docker vs. kubernetes k8s - Unlocking the Power of Containerization: Why Data Scientists Need to Learn Dockers and Kubernetes?

Docker and Kubernetes are both critical components in the containerization ecosystem, serving different purposes. Docker is primarily used for creating and executing containers, while Kubernetes is utilized for orchestrating and automating the deployment, scaling, and management of containers across host clusters.

Docker offers a straightforward and effective approach to containerization, while Kubernetes provides advanced functionalities like automatic scaling, self-healing, and container deployment.

4. Why you should learn Kubernetes & Docker as a data scientist?

k8s kubernetes vs docker - Unlocking the Power of Containerization: Why Data Scientists Need to Learn Dockers and Kubernetes?

4.1. Why you should learn Docker as a data scientist?

Imagine you have developed a machine learning solution that works perfectly on your local machine. However, when you attempt to deploy it on a different server with a different operating system or library version, the code doesn’t work as expected. This can be a frustrating experience for developers, but it is a common occurrence.

The solution to this problem is to use Docker, which allows you to define a consistent and precise environment for your project. With Docker, you can package your code along with all the necessary dependencies and libraries into a container that can run on any machine, regardless of the underlying environment. This ensures that your code will run smoothly and consistently, regardless of the target deployment environment.

4.2. Should data scientists learn Docker in the presence of a DevOps team

If you’re a data scientist, you may be questioning the need to learn Docker, especially when there are dedicated DevOps teams to handle the infrastructure aspects of your projects. However, it’s crucial to recognize that Docker plays a significant role in the data science workflow. Even with a DevOps team, you’ll find that Docker can simplify and streamline many aspects of your work. Therefore, it’s essential to understand why Docker is such a valuable tool for data scientists.

4.3. Benefits of learning Docker as a data scientist

Learning Docker as a data scientist offers several benefits:

  1. Consistency and Reproducibility: Docker allows you to package and ship your entire project, including the code, dependencies, and environment, in a single container. This ensures that your work runs consistently across different machines and environments, eliminating issues with compatibility and versioning. Additionally, Docker containers allow you to reproduce your work exactly as it was at any point in time, making it easier to revisit past results or share your work with others.

  2. Easy Deployment: With Docker, you can easily deploy your work in different environments, whether it’s on your local machine or in the cloud. By encapsulating your project in a container, you can deploy it with minimal configuration and without worrying about dependencies or compatibility issues.

  3. Collaboration: Docker enables easier collaboration between team members working on the same project. By packaging your work in a container, you can ensure that everyone is working in the same environment, making it easier to share code and reproduce results.

  4. Speed: Docker containers are lightweight and start up quickly, which means that you can run and test your code much faster than traditional methods. This can lead to a more efficient workflow and faster iteration times.

4.4. Should data scientists learn Kubernetes?

While there may be differing opinions on whether data scientists should learn Kubernetes, it is widely acknowledged that having a basic understanding of containerization and orchestration technologies like Kubernetes is beneficial. One reason for this is that it can help data scientists better communicate with DevOps and infrastructure teams. By having a basic understanding of Kubernetes, data scientists can understand the infrastructure requirements for deploying their models and applications in production. This can help facilitate smoother communication and collaboration between data scientists and other teams, leading to more efficient workflows and faster deployment times. Additionally, Kubernetes can provide data scientists with a way to easily deploy and manage their models and applications in a scalable and reliable manner. By using Kubernetes, data scientists can automate the deployment and scaling of their models, making it easier to handle large volumes of data and requests.

Furthermore, as machine learning models and applications become more complex, they often require more infrastructure resources to run efficiently. Kubernetes can help data scientists manage these resources effectively, making it possible to run complex models and applications at scale without having to worry about infrastructure constraints.

Overall, while it may not be necessary for data scientists to become experts in Kubernetes, having a basic understanding of the technology can help them work more effectively with other teams and deploy their models and applications more efficiently. You can read more about this topic in these blog posts:

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