DevOps Basics - Docker vs Kubernetes vs Podman

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Difference between Docker, Kubernetes, and Podman?

Hello friends, if you are preparing for Tech interviews, you must prepare for container technologies like Docker and Kubernetes, as containers are now used to deploy most apps, including Microservices and monoliths.

One of the most common questions on System Design and Software developer interviews nowadays is the difference between Docker, Kubernetes, and Podman? What they are and when to use them.

In the past, I have talked about system design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy, and today, I will answer the difference between Docker, Kubernetes, and Podman.

Docker, Kubernetes, and Podman are all popular containerization tools that allow developers and DevOps to package and deploy applications in a consistent and efficient manner.

Docker is a popular containerization platform that allows developers to create, deploy, and run applications in containers.

Docker provides a set of tools and APIs that enable developers to build and manage containerized applications, including Docker Engine, Docker Hub, and Docker Compose.

On the other hand, Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications.

Kubernetes also provides a set of APIs and tools that enable developers to deploy and manage containerized applications at scale, across multiple hosts and environments.

And*, Podman* is a relatively new containerization tool that is similar to Docker, but with a different architecture. Podman does not require a daemon to run containers, and it is compatible with Docker images and registries.

Podman provides a simple command-line interface for creating and managing containers, and it can be used as a drop-in replacement for Docker in many cases.

Now that we have a basic idea of what they are and what they do, let's dive deeper to understand how they work.

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, Bugfree.ai and Udemy which have many great System design courses

how to answer system design question



What is Docker? How does it work?

As I said, Docker is an open-source platform that enables developers to automate the deployment and management of applications within containers.

It provides a way to package an application and its dependencies into a standardized unit called a container, which can be run on any compatible system without worrying about differences in operating systems or underlying infrastructure.

Here are a few important Docker concepts that you, as a Developer or DevOps Engineer, should know :

1. Containerization
Docker utilizes containerization technology to create isolated environments, known as containers, for running applications. Containers are lightweight and encapsulate the application code, runtime, system tools, libraries, and dependencies required to run the application.

This allows applications to run consistently across different environments, ensuring that they behave the same regardless of the underlying system.

2. Docker Images
A Docker image serves as a template for creating containers. It is a read-only snapshot that contains the application code and all the necessary dependencies.

Docker images are created using a Docker file, which is a text file that specifies the steps to build the image. Each step in the Dockerfile represents a layer in the image, allowing for efficient storage and sharing of images.

3. Docker Engine
The Docker Engine is the core component of Docker. It is responsible for building and running containers based on Docker images. The Docker Engine includes a server that manages the containers and a command-line interface (CLI) that allows users to interact with Docker.

4. Docker Registry
Docker images can be stored in a registry, such as Docker Hub or a private registry. A registry is a centralized repository for Docker` images, making it easy to share and distribute images across different systems. Developers can pull pre-built images from registries or push their own custom images for others to use.

5. Container Lifecycle
To run an application, Docker creates a container from an image. Containers are isolated and have their own filesystem, processes, and network interfaces.

They can be started, stopped, paused, and removed as needed. Docker provides a set of commands and APIs to manage the lifecycle of containers, allowing for easy scaling, updates, and monitoring.

6. Container Orchestration
While Docker itself provides container management capabilities, it also works seamlessly with container orchestration platforms like Kubernetes.

These platforms enable the management of large clusters of containers, handling tasks such as load balancing, scaling, and automated deployments across multiple hosts.

Overall, Docker simplifies the process of packaging, distributing, and running applications by utilizing containerization technology. It helps developers achieve consistency, portability, and scalability for their applications, making it a popular choice in modern software development and deployment workflows.

And, if you want to learn more about Docker, I highly recommend you join a good course like Docker & Kubernetes: The Practical Guide by Maximilian Schwarzmuller on Udemy. It's a highly rated, up-to-date, hands-on course to master Docker.

Here is also a nice diagram from ByteByteGo which highlights key components of Docker and how it works:

How Docker works


What is Kubernetes? How does it work?

Both Docker and Kubernetes are like brothers, and they are often referred to together, but they are very different from each other. Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications.

It provides a framework for running and coordinating multiple containers across a cluster of machines, making it easier to manage complex distributed systems.

Here are important Kubernetes or K8 concepts that I think every developer or DevOps should learn and know:

1. Cluster Architecture
Kubernetes operates in a cluster architecture, which consists of a master node and multiple worker nodes. The master node manages the cluster and coordinates the overall operations, while the worker nodes are responsible for running the containers.

2. Pods
The basic unit of deployment in Kubernetes is a pod. A pod is a logical group of one or more containers that are co-located and share the same resources, such as network namespace and storage.

Containers within a pod can communicate with each other using localhost. Pods are treated as ephemeral units and can be easily created, updated, or terminated.

3. Replica Sets and Deployments
Replica Sets define the desired number of identical pod replicas to be running at any given time.

They ensure high availability and scalability by automatically managing and maintaining the desired number of pod instances.

Deployments are a higher-level abstraction that allows you to manage and update Replica Sets declaratively, enabling seamless rolling updates and rollbacks of application versions.

4. Services
Kubernetes Services provide stable network endpoints to connect to a set of pods. They enable load balancing and expose the containers within a pod to other services or external clients.

Services abstract the underlying pod instances, allowing applications to communicate with other components without worrying about their dynamic nature.

5. Labels and Selectors
Kubernetes uses labels and selectors to enable flexible and dynamic grouping and selection of objects. Labels are key-value pairs attached to pods, deployments, services, and other Kubernetes objects.

Selectors are used to filter and match objects based on their labels, allowing for targeted operations and grouping of related resources.

6. Scaling and Auto-Scaling
Kubernetes allows you to scale applications by adjusting the number of pod replicas. Horizontal Pod Autoscaling (HPA) is a feature that automatically scales the number of pod replicas based on resource utilization metrics such as CPU or memory usage.

7. Container Networking
Kubernetes also manages networking between pods and nodes. Each pod gets its own IP address, and containers within a pod can communicate with each other using localhost.

Kubernetes provides network plugins that facilitate container networking and enable communication across pods and clusters.

8. Cluster Management
Kubernetes also offers extensive cluster management capabilities, including rolling updates, secrets management, configuration management, and health monitoring.

It provides a declarative approach to define the desired state of the system, allowing Kubernetes to continuously monitor and reconcile the actual state with the desired state.

9. Container Storage
Kubernetes supports various storage options, including persistent volumes and storage classes. Persistent volumes provide a way to decouple storage from the lifecycle of pods, enabling data persistence and sharing across pods and container restarts.

By abstracting the complexities of managing containers at scale, Kubernetes enables developers to focus on application logic rather than infrastructure management.

It provides a robust and scalable platform for deploying and managing containerized applications, making it a popular choice for building modern, cloud-native systems.

And, if you want to learn more about Kubernetes, I highly recommend you join a good course like Kubernetes for the Absolute Beginners - Hands-on on Udemy. It's a highly rated, up-to-date, hands-on course to master Docker.

Here is a nice diagram that shows different components of K8 or Kubernetes and how they work together:

What is Kubernetes


What is Podman? How does it work?

Now that you already know what Docker and Kubernetes are, it's time to take a look at another popular tool called Podman, which is often seen as an alternative to Docker.

Podman is an open-source container runtime and management tool that provides a command-line interface (CLI) for managing containers.

It aims to be a compatible alternative to Docker, offering a Docker-compatible API and allowing users familiar with Docker to transition easily*. Podman is designed to provide a secure and lightweight container experience.

Here's an overview of how Podman works and important Podman concepts you should know:

1. Container Runtime
Podman serves as a container runtime, which means it can create and run containers. It uses the Open Container Initiative (OCI)-compatible container format, which ensures compatibility with other container runtimes and allows Podman to run OCI-compliant containers.

2. CLI Compatibility
Podman's CLI is designed to be familiar to Docker users. It provides commands similar to Docker CLI, allowing users to manage containers, images, volumes, and networks with ease.

This compatibility makes it easier for developers and system administrators to transition from Docker to Podman without significant changes to their workflows.

3. Rootless Containers
One notable feature of Podman is its support for rootless containers. It allows non-root users to run containers without requiring privileged access.

This enhances security by isolating containers from the host system and reducing the risk of container escapes.

4. Container Management
Podman provides a range of management capabilities, such as creating, starting, stopping, and removing containers. It supports network configuration, allowing containers to communicate with each other and the host system.

Podman also provides options for managing container volumes, environment variables, and resource constraints.

5. Container Images
Like Docker, Podman relies on container images as the basis for creating containers. It can pull and push container images from various container registries, including Docker Hub. Podman can also build images locally using a Dockerfile or import images from other container runtimes.

6. Pod Support
Podman extends beyond individual containers and supports the concept of pods, similar to Kubernetes. Pods are a group of containers that share the same network namespace and resources.

Podman allows users to create and manage pods, enabling more complex deployments and communication patterns between containers.

7. Integration with Orchestration Platforms
While Podman can be used as a standalone container runtime, it can also integrate with container orchestration platforms like Kubernetes. It can act as the container runtime for Kubernetes pods, allowing users to leverage Podman's features and compatibility within a Kubernetes cluster.

8. Security Focus
Podman places a strong emphasis on security. It supports features such as user namespace mapping, which maps container user IDs to non-root user IDs on the host, enhancing container isolation.

Podman also integrates with security-enhancing technologies like SELinux and seccomp profiles to provide additional layers of protection.

Podman aims to provide a seamless transition for Docker users while emphasizing security and lightweight container management.

It offers compatibility, flexibility, and a user-friendly CLI, making it a compelling option for those seeking an alternative container runtime.

What is Podman


What is the difference between Docker, Kubernetes, and Podman?

Here are the key differences between Docker, Kubernetes, and Podman. I have compared them on different points, which are mainly features, and capabilities each of these tools provides like containerization and container management etc.

1. Container Engine
Docker is primarily a container runtime and engine for building, running, and distributing containers. Kubernetes, on the other hand, is an orchestration platform designed for managing containerized applications across a cluster of machines.

Podman is a container runtime and management tool that provides a Docker-compatible CLI and container runtime.

2. Container Format
Docker uses its own container format called Docker containers. Kubernetes can work with multiple container formats, but Docker containers are the most common choice.

Podman, on the other hand, uses the Open Container Initiative (OCI)-compatible container format and can run OCI-compliant containers.

3. Orchestration
Docker has Docker Swarm, its built-in orchestration tool, which allows managing a swarm of Docker nodes for running containers.

Kubernetes, on the other hand, provides advanced orchestration capabilities for managing containerized applications, including scaling, load balancing, automated deployments, and self-healing.

Podman does not have built-in orchestration capabilities like Docker Swarm or Kubernetes, but it can work alongside Kubernetes or other orchestration platforms.

4. Cluster Management
Docker does not have native support for managing container clusters. Kubernetes, on the other hand, is specifically designed for managing container clusters and provides features for scaling, upgrading, monitoring, and managing containerized applications.

Podman does not have native support for managing container clusters but can be used with external tools like Kubernetes or other container orchestration frameworks.

5. Security
For Security comparison, Docker provides basic isolation and security features, but its primary focus is on running single containers. Kubernetes offers advanced security features such as network policies, secrets management, and RBAC.

Podman, on the other hand, focuses on security and provides features like user namespace mapping, seccomp profiles, and SELinux integration for enhanced container security.

6. User Interface
When it comes to comparing UI, Docker provides a user-friendly CLI and a web-based graphical interface (Docker Desktop) for managing containers. Kubernetes has a CLI tool called "kubectl" and a web-based dashboard (Kubernetes Dashboard) for managing containers and clusters.

Podman provides a CLI similar to the Docker CLI and can be used with third-party tools like Cockpit for web-based management.

And, if you like tables, here is a nice table where I have put all the differences between Docker, Kubernetes, and Podman in tabular format:

difference between Docker, Kubernetes, and Podman

These are the fundamental differences between Docker, Kubernetes, and Podman, each serving different purposes in the containerization ecosystem.


System Design Interviews Resources:

And, here are curated list of best system design books, online courses, and practice websites which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.

  1. DesignGuru's Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

  2. "System Design Interview" by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.

  3. "Designing Data-Intensive Applications" by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.

  4. LeetCode System Design Tag: LeetCode is a popular platform for technical interview preparation. The System Design tag on LeetCode includes a variety of questions to practice.

  5. "System Design Primer" on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.

  6. Educative's System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

  7. High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.

  8. YouTube Channels: Check out channels like "Gaurav Sen" and "Tech Dummies" for insightful videos on system design concepts and interview preparation.

  9. ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of the System Design Interview book volumes 1 and 2 and will be updated with volume 3 which is coming soon.

  10. Exponent: A specialized site for interview prep, especially for FAANG companies like Amazon and Google. They also have a great system design course and many other materials that can help you crack FAAN interviews.

how to prepare for system design

image_credit - ByteByteGo

That's all about the difference between Docker, Kubernetes, and Podman. In summary, Docker is a popular containerization platform for creating and managing containers, Kubernetes is a container orchestration platform for managing containerized applications at scale, and Podman is a containerization tool with a different architecture that can be used as a drop-in replacement for Docker in many cases.

Each of these tools serves a different purpose, and they can all be used together to provide a comprehensive containerization solution for developers, but more importantly, every Developer and DevOps should be aware of these tools.

    The Software Engineer's Reading List for 2026 (10 Books That Matter)

    Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.
    10 Must Read Software Engineering Books for Developers

    Credit - ByteByteGo - https://bit.ly/3P3eqMN

    Hello Devs, if you have worked as a Software Engineer, Developer, or Programmer, you may know that, as software engineers, continuous learning is not just a goal—it's a necessity.

    From one task to the next, from one project to the next, there is always learning required. Many times you need to learn a new programming language or framework, or library, or coding and designing.

    And, when it comes to learning, nothing beats a book.

    Whether you're looking to refine your coding practices, master system design, or gain a deeper understanding of software architecture, the right books can offer insights and techniques to elevate your skills.

    I know that we learn from our experience, and books are a great way to learn from other people's experiences.

    There is a limit to how much you can learn from your own experience, but if you start learning from other folks' experiences, then there is no limit.

    And, books give a glimpse of how experts think, what understanding they have of certain topics that matter most for software engineers and developers.

    In the last few articles, I have shared many popular system design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy as well as 50 system design problems and today I am going to share the 10 books you can read to become a better software engineer.

    This article features ten essential books every software engineer should read, divided into five categories: General Advice, Coding, Software Architecture, Design Patterns, and Data Structures & Algorithms.

    So what are we waiting for? Let's start


    10 Books Every Software Engineer and Developer Must Read

    Without any further ado, here are the best books any Software engineer, developer, or programmer can read.

    We will start with general advice for programmers and then finish it on Data Structures and Algorithms, one topic that separates amateurs from professionals in the programming field.

    General Advice

    1. The Pragmatic Programmer This classic by Andrew Hunt and David Thomas provides actionable advice for software developers, from debugging techniques to career growth. It emphasizes the importance of being adaptable and continuously refining your craft.
    2. Code Complete (2nd Edition) Written by Steve McConnell, this book is often hailed as a comprehensive guide to software construction.

    It provides proven practices for writing robust, maintainable code and improving your software craftsmanship. This book was first recommended to me by our tech lead, who had a tradition of giving this book to every new joiner in the company.

    best coding books for developers


    Coding

    Now, let's see a few coding books that every software engineer should read

    1. Clean Code
      By Robert C. Martin ("Uncle Bob"), this book is a manifesto for writing readable, efficient, and maintainable code. It provides practical examples of bad code versus clean code and teaches you the mindset of a skilled programmer.

    2. Refactoring: Improving the Design of Existing Code
      This book by Martin Fowler is essential for anyone looking to improve their existing codebase without introducing new bugs. It's a step-by-step guide to transforming bad code into good code while preserving functionality.

    best refactoring books for developers


    Software Architecture

    1. Designing Data-Intensive Applications
      By Martin Kleppmann, this book dives deep into the systems that power modern software applications. It's an indispensable resource for understanding distributed systems, data pipelines, and scalability.

    2. System Design Interview (Volumes 1 & 2)
      Designed to prepare you for system design interviews, these books by Alex Xu break down complex systems into simple, digestible components. They're packed with real-world examples and diagrams to help you master system design.

    best software architecture books for developers


    Design Patterns

    1. Design Patterns: Elements of Reusable Object-Oriented Software
      This seminal work by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides (the "Gang of Four") introduces 23 foundational design patterns. It's a must-read for understanding object-oriented programming and software design.

    2. Domain-Driven Design: Tackling Complexity in the Heart of Software
      By Eric Evans, this book explores techniques to handle complex business domains in software development. It emphasizes collaboration between developers and domain experts to create intuitive, effective systems.

    best design pattern books for developers


    Data Structures & Algorithms

    1. Introduction to Algorithms
      Known as the "CLRS book," this comprehensive guide covers a wide range of algorithms and data structures, complete with mathematical rigor. It's an essential resource for both beginners and seasoned developers.

    2. Cracking the Coding Interview
      By Gayle Laakmann McDowell, this book is the ultimate resource for technical interviews. It provides 189 programming questions and solutions, along with strategies to tackle them effectively.

    best data structures and algorithms books for develoeprs


    Why These Programming and Development Books Matter?

    This is a good question: why should you read these books? There are better things you can do, like watching Squid Games season 2 on Netflix or binge-watching YouTube videos, so why should one read these books?

    Well, these ten books collectively address the most critical aspects of software engineering.

    From honing your coding skills and understanding architectural design to mastering data structures and algorithms, they prepare you for real-world challenges and interviews alike.

    Just reading these books will expand your knowledge base, and even if you don't get 100%, you will be a much better engineer than without reading these books.

    If you ask me, I have read most of them, not page to page, but most of it, and I can say that every time I read a book, I come up with more knowledge and wisdom, even on topics that I thought I knew very well.

    If you want to just read one book, start with Clean Code, one of my favorite books on coding. I learned a lot about coding as an art from this book.

    It's the one that takes you from HelloWorld programmer to a professional, well, at least it made that impact for me.

    best programming books for software engineer


    How to Get Started? Which Book Should You Read First

    This is a difficult part, especially if you are presented with a list of 10 books.

    I would suggest starting by identifying the areas where you want to improve.

    If you're aiming for cleaner code, dive into Clean Code or Refactoring: Improving the Design of Existing Code.

    Preparing for interviews? Focus on "Cracking the Coding Interview" and System Design Interview (Volumes 1 & 2)

    If you're venturing into software architecture, Design Patterns: Elements of Reusable Object-Oriented Software is a must-read.

    Remember, each of these books represents a treasure trove of knowledge that can transform your approach to software engineering. So pick one today and start your journey toward mastery!

    All the best with your learning journey !!

    Bonus

    As promised, here is the bonus for you, a free book. I just found a new free book to learn Distributed System Design, you can also read it here on Microsoft --- https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-eBook-DesigningDistributedSystems.pdf

    design patterns in Java

    image_credit --- twitter

      The 5 Most In-Demand Programming Skills to Learn in 2026

      Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.
      10 Must Know System Design Concepts for Interviews

      image_credit - Exponent

      Hello devs, if you have been doing programming and software development, then you know that there is only one constant in our field, and that is "Change".

      We always need to learn new tools, technologies, frameworks, and skills to do our job, and there is no end to it. If you don't learn, you will be left behind with others; that's why I always look for new skills to learn.

      In this article, I am going to share 5 skills programmers and developers can learn in 2026 to become better at their job and also become more efficient.

      In the past, I talked about difference between API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy, which you guys liked a lot and in this article I am going to share top five developer skills that are set to take center stage in 2026.

      P.S. Keep reading until the end. I have a bonus for you.

      These skills include both new and old but essential skills like prompt engineering, coding, cloud computing realms, system design, and Python; these skills are in demand and essential for any software developer.

      Whether you're an experienced developer looking to upgrade yourself or an intermediate developer who wants to enhance their profile, these skills will certainly help you.

      5 Skills Software Engineers Should Learn in 2026

      In the ever-evolving landscape of technology, staying ahead of the curve is essential for developers seeking to thrive in 2026 and beyond.

      As we navigate through the dynamic realms of software development, certain skills have emerged as indispensable for the modern developer, and that's what you are going to learn in this article.

      Let's deep dive into the top five developer skills that are poised to make a significant impact in 2026.

      1. System Design

      In 2026, developers need to go beyond traditional coding practices and embrace modern system design principles. This involves understanding distributed systems, microservices architecture, and designing scalable and resilient applications.

      Developers who can navigate complexities in system design are better equipped to create robust solutions that can handle the demands of a rapidly evolving technological landscape.

      If you want to learn System Design in depth, then you can also check out sites like ByteByteGo, Design Guru, Exponent, Educative Bugfree.ai and Udemy which have many great System design courses

      how to answer system design question

      If you need more resources, then here is a list of System design books, courses, and websites to learn and master Software design and architecture in 2026.


      2. Prompt Engineering

      In this era of AI tools, Software development is not untouched, and more and more AI tools are coming to help you with Coding, debugging, and testing, but you need prompt engineering to make effective use of these tools.

      In other words, Prompt engineering has become a crucial skill for developers aiming to deliver high-quality products quickly.

      This involves not only writing efficient and concise code but also adopting agile methodologies and tools to streamline the entire development process.

      Developers proficient in prompt engineering can rapidly respond to changing requirements, ensuring their software remains adaptable and resilient in the face of evolving market demands.

      If you need a resource to learn ChatGPT and Prompt Engineering, then I suggest you see ChatGPT Prompt Engineering for Developers course on Coursera.

      best prompt engineering courses

      If you need more options, then you can also see these ChatGPT and Prompt Engineering courses to learn more.


      3. Coding

      While coding might seem like an obvious skill, its significance cannot be overstated. Mastery in coding goes beyond writing functional programs; it involves writing clean, maintainable, and scalable code.

      Developers should focus on enhancing their proficiency in programming languages, understanding algorithms, and implementing best practices.

      A solid foundation in coding forms the bedrock for success in any development endeavor. And, if you need a resource, you can check out The Complete JavaScript Course 2026: Build Real Projects to start with.

      best course to learn coding with javascript

      If you need more choices, you can also check out these Programming and Coding courses


      4. Cloud Computing

      Cloud computing has transcended from being a buzzword to an essential skill for developers. With the increasing reliance on cloud services, developers must be well-versed in deploying, managing, and optimizing applications in cloud environments.

      Platforms like AWS, Azure, and Google Cloud are integral to modern development, and developers proficient in cloud computing can create scalable and cost-effective solutions.

      And, if you need resources, you can see these articles where you can find highly recommended resources to learn Cloud Computing

      best course to learn AWS

      And, if you need more choices, you can also see this article where you can find highly recommended AWS Fundamentals Specialization on Coursera. This program is created by AWS itself.


      5. Python

      Python continues to assert its dominance as a versatile and powerful programming language. From web development to data science, machine learning, and artificial intelligence, Python is at the forefront of innovation.

      Developers in 2026 should invest in mastering Python, as it not only facilitates rapid development but also provides a gateway to a plethora of cutting-edge technologies shaping the future of the industry.

      If you want to learn and master Python in 2026, you can start with this Python course 100 Days of Code: The Complete Python Pro Bootcamp for 2026, which I am also using to learn Python nowadays.

      best courses to learn Python

      If you need more resources, you can also see these Python books, courses, and websites to learn Python in depth.

      That's all about the top 5 developer skills you can learn in 2026. In conclusion, the top developer skills for 2026 reflect the industry's demand for agility, adaptability, and technical excellence.

      Whether you are a seasoned developer or just starting your coding journey, honing these skills will undoubtedly position you as a valuable asset in the ever-evolving world of software development.

      Embrace the challenges, stay curious, and embark on a journey of continuous learning to thrive in the dynamic landscape of 2026 and beyond.

      Bonus

      As promised, here is the bonus for you, a free book which you can read to learn Distributed System Design. You can either download the Free PDF or read online on Microsoft --- https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-eBook-DesigningDistributedSystems.pdf

      free books to learn Distributed System design

      And let me know in the comments which skills you are learning in 2026? I am always eager to learn skills that can enhance my profile as a software developer.

      Thank you

        10 Real Microservices Architecture Challenges Every Senior Engineer Must Know

        Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

        Microservices architecture best practices

        image_credit - ByteByteGo

        Hello friends, if you are preparing for system design interview then you must also prepare for Microservices architecture. It's favorite architecture of many interviewers and it provide a lot of material to grill you.

        There is no doubt that Microservices architecture has revolutionized software development by breaking down monolithic applications into smaller, loosely coupled services.

        In the past, I have shared several system design interview articles like API Gateway vs load balancer, Forward Proxy vs Reverse Proxy as well common System Design problems and in this article we will discuss about the challenges of Microservices architecture.

        It's also one of the essential System design topics or concepts for programmers to know.

        While Microservices approach promises increased scalability, flexibility, and faster development cycles but it comes with its own set of challenges which is very important for a developer to know, not just know but to solve them efficiently.

        While there are many articles which talk about Microservices best practices, there are few which put light on what benefits they offer and what challenges they solve.

        In this article, we will explore the ten key challenges that developers face when working with microservices and learn effective strategies to overcome them.

        By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative and Udemy which have many great System design courses

        Also a solid knowledge of various Microservices patterns like Service Discovery, CQRS, and Saga goes a long way in solving many of the challenges we are going to discuss in this article, on that note, here is a nice diagram from DesignGuru.io on how the service discovery work in Microservices, we will use this pattern later in the article

        Service discovery in Microservices


        10 Challenges of Microservices Development and Solutions

        Here is a list of key challenges one can face while creating applications using Microservices architecture

        1. Service Communication Challenges

        If you have worked in a real world Microservice architecture then you may know that Microservices rely heavily on inter-service communication, which can become a challenge as the number of services grows.

        With each service having its own API and protocols, managing communication becomes complex.

        To deal with this, adopt communication patterns like REST, message queues, and event-driven architecture. Also, consider using API gateways to centralize communication logic and handle cross-cutting concerns.

        microservice architecture challenges


        2. Data Management Challenges

        Data management across microservices can be intricate due to the decentralized nature of the architecture. Inconsistent data models and maintaining data consistency pose difficulties.

        In order to solve this problem, you can implement a polyglot persistence strategy, using databases that suit the specific needs of each service.

        You should also leverage techniques like event sourcing andCQRS (Command Query Responsibility Segregation)to maintain data integrity and separation of read and write operations.

        Data Management challenges on Microservices


        3. Distributed Tracing and Monitoring Challenge

        Monitoring and debugging microservice applications becomes really challenging as requests span multiple services. Traditional monitoring tools may not provide the required visibility.

        In order to solve this proble,m you should integrate distributed tracing systems like Jaeger or Zipkin to track requests across services.

        You can also use centralized logging and monitoring solutions to aggregate and analyze logs and metrics from various services, aiding in early issue detection.

        For developers, debugging issues in Microservices is one of the biggest challenges to deal with, and knowing about tracing systems like Zipkin really works.

        Distributed Tracing and Monitoring Challenge in Microservice architecture


        4. Service Orchestration and Choreography Challenges

        Microservices can be orchestrated centrally or choreographed in a decentralized manner. Both approaches have their challenges.

        Orchestrating services might lead to a single point of failure, while choreography can result in increased complexity and difficulty in tracking the flow.

        In this situation, you should strive for a balance, employing orchestration for critical workflows and choreography for services that can operate independently.

        Service Orchestration and Choreography Challenges in Microservices


        5. Deployment and DevOps Challenges

        The deployment of Microservices involves managing multiple service instances and ensuring compatibility across different environments. It's almost impossible to deploy a microservice using the traditional way.

        Containerization using tools like Docker and orchestration using Kubernetes can help standardize deployment processes, and in fact, they are a must if you want to have peace of mind.

        You should also embrace DevOps practices and automate deployment pipelines to ensure consistency and rapid deployment of microservices.

        Deployment and DevOps Challenges in Microservices


        6. Testing across Services Challenges

        Testing Microservices is not easy at all; it requires comprehensive strategies due to the intricate nature of their interactions.

        Traditional unit testing might not be sufficient.

        To solve this problem, you can incorporate integration testing, contract testing, and end-to-end testing to validate service interactions and data flow.

        You should also implement a robust CI/CD pipeline that automates testing across the entire microservices ecosystem.

        esting across Services Challenge in Microservices


        7. Security and Access Control Challenges

        Microservices can expose numerous endpoints, increasing the potential attack surface. Most of the time, you will not even be aware of this, but don't worry, almost all big organizations have a big security team with fat pay to hassle you.

        On your part, you should ensure security across services, managing authentication and authorization, and securing data in transit, which pose significant challenges.

        Adopt a zero-trust security model, implement API security standards like OAuth2 and JWT (JSON Web Tokens), and employ API gateways with strong access control mechanisms.

        Security and Access Control Challenges in Microservices

        credit --- superTokens


        8. Scalability and Resource Allocation

        Scalability is a central promise of microservices and one of the main drivers for many companies to ditch monoliths in favor of Microservices, but it requires careful planning.

        Some services might experience heavier loads than others, leading to resource allocation challenges.

        You should utilize container orchestration platforms and tools like K8 to dynamically allocate resources based on demand.

        You can also implement auto-scaling based on metrics like CPU usage or request rate to ensure optimal resource utilization.

        Scalability and Resource Allocation challenges in Microservices


        9. Versioning and Compatibility Challenges

        As Microservices evolve independently, maintaining backward and forward compatibility becomes vital.

        Incompatible changes can disrupt the entire system.

        As an experienced developer or tech lead, you should implement versioning for APIs, both at the code level and in communication protocols.

        You can also utilize semantic versioning to clearly communicate compatibility expectations. Gradually phase out older versions while providing adequate support and documentation for migrations.

        Versioning and Compatibility Challenges in Microservices


        10. Organizational Complexity and Communication Challenges

        Microservices architecture can mirror an organization's structure, leading to challenges in communication and collaboration, for example, different teams managing different microservices.

        It's important that Cross-functional teams working on different services need to align their efforts.

        As an experienced hand, you should foster a culture of communication and collaboration through regular meetings, shared documentation, and tools that facilitate information exchange.

        Organizational Complexity and Communication Challenges


        Top 10 System Design Interviews Resources for 2026

        And, here is the curated list of best system design books, online courses, and practice websites which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.

        1. ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of the System Design Interview book volumes 1 and 2 and will be updated with volume 3, which is coming soon.

        2. Exponent: A specialized site for interview prep, especially for FAANG companies like Amazon and Google. They also have a great system design course and many other materials that can help you crack FAANG interviews.

        3. DesignGuru's Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

        4. LeetCode for System Design - Bugfree.ai: This is a popular platform for technical interview preparation. The System Design tag includes a variety of questions to practice.

        5. "System Design Primer" on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.

        6. Educative's System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

        7. High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.

        8. YouTube Channels: Check out channels like "Gaurav Sen" and "Tech Dummies" for insightful videos on system design concepts and interview preparation.

        9. "System Design Interview" by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.

        10. "Designing Data-Intensive Applications" by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.

        And, here is a nice system design interview cheat sheet to quickly revise essential System design concepts:

        system design interview cheat sheet

        image_credit - tryExponent

        Conclusion

        That's all about the Microservices architecture challenges and how to deal with them. Microservices architecture offers remarkable benefits in terms of scalability, flexibility, and faster development.

        However, these advantages are accompanied by a unique set of challenges that developers must navigate effectively.

        By adopting best practices in service communication, data management, monitoring, testing, security, and more, teams can overcome these challenges and unlock the full potential of microservices.

        As the landscape of software development continues to evolve, addressing these challenges will remain essential for successful microservices implementation

        While I write this article for system design interview preparation, its equally valuable to experienced developers who are working with Microservices and want more control and better organization.

        All the best with Microservices development !!

        Bonus\
        As promised, here is the bonus for you, a free book. I just found a new free book to learn Distributed System Design, you can also read it here on Microsoft --- https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-eBook-DesigningDistributedSystems.pdf

        free book on distributed systems

          5 Best Artificial Intelligence Courses to Take on Frontend Masters in 2026

          Top 5 Frontend Masters Courses to Learn Artificial Intelligence

          Hello guys, if you are a Software developer then let me tell you an uncomfortable truth: most developers think AI is someone else’s problem. It’s not. It’s your problem. And it’s urgent.

          Every developer who hasn’t learned AI in 2026 is betting that their career will continue without it. That’s not a bet — that’s denial.

          Frontend Masters isn’t your typical online learning platform. It’s different because it’s taught by people like Scott Moss (former Netflix engineer), Will Sentance (creator of some of the best developer education), and Maximiliano Firtman (web pioneer).

          These aren’t content creators teaching theory. These are engineers shipping AI in production.

          The courses on Frontend Masters don’t teach you AI academia. They teach you how to actually build things with AI. How to integrate LLMs into your apps. How to deploy agents. How to think about AI architecture. That’s what matters in 2026.

          Let me walk you through the five best AI courses on Frontend Masters which you watch to improve your AI knowledge and gain some hands-on AI skills in 2026.

          5 Best Frontend Masters Courses to learn Artificial Intelligence and Agentic AI in 2026

          Without any further ado, here are the best frontend masters courses you can join in 2026 to learn both Artificial Intelligence as well as Agentic AI from scratch.

          1. Hard Parts of AI: Neural Networks by Will Sentance

          Will Sentance has this gift of making complex concepts feel obvious in hindsight. This course explores the full mental models of AI prediction and LLM model development — from the theory of how neural networks work to actually building predictive systems.

          What You’ll Learn:

          • How AI prediction fundamentally works
          • Building mental models of neural networks
          • How LLMs are developed and function
          • Real-world predictive systems (like DoorDash refund automation)
          • The intuition behind machine learning
          • Why predictions work the way they do
          • Deploying ML models to production

          Why It Works: Most developers learn AI and still don’t understand it. Will teaches you to understand. You’ll know why neural networks work, not just that they do. This knowledge compounds. Every AI project after this becomes clearer.

          The course uses real examples like automated refund request systems, making abstract concepts concrete and immediately applicable.

          Here is the link to join this course — Hard Parts of AI: Neural Networks

          Best For: Developers who want to truly understand AI at a fundamental level before building with it. If you want the “why,” this is it.

          Time Commitment: 8–10 hours of focused learning

          Student Reviews: “The course made some of the concepts in the field of AI less intimidating while building great mental models for understanding.”

          2. Build AI Agents from Scratch by Scott Moss

          This is where theory meets reality. Scott Moss teaches you to build actual AI agents — systems that can think, decide, and take action — from the ground up.

          What You’ll Learn:

          • Large Language Models (LLMs) fundamentals
          • Building conversational agents and transactional agents
          • Function calling and custom tool integration
          • Integrating Claude, ChatGPT, and other LLMs
          • Building agents that read from APIs (like Reddit)
          • Generating images with DALL-E
          • Real-world agent architecture patterns

          Why It Works: Scott believes in learning by building. You’ll create an agent that reads from Reddit, generates images, and provides random dad jokes. Sounds simple? Wait until you understand the architecture behind it.

          This course is hands-on, direct, and focused on what actually works in production. Scott doesn’t waste time on theory you don’t need.

          Here is the link to join this course — Build AI Agents from Scratch

          Build AI Agents from Scratch by Scott Moss

          Best For: Developers who want to build AI agents immediately. If you want to ship something, start here.

          Prerequisites: JavaScript knowledge and familiarity with APIs

          Student Feedback: “The course truly opened my mind to what’s possible when building intelligent systems. Scott is great — he has this super relaxed, but brilliant mad scientist vibe that makes learning feel comfortable and enjoyable.”

          3. AI Agents: From Prototype to Production by Scott Moss

          You built an agent. Now make it production-ready. This advanced course takes everything from the fundamentals course and applies it to real-world deployment challenges.

          What You’ll Learn:

          • Taking prototype agents to production
          • Memory management and context handling
          • Evals and human-in-the-loop systems
          • Retrieval Augmented Generation (RAG)
          • Production guardrails and safety
          • Monitoring and debugging AI agents
          • Scaling agents for reliability
          • Real-world deployment patterns

          Why It Works: Building an agent locally is fun. Making it handle edge cases, maintain memory across sessions, and operate reliably in production is hard. This course teaches you that hard part.

          Scott’s experience at Netflix and his daily work with AI in production means you’re learning what actually works when real users depend on your systems.

          Best For: Developers who’ve built prototype agents and want to ship them professionally. If your agent will handle real traffic, you need this.

          Prerequisites: Complete “Build AI Agents from Scratch” first

          Student Testimonial: “This course builds on the fundamentals and prepares AI apps for actual production deployment.”

          Here is the link to join this course — AI Agents: From Prototype to Production

          4. Build AI-Powered Apps with OpenAI and Node.js by Scott Moss

          This course dives deep into AI without building a UI. Pure AI integration with Node.js and OpenAI. You’ll learn how to leverage AI techniques in applications at the backend level.

          What You’ll Learn:

          • OpenAI API integration
          • Building AI applications with Node.js
          • Semantic search and embeddings
          • Function calling and tool use
          • Creating AI chat interfaces
          • Document question-answering systems
          • Image generation with APIs
          • Building production AI systems

          Why It Works: Most AI courses mix in frontend complexity. This focuses purely on AI — what you can do with LLMs, how to architect AI flows, how to build useful systems. No UI distractions.

          The course includes real code examples that you can reference, with detailed explanations for every line. You’ll understand not just what you’re doing but why.

          Best For: Backend developers wanting to integrate AI into their applications. Node.js developers building AI systems. Anyone wanting AI expertise without frontend complexity.

          Student Experience: “Scott’s teaching style is mostly coding. He dives deep, gets practical, and doesn’t waste time on unnecessary theory. Perfect for learning by doing.”

          Here is the link to join this course — Build AI-Powered Apps with OpenAI and Node.js

          5. First Look: ChatGPT API for Web Developers by Maximiliano Firtman

          Maximiliano teaches you how ChatGPT and modern LLMs change web development. From chatbots to plugins to content generation, this course shows practical ways to integrate AI into web applications.

          What You’ll Learn:

          • Integrating ChatGPT into web apps
          • Building chatbots and conversational interfaces
          • Using AI for content creation and personalization
          • Language translation with LLMs
          • Plugins and API integrations
          • AI in web development workflows
          • ChatGPT plugins architecture
          • Practical web + AI examples

          Why It Works: Maximiliano approaches AI from a web developer’s perspective. How do I use this in my next project? How does this change my workflow? How do I integrate it into existing applications?

          Real examples include building plugins, creating personalized content, and using AI for customer interactions.

          Here is the link to join this course — First Look: ChatGPT API for Web Developers

          Best For: Web developers wanting to understand how AI fits into web development. Frontend developers integrating AI features. Anyone building customer-facing AI applications.

          Time Commitment: 6–8 hours

          Practical Value: Immediate application in web projects

          My Recommended Learning Path

          There are three ways you can take these courses:

          Path 1: I Want to Understand AI Fundamentally (2–3 months)

          1. Week 1–2: AI Fundamentals for Software Engineers — Build mental models
          2. Week 3–4: First Look: ChatGPT API for Web Developers — See practical integration
          3. Week 5–6: Build AI Agents from Scratch — Apply your knowledge

          Path 2: I Want to Build AI Applications (3–4 months)

          1. Week 1–2: Build AI Agents from Scratch — Get building immediately
          2. Week 3–5: Build AI-Powered Apps with OpenAI and Node.js — Deepen your integration skills
          3. Week 6–8: AI Agents: From Prototype to Production — Ship it for real

          Path 3: The Complete AI Mastery (4–5 months) Take all five courses in order. You’ll have comprehensive knowledge from fundamentals to shipping production systems.

          Why Frontend Masters for Learning AI?

          You must be wondering why frontend masters for learning AI? Isn’t Frontend Masters is specialized on frontend development courses? well, that’s true but in the recent years they have expanded their courses and brought the same quality on teaching other fields including AI.

          Here are a couple of reasons why I recommend Frontend Masters courses to learn AI in 2026:

          1. Taught by practitioners — Scott Moss, Will Sentance, Maximiliano. Real engineers shipping real systems.
          2. Production-focused — Not theory. What works in production. What you’ll actually do.
          3. Hands-on learning — Build things. Run code. Ship projects.
          4. Updated continuously — AI moves fast. These courses stay current for 2026.
          5. Community of learners — Lifetime access, course forums, peer support.
          6. Comprehensive coverage — From fundamentals to production deployment.

          All these reasons make Frontend Masters a great place to learn AI, particularly Agentic AI in 2026.

          Get Started Today

          AI adoption is accelerating. The window where you can learn AI and gain an advantage is closing. In 2026, knowing AI isn’t special anymore — not knowing it is a liability.

          Frontend Masters gives you the education you need to stay ahead.

          Join Frontend Masters today for annual membership (~$390/year) and get unlimited access to these AI courses plus 200+ others. It’s the best investment you can make in your 2026 career.

          Your AI education starts now. Pick a path. Commit to it. In a few months, you’ll be building AI applications that your competition only dreams about.

          The future is AI. Make sure you’re building it.

          Start your AI journey on Frontend Masters →

          Happy learning!

          Other Frontend Masters Resources you may like to read

          Thank you for reading this article till the end. If you like these Frontend masters courses then please share with your friends and colleagues. If you have any questions or doubts then feel free to ask.

          P. S. — If you are keen to level up your frontend skills then joining frontend master can be a great first step as they have awesome courses to learn valuable frontend skills, you can join Frontend Masters now and even get a 17% discount on their annual plan.