System Design Basics - GraphQL-vs REST vs gRPC

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 GraphQL, REST, and gRPC

image_credit - DesignGuru

Hello devs, if you are preparing for Coding interviews interviews the along with System Design, and Microsrvices, you should also prepare about things like REST, GraphQL, and gRPC like what is difference between REST, GraphQL, and gRPC?, which is also one of the popular questions on programming interviews.

Earlier, I have talked about difference between API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy and difference between JWT, OAuth, and SAML and in this article, I am going to share my thoughts on REST, GraphQL, and gRPC, three popular communication protocols used for building web APIs.

They are used to allow different software components to communicate with each other over a network, for example Microservices can use REST for synchronous communication between them.

Each of these protocols has its own set of advantages and disadvantages, and understanding the differences between them is not just important from tech interview point of view but also important for choosing the right one for your project.

In this article, you will learn about the differences between REST, GraphQL, and gRPC. You will learn the core concepts behind each protocol, their strengths and weaknesses, and provide some use cases for when to use each one.

By the end of this article, you should have a better understanding of which protocol is best suited for your project's requirements.

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

We will first start with some introduction then we will deep dive into each of them and then again revisit the difference so that you have clear understanding of their strength and weakness and when to use them.

REST stands for Representational State Transfer and it is a popular protocol used for creating web services that expose data and functionality over HTTP.

It is based on HTTP protocol and a set of constraints that define how resources are identified and addressed, and how operations can be performed on those resources.

On the other hand, GraphQL is a query language for APIs that was developed by Facebook. It allows clients to specify exactly what data they need, and the server responds with only that data.

GraphQL was created to address shortcomings and limitation of REST, hence it provides a more flexible and efficient way of fetching data from a server, as clients can request multiple resources in a single request.

And, gRPC is a high-performance, open-source protocol used for creating APIs. It uses Google's Protocol Buffers as a data format and provides support for streaming and bi-directional communication.

gRPC is often used in microservice architectures because of its performance and support for multiple programming languages.

Now that we know what they are let's deep dive into each of them.


What is REST? When to use it?

As I said, REST (Representational State Transfer) is an architectural style for designing distributed applications, particularly web-based APIs.

RESTful APIs use HTTP methods (such as GET, POST, PUT, DELETE) to perform CRUD (Create, Read, Update, Delete) operations on resources identified by a URL (Uniform Resource Locator).

If you know HTTP you know REST.

REST also relies on a stateless client-server architecture, where each request from the client contains all the information necessary for the server to fulfill the request, without needing to maintain session state.

Here are some scenarios when REST is a good choice:

  1. When you need to expose data and services via an API because REST is a popular and well-established protocol for creating APIs that can be easily consumed by other applications and services.

  2. When you need to support multiple platforms and programming languages because REST relies on standard HTTP methods and data formats, it can be used by a wide variety of programming languages and platforms.

  3. When you need to support caching because REST supports caching, which can improve performance and reduce network traffic.

  4. When you need to build simple, lightweight APIs

  5. When you need to support a large number of resources

Also, understanding of HTTP methods are very important for designing a REST API. You can further see REST API Design, Development & Management course to learn about REST API design, development, and management.

When to use REST APIS

Overall, REST is a flexible and widely adopted protocol that is a good choice for many types of APIs.

However, it may not be the best choice for all scenarios, particularly those that require real-time updates or more complex querying and data manipulation.

In those cases, other protocols such as GraphQL or gRPC may be more appropriate.


What is GraphQL? When to use it?

GraphQL is a query language for APIs that was developed by Facebook in 2012 and released as an open-source project in 2015. It was originally created to address limitation and shortcomings of REST.

GraphQL allows clients to define the structure of the data they need, and servers to respond with exactly that data, without any unnecessary data.

It's often used as an alternative to RESTful APIs, particularly for scenarios where the client needs fine-grained control over the data that's returned.

Here are some scenarios when GraphQL is a good choice:

  1. When you want to reduce network traffic as GraphQL allows clients to specify exactly what data they need, which can reduce the amount of unnecessary data that's transmitted over the network.

  2. When you need to support a wide variety of clients because GraphQL supports strongly-typed queries, which can be used to ensure that clients receive the correct data in a format they understand.

  3. When you need to support real-time updates as GraphQL supports real-time updates via subscriptions, which allow clients to receive updates as soon as they're available.

  4. When you need to support complex queries and data manipulation: because GraphQL allows clients to perform complex queries and data manipulation operations, such as filtering, sorting, and aggregation, with a simple syntax.

  5. When you need to support versioning because GraphQL supports versioning by allowing clients to specify the version of the schema they're using in their requests, which can make it easier to maintain backward compatibility as the schema evolves over time.

Overall, GraphQL is a powerful and flexible protocol that can be a good choice for scenarios where fine-grained control over data and real-time updates are important.

However, it may require more setup and configuration than RESTful APIs, particularly if you're working with multiple programming languages or platforms.

You can further see GraphQL by Example and GraphQL with React: The Complete Developers Guide to learn more about GraphQL and how to use it.

and Here is also a nice diagram highlighting the difference between REST and GraphQL queries:

When to use GraphQL


What is gRPC? When to use it?

Now let's see what is gRPC and what does it offer? Well, gRPC is a high-performance, open-source framework for remote procedure calls (RPC) developed by Google.

It uses Protocol Buffers as the interface description language and supports a wide range of programming languages, making it easy to build distributed systems that work across different platforms and environments.

Here are some scenarios when gRPC is a good choice:

  1. When you require high performance and efficiency because gRPC uses a binary protocol and supports streaming, which can make it much faster and more efficient than other protocols, particularly over high-latency or low-bandwidth connections.

  2. When you require to support a wide range of programming languages because gRPC supports many programming languages, including Java, C++, Python, and Go, making it easy to build distributed systems that work across different platforms and environments.

  3. When you need to support real-time updates because gRPC supports bidirectional streaming, which allows servers to send updates to clients in real-time.

  4. When you need to work with large amounts of data since gRPC uses Protocol Buffers, which are more efficient and compact than other data formats like JSON or XML, making it a good choice for working with large amounts of data.

  5. When you need to build microservices or distributed systems because gRPC provides a powerful and flexible framework for building microservices and distributed systems that can scale horizontally and handle large volumes of traffic.

Overall, gRPC is a powerful and efficient protocol that can be a good choice for scenarios where performance, efficiency, and real-time updates are important.

However, it may require more setup and configuration than other protocols like RESTful APIs, particularly if you're working with multiple programming languages or platforms.

You can further see Complete Guide to Protocol Buffers 3 [Java, Golang, Python] and gRPC [Java] Master Class: Build Modern API & Microservices  to learn more about gRPC and Google Protocol buffer.

Here is a nice diagram which highlights the difference between REST, gRPC and GraphQL request as well

Difference between REST and GraphQL

image_credit --- https://medium.com/@LadyNoBug/grpc-v-s-rest-v-s-others-5d8b6eaa61df


Difference between GraphQL, REST and, gRPC

Now that you know what is REST, gRPC, and GraphQL and how they work, here are the key differences between REST, GraphQL, and gRPC in point format remember their key characteristic and when to use each of them in your project:

REST:

  • Stands for Representational State Transfer
  • Uses HTTP methods (GET, POST, PUT, DELETE) to perform CRUD operations
  • Sends data in a structured format, usually JSON or XML
  • Can have multiple endpoints for different resources
  • Clients receive all the data specified in the response, even if they don't need it all
  • Caching is supported, but can be complex to manage
  • Well-established and widely adopted, with extensive tooling and documentation available

GraphQL:

  • Allows clients to specify exactly what data they need, and receives only that data
  • Uses a single endpoint to access multiple resources
  • Has its own query language that allows for complex data fetching and manipulation
  • Can support real-time updates via subscriptions
  • Can be more efficient than REST in certain situations, particularly for mobile devices with limited bandwidth
  • Caching can be more fine-grained and easier to manage than with REST
  • Requires more setup and configuration than REST, and may require more expertise to use effectively

gRPC:

  • Stands for Remote Procedure Call (RPC) with Google's Protocol Buffers
  • Uses binary data for communication instead of HTTP
  • Supports streaming data for real-time updates
  • Uses protocol buffers for serialization, which can be more efficient than JSON or XML
  • Can be used across different programming languages
  • Designed for high-performance, low-latency communication between microservices
  • Requires more setup and configuration than REST, and may require more expertise to use effectively
  • Can be less interoperable than REST or GraphQL, since it is not based on HTTP

Here is also a nice table which highlight the difference between REST, GraphQL, and gRPC, you can use it for quick revision:

Difference between REST, GraphQL, and gRPC

It's also worth noting that these protocols are not mutually exclusive, and it's possible to use them in combination to take advantage of their different strengths.

For example, you might use REST for most of your API, but use GraphQL for certain resource-intensive queries, or use gRPC for communication between microservices while using REST or GraphQL for external API clients.


System Design Interviews Resources:

And, if you are preparing for System design interview then 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.

  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 FAAN 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. Bugfree.ai: LeetCode for System design,  a popular platform for technical interview preparation. It 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.

how to prepare for system design

image_credit - ByteByteGo

Conclusion

That's all about difference between REST, GraphQL, and gRPC technology. In short, REST is a popular protocol used for creating web services, inspired by HTTP and take full advantages of what HTTP offers, while GraphQL is a query language that allows clients to specify exactly what data they need from a server.

It was created to address shortcoming of REST, so its definitely a viable option if you are struggling to maintain your REST APIs.

On the other hand, gRPC is a high-performance, open-source protocol that is often used in microservice architectures.

Each of these protocols serves a different purpose, and they can all be used together to provide a comprehensive and efficient communication system for web applications.

    Top 5 Skills Developer Should Learn in 2026 Apart from AI

    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.

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

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    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.


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    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.

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    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.

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    Bonus

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    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

      System Design Basics - Apache Kafka vs RabbitMQ vs ActiveMQ

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

      Apache Kafka vs RabbitMQ vs ActiveMQ

      image_credit - Design Guru

      Hello devs, if you are preparing for System Design interviews then along with popular software design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy, you should also prepare about things like messaging brokers, kafka, rabbitmq, and activemq like what is difference between Kafka, RabbitMQ, and ActiveMQ?, which is also one of the popular questions on Java interviews.

      In my last article, I shared about 50 System Design Interview Questions and REST vs GraphQL vs gRPC, and in this article, I am going to share my thoughts on Kafka, RabbitMQ, and ActiveMQ, three popular message brokers used for asynchronous communication.

      Messaging systems and Message brokers play a crucial role in modern distributed architectures, where applications and services communicate with each other over a network.

      The messaging systems allow decoupling of the sender and receiver, thereby enabling asynchronous communication. RabbitMQ, Apache Kafka, and ActiveMQ are three popular messaging systems used in the industry.

      In this article, we will discuss the differences between RabbitMQ, Apache Kafka, and ActiveMQ.

      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 , bugfree.ai, and Udemy which have many great System design courses and if you need free system design courses you can also see the below article.

      And, if you are in a hurry, here is a table from ByteByteGo which compares Kafka with RabbitMQ on different parameters like architecture, structure, working, etc

      Difference between Kafka, RabbitMQ and ActiveMQ
      Rabbit MQ vs Kafka

      What is Apache Kafka and where is it used?

      Apache Kafka is an open-source distributed event streaming platform that was originally developed by LinkedIn. Kafka is written in Scala and Java and is designed to handle large-scale streaming data flows.

      Kafka uses a publish/subscribe messaging model and is optimized for high throughput, low latency, and fault-tolerance.

      Kafka has a durable messaging model, which means that messages are stored on disk and can be replayed multiple times.

      If you want to learn more about Kafka, particularly from a system design point of view, you can also join ByteByteGo, a great platform to learn essential system design concepts

      What is Apache Kafka? where it is used


      What is RabbitMQ and where is it used?

      RabbitMQ is an open-source message broker that implements the Advanced Message Queuing Protocol (AMQP) standard.

      It is written in Erlang and has a pluggable architecture that allows for easy extensibility.

      RabbitMQ supports multiple messaging patterns such as publish/subscribe, request/reply, and point-to-point, and it has a robust set of features such as message acknowledgment, routing, and queuing.

      Arslan Ahmend has explained about RabbitMQ in his classic Grokking the System design interview course, if you are preparing for a tech interview, you can also see that resource for better preparation.

      What is RabbitMQ? where it is used


      What is ActiveMQ? where does it used?

      Apache ActiveMQ is an open-source message broker that implements the Java Message Service (JMS) API. ActiveMQ is written in Java and has a pluggable architecture that allows for easy extensibility.

      ActiveMQ supports multiple messaging patterns such as point-to-point, publish/subscribe, and request/reply, and it has a robust set of features such as message acknowledgment, routing, and queuing.

      What is ActiveMQ? where does it used?


      Differences between RabbitMQ, Apache Kafka, and ActiveMQ?

      Now that you have a fair idea of what is RabbitMQ, ActiveMQ, and Apache Kafka, it's time to find out the difference between them from messaging model to performance.Here are key differences between Apache Kafka, RabbitMQ and ActiveMQ:

      1. Messaging Model

      RabbitMQ and ActiveMQ both support the JMS API, which means that they follow a traditional messaging model where messages are sent to a queue or a topic and consumed by one or more consumers.

      On the other hand, Kafka uses a publish/subscribe messaging model, where messages are published to a topic and consumed by one or more subscribers.

      The traditional messaging model used by RabbitMQ and ActiveMQ is well-suited for applications that require strict ordering and reliable delivery of messages.

      On the other hand, the publish/subscribe messaging model used by Kafka is better suited for streaming data scenarios, where real-time processing of data is required.

      Here is a nice diagram which highlight the architecture difference between Kafka and RabbitMQ

      Kafka vs RabbitMQ


      2. Scalability

      Scalability is an essential requirement for messaging systems, especially when dealing with large volumes of data. RabbitMQ and ActiveMQ are both designed to be scalable, but they have different approaches to achieving scalability.

      RabbitMQ uses a clustering approach to achieve scalability, where multiple RabbitMQ brokers are connected to form a cluster. Messages are distributed across the cluster, and consumers can connect to any broker in the cluster to consume messages.

      RabbitMQ also supports federation, which allows multiple RabbitMQ clusters to be connected together.

      ActiveMQ uses a network of brokers approach to achieve scalability, where multiple ActiveMQ brokers are connected to form a network.

      Messages are distributed across the network, and consumers can connect to any broker in the network to consume messages. ActiveMQ also supports master/slave replication, which provides high availability for the message broker.

      Kafka, on the other hand, is designed to be highly scalable out of the box. Kafka uses a partitioning approach to achieve scalability, where messages are partitioned across multiple Kafka brokers.

      Each partition is replicated across multiple brokers for fault tolerance. This approach allows Kafka to handle large volumes of data while maintaining low latency and high throughput.

      kafka vs Active MQ


      3. Performance

      Performance is another critical factor to consider when choosing a messaging system. RabbitMQ, Kafka, and ActiveMQ all have different performance characteristics.

      RabbitMQ is designed to be a reliable messaging system, which means that it prioritizes message delivery over performance.

      RabbitMQ can handle moderate message rates and is suitable for applications that require strict ordering and reliable delivery of messages.

      Kafka, on the other hand, is designed for high-performance and can handle large volumes of data with low latency. Kafka achieves this performance by using a distributed architecture and optimizing for sequential I/O.

      ActiveMQ is also designed for high-performance and can handle high message rates. ActiveMQ achieves this performance by using an asynchronous architecture and optimizing for message batching.

      Here is a chart from confluent which compares performance of Apache Kafka, Pulsar and RabbitMQ

      Active MQ vs Rabbit MQ

      Benchmarking Apache Kafka, Apache Pulsar, and RabbitMQ: Which is the Fastest?


      4. Data Persistence

      Data persistence is an important feature of messaging systems, as it allows messages to be stored and retrieved even if the messaging system goes down. RabbitMQ, Kafka, and ActiveMQ all have different approaches to data persistence.

      RabbitMQ stores messages on disk by default, which allows messages to be persisted even if the broker goes down.

      RabbitMQ also supports different storage backends, including in-memory storage, which provides better performance at the cost of data durability.

      Kafka stores messages on disk by default and uses a log-based architecture to achieve high durability and reliability. Kafka retains messages for a configurable period, which allows messages to be replayed if necessary.

      ActiveMQ also stores messages on disk by default and supports different storage backends, including JDBC and file-based storage. ActiveMQ can store messages in a database, which provides better data durability at the cost of performance.

      Here is a nice diagram from IBM that shows a Kafka architecture:

      Kafka vs RabbitMQ vs ActiveMQ

      image --- https://ibm-cloud-architecture.github.io/refarch-eda/technology/kafka-overview/


      5. Integration with Other Systems

      Integration with other systems is an important factor to consider when choosing a messaging system. RabbitMQ, Kafka, and ActiveMQ all have different integration capabilities.

      RabbitMQ integrates well with different programming languages, including Java, Python, Ruby, and .NET. RabbitMQ also has plugins that allow it to integrate with different systems, including databases, web servers, and message brokers.

      Kafka integrates well with different data processing systems, including Apache Spark, Apache Storm, and Apache Flink. Kafka also has a connector framework that allows it to integrate with different databases and data sources.

      ActiveMQ integrates well with different JMS clients, including Java, .NET, and C++. ActiveMQ also has plugins that allow it to integrate with different systems, including Apache Camel and Apache CXF.

      Here is also a nice table to highlight the difference between Kafka, RabbitMQ, and ActiveMQ

      Messaging Queue vs Message Broker


      System Design Interviews Resources:

      And, here are curated list of the 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. Codemia.io: This is another great platform to practice System design problems for interviews. It has more than 120+ System design problems, many of which are free and also a proper structure to solve them.

      3. Bugfree.ai: Bugfree.ai is a popular platform for technical interview preparation. The System Design sections and interview experience include a variety of questions to practice.

      4. Exponent: A specialized site for interview pre,p 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.

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

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

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

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

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

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

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

      how to prepare for system design

      image_credit - ByteByteGo

      Remember to combine theoretical knowledge with practical application by working on real-world projects and participating in mock interviews.

      Conclusion

      That's all about the difference between Apache Kafka, RabbitMQ, and ActiveMQ. RabbitMQ, Apache Kafka, and ActiveMQ are three popular messaging systems that have different features and capabilities.

      RabbitMQ and ActiveMQ follow a traditional messaging model, while Kafka uses a publish/subscribe messaging model.

      RabbitMQ and ActiveMQ use clustering and a network of brokers approach to achieve scalability, while Kafka uses partitioning. RabbitMQ prioritizes message delivery over performance, while Kafka and ActiveMQ prioritize performance. RabbitMQ, Kafka, and ActiveMQ all have different data persistence and integration capabilities.

      When choosing a messaging system, it is essential to consider the specific requirements of the application or system.

      RabbitMQ and ActiveMQ are suitable for applications that require strict ordering and reliable delivery of messages, while Kafka is suitable for streaming data scenarios.

      RabbitMQ and ActiveMQ are suitable for applications that require moderate to high message rates, while Kafka is suitable for applications that require high message rates.

      Similarly, RabbitMQ and ActiveMQ are suitable for applications that require high data durability, while Kafka is suitable for applications that require high performance.

        Top 10 Essential Tools for DevOps Engineers 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.

        essential tools for DevOps Engineers

        Hello friends, In the last few articles, I talked about System Design interview questions, and WHERE vs HAVING in SQL, and today, I will talk about essential DevOps tools that both developers and DevOps should know.

        In today's fast-paced software development landscape, DevOps practices and tools have become essential for efficient Software development and delivery.

        DevOps is all about breaking down the traditional silos between development and operations teams, fostering collaboration, and automating key processes.

        To achieve these goals, a wide array of DevOps tools and technologies has emerged, each addressing specific aspects of the software delivery lifecycle.

        In this article, I am going to share the top 10 DevOps tools that play an important role in the way organizations build, test, deploy, and monitor software.

        These tools span a range of categories, from version control and continuous integration to container orchestration and monitoring.

        Whether you're a DevOps Engineer or a Senior developer looking to expand your toolkit or an organization seeking to adopt DevOps practices, these tools can help streamline your software development and operations processes.

        10 Essential DevOps Tools You Can Learn in 2026

        Let's dive into the world of DevOps and discover the top 10 tools that can empower your team to achieve faster, more reliable software delivery.

        1. Git

        Git revolutionized version control, making it one of the foundational tools in DevOps. It allows developers to track changes in their codebase, collaborate seamlessly, and manage multiple code branches effectively.

        Git hosts like GitHub, GitLab, and Bitbucket have further enhanced their capabilities, providing a platform for distributed version control, code review, and project management.

        And if you want to learn Git, you can start with Git Complete: The definitive, step-by-step guide to Git, one of the most comprehensive courses on Udemy.

        best courses to learn Git

        If you need more choices, then you can also see these best Git online courses for beginners in 2026. It contains git courses and tutorials for both beginners and experienced DevOps engineers.


        2. Jenkins: Continuous Integration and Continuous Delivery (CI/CD)

        Jenkins is an open-source automation server that plays a crucial role in automating the CI/CD pipeline. It allows developers to build, test, and deploy code continuously, ensuring that changes are integrated smoothly and errors are detected early in the development process.

        With a vast library of plugins, Jenkins can be customized to suit the specific needs of your development environment.

        And if you want to learn Jenkins in depth then you can start with Jenkins, From Zero To Hero: Become a DevOps Jenkins Master course, its a nice course to learn Jenkins.

        best courses to learn Jenkins

        If you need more choices, then you can also see these best Jenkins courses for 2026.


        3. Docker: Containerization for Portability

        Docker has revolutionized how applications are packaged and deployed. With Docker containers, you can bundle your application and its dependencies into a single, lightweight unit that runs consistently across different environments.

        This portability and isolation make Docker a key tool for DevOps teams aiming to achieve consistency from development to production.

        And if you want to learn Docker in 2026, you can start with Docker and Kubernetes: The Complete Guide course, it's a nice course to learn Docker from scratch.

        best Docker courses in 2026

        If you need more choices, then you can also see these best Docker courses for beginners in 2026 to start with.


        4. Kubernetes

        Kubernetes has emerged as the de facto standard for container orchestration. It simplifies the management of containerized applications, automating tasks such as scaling, load balancing, and fault tolerance.

        Kubernetes provides the foundation for building resilient, Microservices-based applications, and it's a must-have tool for modern DevOps teams.

        And if you want to learn Kubernetes in depth, you can start with this beginner-level hands-on course Kubernetes for the Absolute Beginners --- Hands-on by Mumshad Mannambeth on Udemy.

        best online courses to learn Kubernetes

        If you need more choices, here are the best Kubernetes courses for DevOps Engineers to join in 2026.


        5. Ansible

        Ansible is a powerful open-source tool for automating configuration management and application deployment. It allows you to define infrastructure as code, making it easier to provision and manage servers and services.

        Ansible's simplicity and agentless architecture make it a favorite among DevOps professionals for automating repetitive tasks.

        And if you want to learn Ansible in depth, you can start with the Ansible for the Absolute Beginner — Hands-On — DevOps course by KodeCloud Training on Udemy. It's a nice hands-on course to learn Ansible.

        best Ansible courses for DevOps

        If you need more options, you can always check these best Ansible online courses in 2026. It contains Ansible courses for both beginner and intermediate DevOps engineers.


        6. Terraform

        Terraform is another key tool for infrastructure as code. It enables you to define and provision infrastructure resources across various cloud providers and on-premises environments.

        Terraform's declarative syntax and modular design make it a versatile choice for managing infrastructure at scale.

        And if you want to learn Terraform in depth, then you can start with the Terraform for the Absolute Beginners with Labs course by Kodecloud training.

        best online courses to learn Terraform

        If you need more choices, then you can also check out these best Terraform courses for 2026.


        7. Prometheus

        Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It can collect metrics from various sources, allowing you to gain insight into the health and performance of your applications and infrastructure.

        With its flexible query language and robust alerting capabilities, Prometheus empowers DevOps teams to proactively identify and address issues.

        If you need a course to learn Prometheus, you can start with Prometheus | The Complete Hands-On for Monitoring & Alerting course on Udemy. I took this course last month, its quite nice.

        best courses to learn Prometheus


        8. ELK Stack

        The ELK Stack, which consists of Elasticsearch, Logstash, and Kibana, provides a comprehensive solution for log management and analysis, particularly in the microservices world.

        It allows DevOps teams to collect, parse, store, and visualize log data from various sources.

        This stack is invaluable for troubleshooting, performance optimization, and security monitoring.

        And, if you want to learn more about the ELK stack, you can start with the Complete Elasticsearch Masterclass with Logstash and Kibana course from Udemy. It's a nice beginner-level course for the ELK stack.
        best courses to learn ELK stack

        And, if you need more choices, you can always take a look at these best ELK stack courses for Beginners in 2026


        9. Jenkins X

        Jenkins X is a Kubernetes-native CI/CD solution that brings automation and GitOps principles to the forefront. It simplifies the process of building, testing, and deploying cloud-native applications on Kubernetes clusters.

        Jenkins X streamlines the development workflow and promotes best practices for containerized applications.

        best courses to learn Jenkins


        10. Grafana

        Grafana is a popular open-source platform for data visualization and monitoring. It can integrate with various data sources, including Prometheus, to create dynamic dashboards and alerts.

        DevOps teams use Grafana to gain real-time insights into application and infrastructure performance, facilitating data-driven decision-making. If you want to learn Grafana in depth, you can start with the Grafana course on Udemy. It's a nice course to learn Grafana from scratch.

        best courses to learn Grafana

        If you need more choices, then you can also check out these best Grafana online courses in 2026.

        That's all about the 10 essential tools DevOps can learn in 2026. The DevOps landscape is continually evolving, and the tools mentioned above are just a snapshot of the vast ecosystem available to DevOps practitioners.

        Each tool plays a crucial role in different aspects of the software delivery pipeline, from version control and continuous integration to container orchestration and monitoring.

        The key to successful DevOps adoption is selecting the right tools that align with your organization's needs and goals.

        By embracing these DevOps tools, your organization can streamline its development and operations processes, reduce manual effort, improve collaboration, and deliver high-quality software at a faster pace. 

          Top 50 Easy, Medium, and Hard System Design Interview Questions for 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 friends, if you are preparing for Tech interviews, then you must prepare for System design questions because this is where most of the people struggle.

          Even experienced programmers struggle to solve common questions like how to design WhatsApp or YouTube, or answer the difference between API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy.

          In today's increasingly distributed world, the ability to architect robust and scalable systems is a fundamental skill sought after by top-tier tech companies.

          System design interviews have become a crucial component in evaluating a candidate's capacity to solve real-world challenges, assess trade-offs, and design systems that can handle complex requirements.

          In the past, I have also shared about Database Sharding, System design topics, Microservice Architecture, and System design algorithms, and today, I am going to share system design questions for interviews.

          In this article, I have 50+ system design interview questions carefully crafted to guide candidates from the foundational concepts to intricate design scenarios.

          Whether you're a beginner aiming to grasp the essentials or an experienced engineer seeking to refine your skills, these questions will not only prepare you for interviews but also improve your knowledge about system design and software architecture.

          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

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


          50 System Design Interview Questions for 2026

          Here is a list of 50 popular System design interview questions for beginners and experienced developers, which you can solve to start your preparation.

          In this list, I have not only shared easy, medium, and hard system design problems but also concept-based questions like API Gateway vs Load Balancer or Microservice vs Monolithic. You can practice these system design problems and questions for interviews.

          System Design Concept-based Questions

          1. What is the difference between API Gateway and Load Balancer? [solution]
          2. What is the difference between Reverse Proxy and Forward Proxy? (answer)
          3. What is the difference between Horizontal scaling and vertical scaling? (answer)
          4. What is difference between Microservices and Monolithic architecture? (Answer)
          5. What is difference between vertical and horizontal partitioning ?
          6. What is Rate Limiter? How does it work? (answer)
          7. How does Single Sign On (SSO) works? (answer)
          8. How does Apache Kafka works? why it so fast? (answer)
          9. Difference between Kafka, ActiveMQ, and RabbitMQ? (answer)
          10. Difference between JWT, OAuth, and SAML? (answer)

          Here is a nice diagram from DesignGuru.io which explains difference between vertical and horizontal database partition
          difference between horizontal and vertical partitioning


          𝐄𝐚𝐬𝐲 System Design Problems

          Now, let's jump into easy system design problems. These are common question where you need to design small utility which is used everywhere like URL shortner:

          1. How to Design URL Shortener like TinyURL [solution]
          2. How to Design Text Storage Service like Pastebin? [solution]
          3. Design Content Delivery Network (CDN) ? [solution]
          4. Design Parking Garage [solution]
          5. Design Vending Machine [solution]
          6. How to Design Distributed Key-Value Store
          7. Design Distributed Cache
          8. Design Distributed Job Scheduler
          9. How to Design Authentication System
          10. How to Design Unified Payments Interface (UPI)

          And, here is a high level design of YouTube from Educative.io for your reference:

          high level design of YouTube


          𝐌𝐞𝐝𝐢𝐮𝐦 System Design Problems

          Now, is the time to see medium difficulty of System design problems. These questions are neither easy nor very tough but you need good knowledge of various software architecture component and system design concepts to answer them.

          11. Design Instagram [solution]
          12. How to Design Tinder
          13. Design WhatsApp (solution)
          14. How to Design Facebook
          15. Design Twitter
          16. Design Reddit
          17. Design Netflix [solution]
          18. Design Youtube [solution]
          19. Design Google Search
          20. Design E-commerce Store like Amazon
          21. Design Spotify
          22. Design TikTok
          23. Design Shopify
          24. Design Airbnb
          25. Design Autocomplete for Search Engines
          26. Design Rate Limiter
          27. Design Distributed Message Queue like Kafka
          28. Design Flight Booking System
          29. Design Online Code Editor
          30. Design Stock Exchange System
          31. Design an Analytics Platform (Metrics & Logging)
          32. Design Notification Service
          33. Design Payment System

          And, here is a high level system design of Netflix from DesignGurus, one of my favorite place for learning system design

          Netflix architecture for system design


          𝐇𝐚𝐫𝐝 System Design Problems

          Now, let's see some hard questions which demand more effort from you. You may feel uncomfortable solving these questions but by doing this you become better.

          34. How to Design Location Based Service like Yelp
          35. Design Uber
          36. Design Food Delivery App like Doordash
          37. Design Google Docs
          38. How to Design Google Maps
          39. Design Zoom
          40. How to Design File Sharing System like Dropbox
          41. How to Design Ticket Booking System like BookMyShow
          42. Design Distributed Web Crawler
          43. How to Design Code Deployment System
          44. Design Distributed Cloud Storage like S3
          45. How to Design Distributed Locking Service

          Here is high level design of Google Map by Educative.io

          high level design of Google Map

          And, if you need solutions then they are available in this GitHub repository by @ Ashish Pratap Singh: https://github.com/ashishps1/awesome-system-design-resources/blob/main/README.md#system-design-interview-problems

          And, now see a few more resources for System design interview preparation


          Best System Design Interview Resources

          And, here are curated list of the 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. Codemia.io: This is another great platform to practice System design problems for interviews. It has more than 120+ System design problems, many of which are free, and also a proper structure to solve them.

          3. Bugfree.ai: This is another popular platform for technical interview preparation. It contains AI-based mock interviews as well as Interview experience and more than 3200+ real questions on System Design, Machine Learning, and other topics for practice =.

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

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

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

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

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

          9. YouTube Channels: Check out channels like "Gaurav Sen" (ex-Google engineer and founder of InterviewReddy.io and "Tech Dummies" for insightful videos on system design concepts and interview preparation.

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

          11. 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.

          how to prepare for system design

          image_credit - ByteByteGo

          Remember to combine theoretical knowledge with practical application by working on real-world projects and participating in mock interviews. Continuous practice and learning will undoubtedly enhance your proficiency in system design interviews.

          That's all about 50 System design interview questions for 2026. If you are preparing for technical interviews, then most likely you can solve these questions, but if you struggle, you can see the answer links, which go to free tutorials and YouTube videos, as well as the online courses and books I have shared.

          Whether you're a candidate preparing for a technical interview or a seasoned professional looking to refine your skills, mastering system design is a pivotal step in advancing your career in the ever-evolving tech industry, and these questions will help you.

          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