So, you want to become a Site Reliability Engineer (SRE) and get your SRE Foundation Certification? Well, you’ve come to the right place! In this blog article, we will explore the steps you need to take to achieve this prestigious certification. But before we dive into the details, let’s first understand what SRE is all about.

What is SRE Foundation Certification

The SRE Foundation Certification is a credential that demonstrates a person’s knowledge and understanding of the core concepts and practices of Site Reliability Engineering (SRE). SRE is a discipline that combines software engineering and IT operations to create scalable and highly reliable software systems. The certification is designed to validate a candidate’s familiarity with the principles, practices, and methodologies of SRE as well as their ability to apply these concepts to real-world scenarios. Achieving the certification indicates that an individual has a solid foundation in SRE and is capable of contributing to the implementation and maintenance of reliable and scalable systems.

The SRE Foundation Training Course by DevOpsSchool offers a practical and in-depth introduction to Site Reliability Engineering, empowering professionals to build and maintain highly reliable and scalable systems. Participants will cover essential SRE concepts, including service-level indicators (SLIs), service-level objectives (SLOs), error budgeting, automated monitoring, alerting, and proven incident management strategies. Designed for SREs, DevOps engineers, developers, system administrators, and IT operations leaders, this course combines hands-on labs, real-world scenarios, and expert-led instruction to equip you with effective techniques for minimizing downtime, optimizing system performance, and fostering resilience. Discover more and enroll here: SRE Foundation Training Course.

Here are some of the benefits of getting SRE Foundation Certification:

• Demonstrate your skills and knowledge to potential employers
• Advance your career in the field of SRE
• Gain access to exclusive resources and events
• Stay up-to-date on the latest SRE practices
• Build your network with other SRE professionals

Why SRE Certification is important?

Site Reliability Engineering (SRE) is a rapidly growing field, and there is a high demand for skilled SRE professionals. SRE Certification can help you demonstrate your skills and knowledge to potential employers, advance your career, and stay up-to-date on the latest SRE practices.

Here are some of the reasons why SRE Certification is important:

• It demonstrates your skills and knowledge to potential employers. When you are looking for a job in SRE, having a certification can give you a competitive edge over other candidates. It shows that you have a solid understanding of the principles and practices of SRE and that you are committed to continuous learning.
• It can help you advance your career in the field of SRE. As you gain experience in SRE, you may want to pursue more advanced certifications, such as the SRE Professional Certification. These certifications can help you to advance your career and take on more senior roles.
• It gives you access to exclusive resources and events. Many organizations that offer SRE certifications also offer exclusive resources and events to their certified members. This can include access to online courses, webinars, and conferences.
• It helps you stay up-to-date on the latest SRE practices. The field of SRE is constantly evolving, so it is important to stay up-to-date on the latest practices. SRE certifications can help you to do this by providing you with the knowledge and skills you need to be successful in the field.
• It helps you build your network with other SRE professionals. Attending SRE certification events is a great way to meet other SRE professionals and learn from their experiences. This can be invaluable in your career, as you will be able to network with people who can help you advance your career.

What are the tools needed to learn for a strong SRE Foundation?

Here are some of the tools that you need to learn for a strong SRE Foundation:

• Monitoring and alerting tools: These tools are used to collect and analyze data about your systems and applications so that you can identify and fix problems before they impact your users. Some popular monitoring and alerting tools include:
  • Prometheus
  • Grafana
  • ELK Stack

• Incident management tools: These tools are used to manage incidents when they do occur. They help you to track the incident, communicate with your team, and resolve the issue as quickly as possible. Some popular incident management tools include:
  • PagerDuty
  • VictorOps
  • Opsgenie

• Infrastructure automation tools: These tools are used to automate the provisioning, configuration, and deployment of your systems and applications. This can help you to save time and reduce errors. Some popular infrastructure automation tools include:
  • Terraform
  • Ansible
  • Chef

• Continuous integration and continuous delivery (CI/CD) tools: These tools are used to automate the build, test, and deployment of your applications. This can help you to release new features and fixes more quickly and reliably. Some popular CI/CD tools include:
  • Jenkins
  • Spinnaker
  • CircleCI

• Containerization and orchestration tools: These tools are used to package your applications into containers, which can then be deployed to any environment. This can help you to improve the portability and scalability of your applications. Some popular containerization and orchestration tools include:
  • Docker
  • Kubernetes
  • Nomad

How DevOpsSchool’s is best for SRE Foundation Certification?

DevOpsSchool is a great resource for learning about SRE and preparing for the SRE Foundation Certification exam. If you are serious about becoming an SRE, I highly recommend checking out DevOpsSchool.

Here are some additional benefits of using DevOpsSchool for SRE Foundation Certification:

• Flexible learning options: DevOpsSchool offers a variety of learning options, including live online courses, on-demand courses, and self-paced courses. This allows you to choose the option that best suits your learning style and schedule.
• Accredited training: DevOpsSchool’s SRE Foundation Certification training is accredited by the DevOps Institute. This means that you can be sure that you are receiving high-quality training that is aligned with the latest industry standards.
• Money-back guarantee: DevOpsSchool offers a money-back guarantee on its SRE Foundation Certification training. This means that you can try the training out risk-free. If you are not satisfied with the training, you can get a full refund.

Vue.js (pronounced “view.js”) is a progressive JavaScript framework used for building user interfaces (UIs) and single-page applications (SPAs). It is designed to be flexible, approachable, and efficient, making it a popular choice among developers for creating interactive and dynamic web applications.

Here are some of the benefits of using Vue.js:

• Easy to learn: Vue.js is a relatively easy framework to learn, even for beginners. The documentation is clear and concise, and there are many tutorials and resources available online.
• Fast: Vue.js is a fast framework that can be used to create responsive and interactive user interfaces.
• Scalable: Vue.js is a scalable framework that can be used to build large and complex applications.
• Flexible: Vue.js is a flexible framework that can be used to build a variety of different types of user interfaces.
• Community: There is a large and active community of Vue.js developers who are always willing to help.

What are the top use cases of Vue.js?

Vue.js is a popular JavaScript framework for building user interfaces. It is used by a wide variety of companies, including Google, Netflix, and Alibaba. Here are some of the top use cases of Vue.js:

• Building single-page applications (SPAs): SPAs are web applications that load all of their content in a single page. This makes them more responsive and interactive than traditional web applications. Vue.js is a popular choice for building SPAs because it is easy to learn and use, and it is very performant.
• Building progressive web applications (PWAs): PWAs are web applications that can be installed on a user’s device and used like a native app. Vue.js is a good choice for building PWAs because it is easy to make Vue.js applications work offline and because it can be used to create hybrid apps that combine the features of web apps and native apps.
• Building custom user interfaces: Vue.js can be used to build custom user interfaces for a variety of applications, such as dashboards, e-commerce websites, and marketing websites. Vue.js is a good choice for building custom user interfaces because it is flexible and can be customized to meet the specific needs of an application.
• Building data-driven applications: Vue.js is a good choice for building data-driven applications because it is reactive and can automatically update the UI when the data changes. This makes it easy to build applications that are responsive and interactive.
• Building mobile applications: Vue.js can be used to build mobile applications using a variety of frameworks, such as Nuxt.js and Quasar. Vue.js is a good choice for building mobile applications because it is easy to learn and use, and it is very performant.

What are the features of Vue.js?

Vue.js is a JavaScript framework that is used to build user interfaces. It is a progressive framework, which means that it can be used incrementally. You can start with a simple component and then add more features as needed.

Vue.js has many features, but some of the most important ones include:

• Declarative: Vue.js uses a declarative programming style, which means that you describe what you want to do, not how to do it. This makes it easier to write code that is both readable and maintainable.
• Reactive: Vue.js is reactive, which means that it automatically updates the UI when the data changes. This makes it easy to build user interfaces that are responsive and interactive.
• Composition: Vue.js is based on the principle of composition, which means that you can build complex components by composing simpler components. This makes it easy to create reusable and maintainable code.
• Virtual DOM: Vue.js uses a virtual DOM, which means that it creates a copy of the DOM in memory and then updates the copy instead of the real DOM. This makes Vue.js very performant.
• Routing: Vue.js has a built-in routing system that makes it easy to navigate between different pages in your application.
• State management: Vue.js has a built-in state management system that makes it easy to manage the state of your application.
• Internationalization: Vue.js has built-in support for internationalization, which makes it easy to create applications that can be used by people all over the world.

What is the workflow of Vue.js?

The workflow of Vue.js can be divided into the following steps:

1. Create a new project: The first step is to create a new project. You can do this using the Vue CLI, which is a command-line tool that can be used to create and manage Vue.js projects.
2. Install the dependencies: The next step is to install the dependencies for your project. You can do this using the npm package manager.
3. Create a component: The next step is to create a component. A component is a reusable piece of code that can be used to build your user interface.
4. Register the component: Once you have created a component, you need to register it with Vue.js. This can be done by adding the component to the components property in your Vue instance.
5. Use the component: Once you have registered the component, you can use it in your templates. To do this, you need to use the tag.
6. Bind data to the component: You can bind data to the component by using the v-bind directive. The v-bind directive tells Vue.js to bind the data to the corresponding property in the component.
7. Listen for events: You can listen for events from the component by using the von directive. The von directive tells Vue.js to listen to the event and execute the corresponding function when the event is triggered.
8. Compile the code: Once you have finished writing your code, you need to compile it. This can be done using the Vue CLI.
9. Run the application: Once the code has been compiled, you can run the application. You can do this by using the npm run serve command.

How Vue.js Works & Architecture?

Vue.js is a progressive JavaScript framework for building user interfaces. It is a declarative framework, which means that you describe what you want to do, not how to do it. This makes it easier to write code that is both readable and maintainable.

Vue.js is based on the following principles:

• Declarative: Vue.js uses a declarative programming style, which means that you describe what you want to do, not how to do it. This makes it easier to write code that is both readable and maintainable.
• Reactive: Vue.js is reactive, which means that it automatically updates the UI when the data changes. This makes it easy to build user interfaces that are responsive and interactive.
• Composition: Vue.js is based on the principle of composition, which means that you can build complex components by composing simpler components. This makes it easy to create reusable and maintainable code.

How to Install and Configure Vue.js?

To install and configure Vue.js, you can follow these steps:

1. Installation:
You have a few options for installing Vue.js, but using Vue CLI is the most recommended and straightforward method.

Using Vue CLI (Command Line Interface):
Vue CLI is a command-line tool that helps you scaffold and manage Vue.js projects.

• First, make sure you have Node.js and npm (Node Package Manager) installed on your system. You can download them from https://nodejs.org/.
• Open your terminal or command prompt.
• Install Vue CLI globally by running the following command:

npm install -g @vue/cli

• After the installation is complete, you can verify it by running:

vue --version

2. Create a New Vue Project:

• Navigate to the directory where you want to create your Vue project using the terminal.
• Create a new Vue project by running:

vue create project-name

• Follow the prompts to select configuration options for your project. You can choose default settings or manually configure features like additional tools, CSS preprocessing, and more.

3. Navigate to Project Directory:

• After the project is created, navigate to the project directory:

cd project-name

4. Run the Development Server:

• Start the development server to preview your Vue application in a web browser:

npm run serve

• This command compiles and serves your project. You’ll see a URL (usually http://localhost:8080/) in the terminal where you can access your app.

5. Configuration (Optional):

• Vue CLI provides a vue.config.js file in the project root for advanced configuration.
• This file can be used to modify various aspects of the build process, webpack configuration, and more.

6. Build for Production:

• When you’re ready to deploy your Vue.js application, you can build it for production using:

npm run build

• This command generates optimized and minified files in the dist directory that you can deploy to a web server.

Remember that Vue CLI handles most of the configuration for you, allowing you to focus on building your application. However, if you need to customize certain aspects of your project, the vue.config.js file is your go-to place.

PowerShell is a command-line shell and scripting language developed by Microsoft for managing and automating tasks in Windows operating systems. It provides a powerful and flexible environment for system administrators, developers, and IT professionals to interact with and manage Windows systems, services, and applications.

What are the top use cases of PowerShell?

Here are some of the top use cases of PowerShell:

• System administration: PowerShell is a powerful tool for system administrators. It can be used to automate a wide variety of tasks, such as managing users and groups, creating and managing files and folders, installing and uninstalling software, managing Windows services, and troubleshooting system problems.
• DevOps: PowerShell is also a popular tool for DevOps engineers. It can be used to automate the deployment and configuration of applications, as well as to manage infrastructure resources.
• Security: PowerShell can be used to automate security tasks, such as managing permissions, auditing user activity, and detecting and responding to security incidents.
• IT operations: PowerShell can be used to automate IT operations tasks, such as monitoring systems, managing backups, and provisioning resources.
• Development: PowerShell can be used to develop custom applications and scripts. It can also be used to automate tasks in other programming languages, such as C# and Java.

What are the features of PowerShell?

PowerShell is a task automation and configuration management framework from Microsoft, consisting of a command-line shell and scripting language. PowerShell is designed to help system administrators and power users automate the administration of Windows systems.

Here are some of the features of PowerShell:

• Powerful command-line shell: PowerShell provides a powerful command-line shell that can be used to interact with Windows systems. The command-line shell allows you to run commands, manage files and folders, and view system information.
• Scripting language: PowerShell is a scripting language that can be used to automate tasks. PowerShell scripts can be used to perform complex tasks that would be difficult or time-consuming to perform manually.
• Object-oriented: PowerShell is an object-oriented scripting language. This means that you can work with objects in PowerShell, such as files, folders, and processes.
• Built-in cmdlets: PowerShell comes with a number of built-in cmdlets that can be used to perform common tasks. These cmdlets can be used to manage users and groups, create and manage files and folders, install and uninstall software, and manage Windows services.
• Extensibility: PowerShell is extensible, which means that you can create your own cmdlets and modules. This allows you to automate tasks that are not supported by the built-in cmdlets.
• Cross-platform: PowerShell is cross-platform, which means that it can be used on Windows, macOS, and Linux. This makes it a valuable tool for system administrators who manage systems across multiple platforms.

What is the workflow of PowerShell?

The workflow of PowerShell refers to the sequence of steps that users follow to interact with the PowerShell environment, execute commands, and achieve their tasks.

The workflow of PowerShell is typically as follows:

• Start PowerShell: You can start PowerShell by typing Powershell in the Start menu or Run dialog box.
• Enter commands: You can enter commands at the PowerShell prompt. For example, to list all the files in the current directory, you would type ls.
• Use cmdlets: PowerShell comes with a number of built-in cmdlets that can be used to perform common tasks. For example, to create a new folder, you would use the New-Folder cmdlet.
• Create scripts: You can create PowerShell scripts to automate tasks. PowerShell scripts are text files that contain PowerShell commands.
• Run scripts: You can run PowerShell scripts by typing .\scriptname.ps1 at the PowerShell prompt.
• Get help: If you need help with a PowerShell command or cmdlet, you can type get-help at the PowerShell prompt.

How PowerShell Works & Architecture?

PowerShell is a task automation and configuration management framework from Microsoft, consisting of a command-line shell and scripting language. PowerShell is designed to help system administrators and power users automate the administration of Windows systems. PowerShell works by using a pipeline architecture. The pipeline is a way of passing data from one command to another. This allows you to chain together commands to perform complex tasks. For example, you could use the following commands to list all the files in the current directory that are larger than 1MB:

ls | Where-Object {$_.Length -gt 1MB}

The first command, ls, lists all the files in the current directory. The second command, Where-Object, filters the results of the first command to only include files that are larger than 1MB. The output of the second command is then displayed on the console. PowerShell also uses objects to represent data. This allows you to work with data in a more structured way. For example, the ls command returns a list of objects that represent files. Each object has properties, such as the file name, the file size, and the file creation date. You can use these properties to filter the results of the command or to perform other tasks.

PowerShell is a powerful tool that can be used to automate a wide variety of tasks on Windows systems. If you are a system administrator or power user, I recommend learning PowerShell.

Here are some additional details about the architecture of PowerShell:

• Pipeline: The pipeline is a way of passing data from one command to another. This allows you to chain together commands to perform complex tasks.
• Objects: PowerShell uses objects to represent data. This allows you to work with data in a more structured way.
• Cmdlets: PowerShell comes with a number of built-in cmdlets that can be used to perform common tasks.
• Modules: Modules are collections of cmdlets that are grouped together. Modules can be used to extend the functionality of PowerShell.
• Scripting language: PowerShell is a scripting language that can be used to automate tasks. PowerShell scripts can be used to perform complex tasks that would be difficult or time-consuming to perform manually.

How to Install and Configure PowerShell?

PowerShell is pre-installed on most modern versions of Windows. However, if you’re looking to install a specific version or PowerShell Core on other platforms, here’s how you can do it:

Installing PowerShell on Windows:

1. Windows PowerShell (Pre-installed):

Most recent versions of Windows come with Windows PowerShell pre-installed. You can access it by searching for “PowerShell” in the Start menu.

2. Windows PowerShell 7 (Upgrade):

Windows PowerShell 7 is the latest version and offers enhanced features. You can download and install it from the official PowerShell GitHub repository:

• Visit the PowerShell GitHub releases page: https://github.com/PowerShell/PowerShell/releases
• Download the installer for your platform (usually an MSI file for Windows).
• Run the installer and follow the on-screen instructions.

Installing PowerShell Core (Cross-Platform):

PowerShell Core is a cross-platform version of PowerShell that works on Windows, macOS, and Linux.

Download and Install PowerShell Core:

• Visit the PowerShell GitHub releases page: https://github.com/PowerShell/PowerShell/releases
• Download the installer for your platform (e.g., Windows, macOS, or Linux).
• Run the installer and follow the installation instructions.

Configuring PowerShell:

After installing PowerShell, you can configure it to suit your preferences and needs.

1. Execution Policy:

PowerShell has an execution policy that determines the level of security for script execution. You can set the execution policy using the following command in an elevated (Run as Administrator) PowerShell session:

Set-ExecutionPolicy

Replace with one of the available policies: Restricted, AllSigned, RemoteSigned, Unrestricted.

2. Profile Scripts:

PowerShell allows you to customize the startup behavior by using profile scripts. There are different profile scripts for different purposes:

• Current User Profile: $PROFILE is the path to your personal profile script.
• All Users Profile: $PROFILE.AllUsersCurrentHost is the path to the profile script that affects all users.
• Edit these files with a text editor to add customizations or aliases that you want to load at startup.

3. Modules and Extensions:

PowerShell can be extended using modules and extensions. You can install additional modules using the Install-Module cmdlet. For example:

Install-Module <ModuleName>

4. PowerShell ISE (Integrated Scripting Environment):

If you’re using Windows PowerShell, you can access the Integrated Scripting Environment (ISE) for a graphical scripting experience. Search for “PowerShell ISE” in the Start menu.

Flutter is an open-source, cross-platform UI toolkit developed by Google. It is used to build native mobile, web, and desktop applications from a single codebase. Flutter is a powerful and versatile toolkit that can be used to build a variety of applications, including:

• Web applications: Flutter can be used to build web applications that are fast, responsive, and beautiful.
• Mobile applications: Flutter can be used to build mobile applications for Android and iOS.
• Desktop applications: Flutter can be used to build desktop applications for Windows, macOS, and Linux.
• Embedded applications: Flutter can be used to build embedded applications for devices such as IoT devices and smart TVs.

What is the top use cases of Flutter?

Flutter, a UI toolkit developed by Google, is used to build natively compiled applications for mobile, web, and desktop from a single codebase. Its fast development cycle, expressive UI, and cross-platform capabilities make it suitable for a range of use cases. Here are some of the top use cases of Flutter:

1. Mobile Apps:
Flutter is primarily known for building high-quality, performant mobile applications for both Android and iOS platforms. It provides a consistent look and feel across devices and allows developers to create complex UIs with ease.

2. Cross-Platform Apps:
Flutter’s “write once, run anywhere” philosophy enables developers to build applications that work on multiple platforms using a single codebase. This reduces development time and effort compared to maintaining separate codebases for each platform.

3. Startup MVPs:
Flutter is a popular choice for startups looking to quickly build a Minimum Viable Product (MVP) for their app. Its fast development cycle allows for rapid iteration and testing of ideas.

4.UI-Intensive Apps:
Flutter’s customizable widgets and expressive UI framework make it suitable for apps that require complex and visually appealing user interfaces, such as e-commerce, social media, and multimedia apps.

5.Custom Apps for Enterprises:
Enterprises can use Flutter to build custom applications for internal use, such as project management tools, employee portals, and communication platforms.

6. Games and Interactive Apps:
Flutter’s graphics capabilities and animation framework make it viable for building simple games, interactive educational apps, and interactive experiences.

7. E-Commerce Apps:
Flutter’s rich set of widgets and flexible design system allows developers to create feature-rich e-commerce apps with appealing product displays, shopping carts, and payment integrations.

8. News and Content Apps:
Flutter is suitable for building news, blogging, and content consumption apps with dynamic layouts, animations, and integration of multimedia content.

9. Travel and Booking Apps:
Flutter can be used to build travel booking and planning apps with interactive maps, itinerary creation, and integration of third-party services.

10. Health and Fitness Apps:
Flutter’s ability to access device sensors and its integration with Bluetooth and health-related APIs makes it useful for building health and fitness tracking applications.

What are feature of Flutter?

Flutter is a popular choice for building cross-platform applications because it has many features that make it a powerful and versatile toolkit. Here are some of the key features of Flutter:

• Hot reload: Flutter supports hot reload, which means that you can make changes to your code and see the changes reflected in your app without having to rebuild the app. This makes Flutter a great choice for rapid development.
• Declarative UI: Flutter uses a declarative UI, which means that you describe what your UI should look like, rather than how it should be implemented. This makes Flutter’s UI code more concise and easier to understand.
• Custom widgets: Flutter provides a rich set of widgets, but you can also create your own custom widgets. This makes Flutter very flexible and allows you to create custom UIs that meet your specific needs.
• Flutter community: The Flutter community is large and active, and there are many resources available to help you learn Flutter and build great apps.

What is the workflow of Flutter?

The workflow of Flutter is as follows:

• Create a project: The first step is to create a project. This can be done using the flutter create command.
• Write your code: Once you have created a project, you can start writing your code. Flutter code is written in the Dart programming language.
• Build your app: Once you have written your code, you can build your app. This can be done using the flutter build command.
• Run your app: Once you have built your app, you can run it. This can be done using the flutter run command.
• Deploy your app: Once you are happy with your app, you can deploy it to the App Store or Google Play.

How Flutter Works & Architecture?

Flutter is an open-source, cross-platform UI toolkit developed by Google. It is used to build native mobile, web, and desktop applications from a single codebase. Flutter works by first compiling your Dart code into machine code for the specific platform that you are targeting. This makes Flutter apps fast and efficient. Flutter then uses a custom rendering engine to render the UI of your app. The rendering engine is written in C++, which makes it very fast and efficient.

Flutter also supports hot reload, which means that you can make changes to your code and see the changes reflected in your app without having to rebuild or restart your app. This makes Flutter a great choice for rapid development.

The architecture of Flutter is divided into three layers:

• The engine layer: The engine layer is responsible for rendering the UI of your app. It is written in C++ and is platform-independent.
• The framework layer: The framework layer provides the widgets that you use to build your UI. It is written in Dart and is platform-specific.
• The app layer: The app layer is where you write your Dart code. It is platform-specific.

How to Install and Configure Flutter?

To install and configure Flutter, you can follow these steps:

1. Install the Flutter SDK: You can download the Flutter SDK from the Flutter website.
2. Configure your environment variables: You need to configure your environment variables to point to the Flutter SDK.
3. Create a new Flutter project: You can create a new Flutter project using the Flutter create command.
4. Open your Flutter project in your IDE: You can open your Flutter project in your IDE of choice.
5. Run your Flutter project: You can run your Flutter project using the flutter run command.

Here are the detailed steps for each step:

1. Install the Flutter SDK: You can download the Flutter SDK from the Flutter website. The Flutter website has a variety of installers available for different operating systems. You can download the installer for your operating system from the website.

2. Configure your environment variables: You need to configure your environment variables to point to the Flutter SDK. You can do this by adding the following lines to your .bashrc or .zshrc file:

export PATH="$PATH:$HOME/flutter/bin"
export FLUTTER_SDK="$HOME/flutter"

3. Create a new Flutter project: You can create a new Flutter project using the flutter create command. This command will create a new project called my_app.

flutter create my_app

4. Open your Flutter project in your IDE: You can open your Flutter project in your IDE of choice. If you are using Visual Studio Code, you can use the Flutter extension to open your Flutter project.

5. Run your Flutter project: You can run your Flutter project using the flutter run command. This command will run your Flutter project on the device that you are currently connected to.

Laravel is a free and open-source PHP web framework, created by Taylor Otwell and intended for the development of web applications following the model–view–controller (MVC) architectural pattern and based on Symfony.

Some of the features of Laravel are:

• A modular packaging system with a dedicated dependency manager, different ways for accessing relational databases, utilities that aid in application deployment and maintenance, and its orientation toward syntactic sugar.
• A powerful command-line interface (CLI) called Artisan, which can be used to generate new code, migrate databases, and perform other tasks.
• A well-documented API that makes it easy to learn and use.
• A large and active community of developers who can provide support and help with problems.

What is top use cases of Laravel?

Laravel is a popular PHP framework that can be used to develop a wide variety of web applications. Here are some of the top use cases of Laravel:

• E-commerce websites: Laravel is a great choice for developing e-commerce websites because it is scalable and secure. Laravel also provides a number of features that are specifically designed for e-commerce websites, such as the ability to create product catalogs, manage orders, and process payments.
• Content management systems (CMS): Laravel can be used to develop custom CMSs or to extend existing CMSs. Laravel’s MVC architecture makes it easy to create a modular and extensible CMS.
• Social media applications: Laravel is a good choice for developing social media applications because it is scalable and efficient. Laravel also provides a number of features that are specifically designed for social media applications, such as the ability to handle large amounts of data and to scale to meet demand.
• APIs: Laravel can be used to develop APIs for a variety of purposes, such as connecting different systems, providing data to third-party applications, or creating a public-facing API for your application. Laravel’s API features make it easy to create secure and scalable APIs.
• Enterprise applications: Laravel is a good choice for developing enterprise applications because it is scalable, secure, and well-documented. Laravel’s features make it easy to create complex applications that meet the needs of large organizations.

What are the features of Laravel?

Laravel is a popular PHP framework that offers a wide range of features that make it a great choice for developing web applications.

Here are some of the most important features of Laravel:

• MVC architecture: Laravel follows the Model-View-Controller (MVC) architectural pattern, which separates the application into three layers: the model, the view, and the controller. This makes the application code more organized and easier to maintain.
• Routing: Laravel provides a powerful routing system that makes it easy to define and map URLs to controller actions. This makes it easy to create complex applications with a large number of URLs.
• Eloquent ORM: Laravel includes an Eloquent ORM that makes it easy to interact with databases. The Eloquent ORM provides a fluent interface that makes it easy to create, read, update, and delete data from databases.
• Artisan CLI: Laravel includes a powerful command-line interface (CLI) called Artisan that can be used to generate new code, migrate databases, and perform other tasks. The Artisan CLI makes it easy to automate tasks and speed up development.
• Security: Laravel includes a number of features that make it a secure framework, such as input validation, password hashing, and CSRF protection. These features help to protect your application from common security vulnerabilities.
• Testing: Laravel includes a built-in testing framework that makes it easy to test your application. The testing framework provides a number of tools and features that help you to write and run tests quickly and easily.
• Documentation: Laravel has excellent documentation that makes it easy to learn and use the framework. The documentation is well-written and comprehensive, and it includes examples and tutorials that can help you get started.
• Community: Laravel has a large and active community of developers who are always willing to help with problems and answer questions. This community is a great resource for learning Laravel and getting help when you need it.

What is the workflow of Laravel?

The workflow of Laravel is a process that is used to develop web applications using the Laravel framework. The workflow is divided into the following steps:

• Routing: The first step is to define the routes for your application. Routes are used to map URLs to controller actions. This is done using the Laravel routing system.
• Controllers: Controllers are responsible for handling requests from the user. They are responsible for calling the appropriate model methods and rendering the appropriate view.
• Models: Models are responsible for interacting with the database. They provide a layer of abstraction between the application and the database.
• Views: Views are responsible for displaying the data to the user. They are typically rendered by the controller.
• Testing: Testing is an essential part of the Laravel workflow. Laravel includes a built-in testing framework that makes it easy to write and run tests.

How Laravel Works & Architecture?

Laravel is a popular PHP framework that follows the Model-View-Controller (MVC) architectural pattern. The MVC pattern divides the application into three layers:

• The Model layer: The model layer is responsible for interacting with the database. It provides a layer of abstraction between the application and the database.
• The View layer: The view layer is responsible for displaying the data to the user. It is typically written in Blade, which is a templating language that is designed for Laravel.
• The Controller layer: The controller layer is responsible for handling requests from the user. It is responsible for calling the appropriate model methods and rendering the appropriate view.

How to Install and Configure Laravel?

Here are the steps on how to install and configure Laravel:

1. Install Composer. Composer is a dependency manager for PHP. You can download Composer from the Composer website.

2. Install Laravel. You can install Laravel using Composer. To do this, run the following command in your terminal:

composer create-project --prefer-dist laravel/laravel my-app

This will create a new Laravel application called my app.

3. Configure the application. You need to configure the application before you can start using it. The configuration file is located at config/app.php. In this file, you can set the application name, the database connection details, and other settings.

4. Generate a key. Laravel uses a key to encrypt sensitive data. You can generate a key using the following command:

php artisan key:generate

5. Start the development server. Laravel includes a built-in development server that you can use to test your application. To start the development server, run the following command:
php artisan serve

Node.js is an open-source, cross-platform runtime environment that runs on the V8 JavaScript engine. It is designed to build scalable network applications. Node.js is event-driven and non-blocking, which makes it efficient at handling concurrent requests. Node.js is a popular choice for building real-time web applications, such as chat applications and streaming applications. It is also used for building server-side applications, such as web APIs and microservices.

Here are some of the key features of Node.js:

• Event-driven: Node.js is event-driven, which means that it only executes code when an event occurs. This makes it efficient at handling concurrent requests.
• Non-blocking: Node.js is non-blocking, which means that it does not block the main thread when it is waiting for an event to occur. This makes it efficient at handling concurrent requests.
• Cross-platform: Node.js is cross-platform, which means that it can run on Windows, macOS, and Linux.
• Asynchronous: Node.js is asynchronous, which means that it can handle multiple requests at the same time.
• Scalable: Node.js is scalable, which means that it can be easily scaled to handle more requests.

What are the top use cases of Node.js?

Node.js is a popular runtime environment that is used for a wide variety of applications.

Here are some of the top use cases of Node.js:

• Real-time web applications: Node.js is a popular choice for building real-time web applications, such as chat applications and streaming applications. This is because Node.js is event-driven and non-blocking, which makes it efficient at handling concurrent requests.
• Server-side applications: Node.js is also used for building server-side applications, such as web APIs and microservices. This is because Node.js is asynchronous, which means that it can handle multiple requests at the same time.
• IoT applications: Node.js is used for building IoT applications, as it can be used to handle the high volume of data that is generated by IoT devices.
• Content management systems: Node.js is used for building content management systems (CMSs), such as WordPress and Drupal. This is because Node.js is scalable and can handle a large number of users.
• E-commerce: Node.js is used for building e-commerce websites, as it can handle the high volume of traffic that is generated by e-commerce websites.
• Logistics: Node.js is used for building logistics applications, such as tracking shipments and managing inventory levels. This is because Node.js is scalable and can handle a large number of transactions.
• Financial: Node.js is used for building financial applications, such as trading platforms and risk management systems. This is because Node.js is scalable and can handle a large number of transactions.
• Healthcare: Node.js is used for building healthcare applications, such as electronic medical records (EMRs) and patient portals. This is because Node.js is scalable and can handle a large number of users.

What are the features of Node.js?

Node.js is a popular runtime environment that is used for a wide variety of applications.

Here are some of the key features of Node.js:

• Event-driven: Node.js is event-driven, which means that it only executes code when an event occurs. This makes it efficient at handling concurrent requests.
• Non-blocking: Node.js is non-blocking, which means that it does not block the main thread when it is waiting for an event to occur. This makes it efficient at handling concurrent requests.
• Cross-platform: Node.js is cross-platform, which means that it can run on Windows, macOS, and Linux.
• Asynchronous: Node.js is asynchronous, which means that it can handle multiple requests at the same time.
• Scalable: Node.js is scalable, which means that it can be easily scaled to handle more requests.
• JavaScript: Node.js is based on JavaScript, which is a popular programming language that is easy to learn and use.
• Ecosystem: Node.js has a large and active ecosystem, which means that there are a wide variety of modules and libraries available for Node.js.

What is the workflow of Node.js?

Here are some additional details about each step:

• Creating a project: The first step is to create a project. This can be done by creating a new directory and initializing it with the npm init command. The npm init command will prompt you to enter a few basic details about your project, such as the name of the project and the version number.
• Installing dependencies: The next step is to install the dependencies for your project. This can be done by running the npm install command. The npm install command will install the dependencies that are listed in the package.json file.
• Writing code: The next step is to write the code for your project. This code will be executed by the Node.js runtime environment. The Node.js runtime environment is a JavaScript engine that is designed to execute JavaScript code on the server.
• Running the code: The code can be run by executing the node command. The node command will execute the JavaScript code that is contained in the file.
• Testing the code: The code can be tested by using a variety of testing frameworks, such as Mocha and Chai. Testing frameworks are used to automate the testing of code.
• Deploying the code: The code can be deployed to a production environment by using a variety of deployment methods, such as Heroku and Amazon Web Services (AWS). Deployment is the process of making the code available to users.

How Node.js Works & Architecture?

Node.js is an open-source, cross-platform runtime environment that runs on the V8 JavaScript engine. It is designed to build scalable network applications. Node.js is event-driven and non-blocking, which makes it efficient at handling concurrent requests. Node.js works by using a single-threaded event loop to handle all of the requests that are made to the server. The event loop is responsible for calling the appropriate callbacks when events occur. Events can be triggered by a variety of things, such as HTTP requests, file system changes, and timer events.

The Node.js architecture is divided into two main parts: the event loop and the modules. The event loop is responsible for handling all of the requests that are made to the server. The modules are responsible for providing functionality to the Node.js runtime environment. The event loop is a critical part of the Node.js architecture. It is responsible for making sure that all of the requests that are made to the server are handled in a timely manner. The event loop is also responsible for making sure that the Node.js runtime environment is not blocked by anyone’s request.

The modules are a key part of the Node.js architecture. They provide functionality to the Node.js runtime environment, such as HTTP request handling, file system access, and database access. The modules are written in JavaScript and can be installed using the npm package manager.

Node.js is a powerful and versatile platform that is used for a wide variety of applications. It is a good choice for applications that require scalability, efficiency, and versatility.

Here are some of the key features of the Node.js architecture:

• Single-threaded event loop: The Node.js event loop is a single-threaded event loop. This means that only one request can be processed at a time. However, the event loop is non-blocking, which means that it can handle multiple requests in parallel.
• Modules: Node.js modules are a key part of the architecture. They provide functionality to the Node.js runtime environment, such as HTTP request handling, file system access, and database access.
• JavaScript: Node.js is based on JavaScript. This makes it easy to learn and use, and there is a large and active community of JavaScript developers.

How to Install and Configure Node.js?

Here are the steps on how to install and configure Node.js:

• Download the Node.js installer. You can download the Node.js installer from the official Node.js website.
• Install the Node.js installer. The installation process will vary depending on your operating system.
• Configure the Node.js environment variables. You will need to configure the Node.js environment variables so that you can run Node.js applications from the command line.
• Verify the installation. You can verify the installation by running the node -v command.

C++ is a general-purpose programming language that was developed as an extension of the C programming language. It was created to add object-oriented programming (OOP) features while retaining the performance and efficiency of C. C++ was first introduced in the late 1970s and has since become one of the most widely used programming languages in various domains, including systems programming, game development, embedded systems, scientific computing, and more.

Here are some of the benefits of using C++:

• Performance: C++ is a compiled language, which means that it is typically faster than interpreted languages.
• Control: C++ gives programmers a high level of control over system resources.
• Portability: C++ code can be compiled for a variety of platforms.
• Community: There is a large and active community of C++ programmers.

What are the top use cases of C++?

C++ is a general-purpose programming language, but it is especially well-suited for certain types of applications.

Here are some of the top use cases of C++:

• Operating systems: C++ is a popular language for developing operating systems, such as Windows, macOS, and Linux. This is because C++ gives programmers a high level of control over system resources, which is essential for developing efficient and reliable operating systems.
• Compilers: C++ is also a popular language for developing compilers, which are programs that translate code from one programming language to another. This is because C++ is a powerful language that can be used to write efficient compilers.
• Video games: C++ is a popular language for developing video games, as it can be used to create high-performance games that are responsive and engaging. This is because C++ gives programmers a high level of control over the hardware, which is essential for developing high-performance games.
• Embedded systems: C++ is also a popular language for developing embedded systems, which are small, specialized computer systems that are used in a variety of devices, such as cars, medical devices, and industrial machinery. This is because C++ is a powerful and efficient language that can be used to write code that is tightly integrated with the hardware.
• Scientific computing: C++ is also a popular language for scientific computing, as it can be used to write code that can be used to solve complex mathematical problems. This is because C++ is a powerful and efficient language that can be used to write code that can be easily parallelized.

What are the features of C++?

C++ is a general-purpose programming language that is powerful and versatile. It has several features that make it a good choice for a wide variety of applications.

Here are some of the key features of C++:

• Static typing: C++ is a statically typed language, which means that the types of variables and expressions are known at compile time. This can help to prevent errors and improve the performance of programs.
• Object-oriented programming: C++ supports object-oriented programming, which is a programming paradigm that models real-world objects as data structures and their operations. This can make programs more modular and easier to maintain.
• Generic programming: C++ supports generic programming, which is a programming paradigm that allows programmers to write code that can be used with different types of data. This can make programs more reusable and easier to maintain.
• Functional programming: C++ supports functional programming, which is a programming paradigm that emphasizes the use of functions and avoids side effects. This can make programs more predictable and easier to reason about.
• Low-level control: C++ gives programmers a high level of control over system resources, such as memory and CPU time. This can be useful for developing high-performance applications.
• Portability: C++ code can be compiled for a variety of platforms, including Windows, macOS, Linux, and embedded systems.
• Performance: C++ is a compiled language, which means that it is typically faster than interpreted languages.
• Community: There is a large and active community of C++ programmers.

What is the workflow of C++?

The workflow of C++ is as follows:

• Write the code: The first step is to write the C++ code. This can be done in a text editor or a specialized IDE.
• Compile the code: Once the code is written, it needs to be compiled. This is the process of converting the code into machine code that can be executed by the computer.
• Link the code: The compiled code then needs to be linked. This is the process of combining the compiled code with other libraries and objects.
• Run the code: Once the code is linked, it can be run. This is the process of executing the code and producing output.
• Debug the code: If the code does not run correctly, it will need to be debugged. This is the process of finding and fixing errors in the code.
• Repeat: The process of writing, compiling, linking, running, and debugging the code may need to be repeated until the code is correct and produces the desired output.

How C++ Works & Architecture?

C++ is a compiled language, which means that it is converted into machine code before it is executed. This makes C++ programs faster than interpreted languages, such as Python or JavaScript.

The C++ workflow is as follows:

• The programmer writes the code in a text editor or IDE.
• The code is then compiled by a compiler, which converts it into machine code.
• The machine code is then linked with other libraries and objects to create an executable file.
• The executable file is then run by the computer.

How to Install and Configure C++?

There are two ways to install and configure C++:

1. Download the C++ compiler and libraries and install them on your computer. This is the traditional way to install C++. You can download the C++ compiler and libraries from the official C++ website. Once you have downloaded the compiler and libraries, you need to install them on your computer.
2. Use a development environment (IDE) that includes a C++ compiler and libraries. This is a more convenient way to install and configure C++. There are a number of IDEs that include a C++ compiler and libraries. Some popular IDEs for C++ include Visual Studio, Code::Blocks, and Eclipse.

Once you have installed C++, you need to configure it. This involves setting the compiler options and the path to the C++ libraries. You can configure C++ by editing the c_cpp_properties.json file in your project.

Here are the steps on how to install and configure C++ using Visual Studio:

1. Download and install Visual Studio.
2. Create a new C++ project.
3. In the project properties, select the “C++/C” tab.
4. In the “Configuration Properties” section, set the “Compiler” option to “Visual Studio Compiler for C++”.
5. In the “Linker” section, set the “Additional Library Directories” option to the path to the C++ libraries.

Once you have installed and configured C++, you can start writing C++ code.

Here are some of the benefits of using a development environment (IDE) to install and configure C++:

• IDEs make it easy to write, compile, and run C++ code.
• IDEs provide a number of features that can help you to debug your code.
• IDEs can help you to learn C++ by providing tutorials and documentation.

JavaScript is a programming language that is one of the core technologies of the World Wide Web, alongside HTML and CSS. It is used to create interactive web pages and web applications. JavaScript is a client-side scripting language, which means that it is executed by the user’s web browser. This makes it a powerful tool for creating dynamic and interactive web pages.

JavaScript is a versatile language that can be used for a variety of tasks, including:

• Adding interactivity to web pages: JavaScript can add interactive elements to web pages, such as dropdown menus, modal dialogs, and sliders.
• Validating user input: JavaScript can be used to validate user input, such as email addresses and passwords.
• Creating animations: JavaScript can be used to create animations, such as spinning logos and bouncing balls.
• Building web applications: JavaScript can be used to build web applications like online forms and shopping carts.
• JavaScript is a powerful language that can be used to create a wide variety of interactive web pages and web applications. If you are interested in web development, JavaScript is a language that you should learn.

Here are some examples of how JavaScript is used in web pages:

• When you click on a button, JavaScript code is executed to change the page’s content.
• When you hover your mouse over an image, JavaScript code is executed to change the image’s color.
• When you scroll down a web page, JavaScript code loads new content.

What are the top use cases of JavaScript

Here are some of the top use cases of JavaScript:

• Web development: JavaScript is one of the core technologies of the World Wide Web, and it is used to create interactive web pages and web applications.
• Mobile development: JavaScript can also be used to develop mobile applications, using frameworks like React Native and Ionic.
• Game development: JavaScript is a popular language for game development, and it is used to create a wide variety of games, from simple 2D games to complex 3D games.
• Data visualization: JavaScript can be used to create interactive data visualizations, such as charts and graphs.
• Server-side scripting: JavaScript can also be used for server-side scripting, using frameworks like Node.js.
• Art and animation: JavaScript can be used to create interactive art and animations.

What is the feature of JavaScript

Here are some of the most important features of JavaScript:

• Dynamic typing: JavaScript is a dynamically typed language, which means that the type of a variable is not declared explicitly. This makes JavaScript very flexible, but it can also lead to errors if the wrong type of data is used.
• Object-oriented programming: JavaScript supports object-oriented programming (OOP), which allows developers to create reusable and self-contained objects. OOP is a powerful feature that can make JavaScript code more readable and maintainable.
• Functional programming: JavaScript also supports functional programming, which is a programming paradigm that emphasizes the use of functions. Functional programming can be used to write code that is more concise and easier to understand.
• Event handling: JavaScript provides a rich event handling system that allows developers to respond to events such as mouse clicks, keyboard presses, and form submissions. Event handling is a powerful feature that can be used to create interactive web pages and web applications.
• DOM manipulation: JavaScript can be used to manipulate the Document Object Model (DOM), which is the underlying structure of a web page. This allows developers to change the content of a web page dynamically, which can be used to create interactive and engaging experiences.
• Asynchronous programming: JavaScript supports asynchronous programming, which allows developers to run code in the background without blocking the main thread. This is a powerful feature that can be used to improve the performance of web applications.
• Libraries and frameworks: There are many JavaScript libraries and frameworks available, which provide developers with a wide range of features and functionality. Some popular libraries and frameworks include jQuery, React, and AngularJS.

What is the workflow of JavaScript?

The workflow of JavaScript can vary depending on the specific project. However, the steps outlined above are a general overview of the process.

Here are some additional tips for following the workflow of JavaScript:

• Use a debugger: A debugger is a tool that can help you find and fix errors in your JavaScript code. There are many different debuggers available, such as the JavaScript debugger in Chrome and Firefox.
• Write unit tests: Unit tests are small pieces of code that test specific parts of your JavaScript code. Unit tests can help you identify errors in your code early on before they cause problems for users.
• Use a framework: A framework is a set of pre-written code that can help you speed up the development of your JavaScript code. There are many different frameworks available, such as React, AngularJS, and Vue.js.

How JavaScript Works & Architecture?

JavaScript is a client-side scripting language that is executed by the user’s web browser. This means that JavaScript code is not sent to the server, but is instead executed on the user’s computer.

JavaScript works by first being parsed by the browser’s JavaScript engine. The JavaScript engine is responsible for converting the JavaScript code into machine code that can be executed by the computer. Once the JavaScript code has been parsed, it is executed by the browser’s JavaScript engine.

The architecture of JavaScript is relatively simple. It consists of the following components:

• The JavaScript engine: The JavaScript engine is responsible for parsing and executing JavaScript code.
• The Document Object Model (DOM): The DOM is the underlying structure of a web page. JavaScript can be used to manipulate the DOM, which allows developers to change the content of a web page dynamically.
• The event handling system: JavaScript provides a rich event handling system that allows developers to respond to events such as mouse clicks, keyboard presses, and form submissions.

How to Install and Configure JavaScript?

JavaScript is a client-side scripting language that is installed by default in most web browsers. However, if you want to use JavaScript in a development environment, you will need to install a JavaScript engine.

Here are the steps on how to install and configure JavaScript:

1. Install a JavaScript engine. You can follow the instructions on the engine’s website.

• Node.js: Node.js is a JavaScript runtime environment that can be used to run JavaScript code outside of a web browser. It is a popular choice for developing web applications and server-side applications.
• Mozilla Firefox: Firefox includes a JavaScript engine called SpiderMonkey.
• Google Chrome: Chrome includes a JavaScript engine called V8.
• Microsoft Edge: Edge includes a JavaScript engine called Chakra.

2. Specify the location of the JavaScript engine in your web browser. This can be done in the browser’s settings.

• For example, in Chrome, you can open the Settings menu and go to Advanced > JavaScript.
• In Firefox, you can open the Options menu and go to Advanced > General > Languages and Fonts.
• In Edge, you can open the Settings menu and go to Preferences > Languages.

3. Start writing JavaScript code. You can use a text editor or an IDE to write JavaScript code.

• A text editor is a simple text-based editor that you can use to write JavaScript code.
• An IDE (Integrated Development Environment) is a more powerful tool that can help you write, debug, and test JavaScript code.

4. Save your JavaScript code. You can save your JavaScript code as a .js file.
5. Test your JavaScript code. You can test your JavaScript code in a web browser.

• To test your JavaScript code in Chrome, you can open the Developer Tools (Ctrl+Shift+I) and click on the Console tab.
• To test your JavaScript code in Firefox, you can open the Web Developer Tools (Ctrl+Shift+I) and click on the Console tab.
• To test your JavaScript code in Edge, you can open the Developer Tools (Ctrl+Shift+I) and click on the Console tab.

The latest report on the global Continuous Delivery market by In4Research provides a detailed study of the Industry with several important aspects such as current market scenario, estimates, top investment pockets, recent trends, changing dynamics, and vibrant forces of the industry from 2019 to 2026 to identify the underlying opportunities. The report gives much focus on the prospects that may transpire soon and provide a significant boost to the overall market growth.

The report also covers the drivers that are playing a substantial role in fueling the Continuous Delivery market growth. At the same time, restraining factors that are expected to obstruct or hold the growth of the industry are also presented by our expert analysts to provide the key market players with a detailed scenario of the future threats in advance.

Revenue and Sales Estimation — Historical Revenue and sales volume is presented, and further data is triangulated with top-down and bottom-up approaches to forecast complete market size and to estimate forecast numbers for key regions covered in the report along with classified and well-recognized Types and end-use industry.

Continuous Delivery Market Segmentation

By Type,

On-premises
Cloud
By Application,

Banking, Financial Services, and Insurance (BFSI)
Telecommunications
Media and entertainment
Retail and eCommerce
Healthcare
Manufacturing
Education
Others (government, transport and logistics, and energy and utilities)
This study will provide a comprehensive outlook of the Continuous Delivery Industry It is vital you keep your market knowledge up to date segmented by Type, Applications, and major players. Also, classify different companies according to your targeted objective or geography and provide customization according to requirements.

Major Players in the Global Continuous Delivery market are

Moreover, Porter’s five forces analytical pattern exhibits the assertiveness of the market by assessing several parameters including threats of alternatives, the strength of the suppliers, and choice of the buyers operating in the industry.

Major Players Covered in this report are:

Atlassian
IBM
Xebialabs
CA Technologies
Electric Cloud
Puppet Enterprise
Chef Software
Cloudbees
Microsoft
Flexagon
Micro Focus
Clarive
Key Region Covered in Continuous Delivery Market are

North America (USA, Canada, and Mexico)
Europe (Germany, France, the United Kingdom, Netherlands, Russia, Italy, and Rest of Europe)
Asia-Pacific (China, Japan, Australia, New Zealand, South Korea, India, and Southeast Asia)
South America (Brazil, Argentina, Colombia, rest of countries, etc.)
Middle East and Africa (Saudi Arabia, United Arab Emirates, Israel, Egypt, Nigeria, and South Africa)
Highlights of the Report:

A detailed and exhaustive evaluation of the Continuous Delivery market.
Accrued revenues from each segment of the market from 2019 to 2026.
Drivers, restraints, and opportunities in the industry.
Approaches embraced by the key market players.
Provinces that would create multiple opportunities for the frontrunners in the industry.
Current scope and trends of the Continuous Delivery market.

Major Pointers in Table of Contents of Continuous Delivery Market Report are as follows:

Part 01: Executive Summary

Part 02: Scope of The Report

Part 03: Research Methodology

Part 04: Global Continuous Delivery Market Landscape
• Market Entropy
• Market segmentation analysis
• Market characteristics

Part 06: Global Continuous Delivery Market Sizing
• Market definition
• Market size and forecast
• Market sizing

Part 07: Global Continuous Delivery Market Segmentation
• Segmentation
• Market opportunity
• Comparison

Part 08: Customer Landscape
Part 10: Decision Framework
Part 09: Regional Landscape

In4Research

In4Research is a provider of world-class market research reports, customized solutions and consulting services, and high-quality market intelligence that firmly believes in empowering the success of its client’s successes in growing or improving their business. We combine a distinctive package of research reports and consulting services, global reach, and in-depth expertise in markets such as Chemicals and Materials, Food and Beverage, Energy, and Power that cannot be matched by our competitors. Our focus is on providing knowledge and solutions throughout the entire value chain of the industries we serve. We believe in providing premium high-quality insights at an affordable cost.

With its announcement of quantum computer Jiuzhang, China puts itself on the global map of quantum technology. As, China and the USA lead the global race in quantum technology, other developed nations are slowly following this pursuit by investing significant intellectual and fiscal resources. Recently, in collaboration with AWS, the Ministry of Electronics and Information Technology (MeitY) announced to establish a Quantum Computing Applications Lab in India.

This will be the world’s first quantum computing applications lab on AWS, supporting a national government’s mission to drive innovation. MeitY assures that this initiative will also accelerate quantum computing-led research and development and enable new scientific discoveries, by inviting applications from researchers and work with subject matter experts. The lab will then provide select applicants with access to quantum computing hardware, simulators, and programming tools, on-demand and at no cost, under the Amazon Braket program.

Amazon Braket program provides a development environment where users can explore and design quantum algorithms, test and troubleshoot them in quantum computers simulations, and run them on different quantum hardware technologies.

What’s unique about Quantum Computers?
Today’s classical computers manipulate individual bits that store information as binary 0 and 1 states. But quantum computers leverage quantum mechanical phenomena to drive information via qubits. Apart from binary values of ‘0’ and ‘1’, qubits have a third state called superposition, which simultaneously represents a ‘0’ or a ‘1’. Instead of analyzing a one or a zero sequentially, superposition allows two qubits in superposition to represent multiple scenarios at the same time. Additionally, qubits are also capable of pairing, which is known as entanglement. Here, the state of one qubit cannot be described independently of the state of the others – thus allowing instantaneous communication.

India’s Strategic Initiatives so Far
It is well-known that quantum computing can propel the development of new breakthroughs in science and lay the foundation for high computational capabilities. Apart from the USA and China, nations like Japan, Germany, Singapore and Canada have all announced ambitious schemes to boost quantum computing programs. Even the European Commission isn’t far behind. In the last budget session, the Indian Government had announced to invest US$1.12 billion in quantum computing research over the next five years. Finance minister, Nirmala Sitharaman, mentioned that this scheme would boost the research for technology under the National Mission of Quantum Technology and Application project. In the year before, the Department of Science & Technology (DST) had set up a research project named Quantum-Enabled Science & Technology (QuEST) with allocation of US$11.2 million (₹80 crores) in funding, at International Institute of Information Technology (IIIT)-Hyderabad.

As per official sources, the MeitY Quantum Computing Applications Lab will offer Quantum Computing-as-a-Service to government ministries and departments, researchers, scientists, academia, and developers to enable advances in areas like manufacturing, healthcare, agriculture, and aerospace engineering. As per the agreement, MeitY says this new lab will take inputs from Central and State governments, Research Institutions and Academia to identify problem statements in quantum computing. Nonetheless, it is still unclear whether this initiative falls under the National Mission on Quantum Technologies and Applications. But according to the President and CEO of the National e-Governance Division (NeGD), Abhishek Singh, this initiative will certainly augment India’s ongoing efforts in developing quantum computing applications.

Meanwhile, MeitY’s Secretary, Ajay Sawhney, is confident that quantum computing will play a key role in scientific advancements and learning. He said, “India has a rich legacy in science, technology, and innovation. We believe that India’s path forward will be driven by achieving world-class scientific solutions”.

It’s not the first time that an Indian institute will play a key role in boosting quantum computing development. For instance, Indian Institute of Science Education and research, Mohali, Punjab has done exemplary work on Photonic quantum memory. Even the study on Optimization of Circuits for qubit Quantum Computers by Jaypee Institute of Information Technology, Noida is also encouraging. Bengaluru-based QuNu Labs is India’s first company working in quantum cryptography.

“Our collaboration with MeitY to establish the Quantum Computing Applications Lab will provide the breadth and depth of AWS services to the research, academic, and scientific community to overcome these challenges and focus on innovation,” says Rahul Sharma, President, Public Sector – AISPL, AWS India and South Asia.

New Jersey, United States,- The report, titled “Cloud Microservices Market Size By Types, Applications, Segmentation, and Growth – Global Analysis and Forecast to 2019-2027” first introduced the fundamentals of Cloud Microservices: definitions, classifications, applications and market overview; Product specifications; Production method; Cost Structures, Raw Materials, etc. The report takes into account the impact of the novel COVID-19 pandemic on the Cloud Microservices market and also provides an assessment of the market definition as well as the identification of the top key manufacturers which are analyzed in-depth as opposed to the competitive landscape. In terms of Price, Sales, Capacity, Import, Export, Cloud Microservices Market Size, Consumption, Gross, Gross Margin, Sales, and Market Share. Quantitative analysis of the Cloud Microservices industry from 2019 to 2027 by region, type, application, and consumption rating by region.

Impact of COVID-19 on Cloud Microservices Market: The Coronavirus Recession is an economic recession that will hit the global economy in 2020 due to the COVID-19 pandemic. The pandemic could affect three main aspects of the global economy: manufacturing, supply chain, business and financial markets. The report offers a full version of the Cloud Microservices Market, outlining the impact of COVID-19 and the changes expected on the future prospects of the industry, taking into account political, economic, social, and technological parameters.

In market segmentation by manufacturers, the report covers the following companies-

Amazon Web Services
Pivotal Software
Infosys
Oracle Corporation
Idexcel
Microsoft
CA Technologies
IBM
Salesforce.com
Tata Consultancy
Services Limited
How to overcome obstacles for the septennial 2020-2027 using the Global Cloud Microservices market report?

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Kubernetes as Enterprise Infrastructure Backbone

Over the past several years, Kubernetes has matured from an experimental container orchestrator into the de facto foundation of modern enterprise infrastructure. It underpins major hybrid cloud platforms and has become one of the most widely adopted infrastructure technologies in large organisations. Despite this momentum, two structural challenges continue to slow enterprise adoption and frustrate day-to-day operations:

• A lack of clear separation between the developer experience and the operator experience.
• A lack of application context — the platform’s inability to treat a collection of related resources as a single logical application.

Challenge 1: The Blurred Line Between Dev and Ops

Kubernetes was architected to solve some of the hardest problems in scalable infrastructure. Built on containers, it delivers excellent workload scalability and reliability. However, its design philosophy is rooted in the concerns of system administrators and infrastructure operators — those who manage compute, network, and storage resources.

In traditional IaaS and PaaS environments, there is a reasonably clean boundary between developers and operators. Developers own the application lifecycle — they package, deploy, and iterate on code. Operators own the stability of the underlying platform. Each role has distinct responsibilities, tooling, and success metrics.

Kubernetes collapses that boundary. Regardless of whether a person’s title is Developer or Platform Engineer, anyone working with Kubernetes must develop a comparable depth of understanding of cluster concepts: packaging workloads, configuring resource requests and limits, defining health probes, managing scaling policies, and more. This uniform expectation creates real friction:

• Developers want to focus on rapid feature iteration and bug fixes. Being required to deeply understand Kubernetes primitives slows them down and increases cognitive load.
• Operators are measured on platform stability. Sharing ownership of deployment decisions with developers introduces risk and blurs accountability.

Enterprise Kubernetes customers are asking vendors and the open-source community for a better-defined developer experience — one that abstracts the infrastructure layer while still exposing the capabilities developers genuinely need, such as environment configuration, scaling hints, and observability hooks. Projects like the Open Application Model (OAM) and platforms like Knative and various internal developer platforms (IDPs) have emerged partly to address this need, but no single solution has become a universal standard.

Challenge 2: Kubernetes Lacks Application Context

When you deploy even a modest application on Kubernetes, the platform decomposes it into a collection of independent objects. Consider a basic web application backed by a database. A typical deployment might involve all of the following Kubernetes resources:

• Deployment — runs the frontend web server pods.
• StatefulSet — manages a highly available database instance.
• ConfigMap — holds configuration settings for the database server.
• Secret — stores credentials used by the application to connect to the database.
• PersistentVolume and PersistentVolumeClaim — provide durable storage for the database.
• Service — exposes the database to the frontend within the cluster.
• Ingress — exposes the web frontend to external traffic.

That is seven or more distinct objects for a simple two-tier application. Each object is managed independently by Kubernetes. The platform has no native concept of the application as a whole — it does not model the relationship between these objects or understand that they collectively represent a single unit of business logic.

Why Application Context Matters

Without application context, common lifecycle operations become unnecessarily complex:

• Health assessment: Determining whether the application is healthy requires checking multiple independent objects rather than querying a single entity.
• Deletion: Removing an application means tracking down and deleting each constituent resource individually, or relying on labels applied consistently at deploy time.
• Upgrades and rollbacks: Coordinating a change that spans a Deployment, a ConfigMap, and a Secret requires manual orchestration or tooling layered on top of Kubernetes.
• Auditing and cost attribution: Attributing resource consumption to a specific application requires consistent label discipline across all related objects — a discipline that is easy to let slip.

What the Ecosystem Has Done (and Not Done)

Helm, the most widely used Kubernetes package manager, partially addresses this problem at install time. A Helm chart groups related resources into a named release, making it possible to install or delete an application as a single operation. However, once a release is deployed, Kubernetes itself still treats each resource independently. If resources are modified outside of Helm — through kubectl apply, for example — the application context stored in the Helm release can diverge from the actual cluster state.

GitOps tools such as Argo CD and Flux address some of this gap by continuously reconciling desired state (stored in Git) with cluster state, and by surfacing application-level health. Custom Resource Definitions (CRDs) and Operators allow platform teams to define higher-level application abstractions that the Kubernetes API can then manage natively. These approaches move in the right direction, but they require significant investment and expertise to implement well.

What Customers Are Asking For

Enterprise teams adopting Kubernetes to run line-of-business applications are asking the ecosystem for several concrete capabilities:

1. A richer developer abstraction layer — tooling that lets developers define what their application needs (a web server, a cache, a database) without requiring them to author raw Kubernetes YAML for every primitive.
2. First-class application entities — a supported mechanism within or alongside Kubernetes to declare, package, and manage a unified application object, rather than a loosely coupled bag of resources.
3. Lifecycle management at the application level — the ability to deploy, update, roll back, inspect, and delete an application as an atomic unit.
4. Clear role boundaries — platform tooling that enforces sensible defaults and guardrails for developers while preserving operational control for platform engineers, without requiring every developer to become a Kubernetes expert.

The Bottom Line

Kubernetes has solved many of the hardest problems in distributed systems infrastructure. Its adoption across enterprises and cloud providers is a testament to that. But the platform’s infrastructure-centric design creates real usability gaps when it is used as the runtime for application teams who do not live and breathe cluster operations.

The next wave of progress in the Kubernetes ecosystem is less about raw platform capability and more about usability, abstraction, and application-level semantics. Teams evaluating Kubernetes platforms, internal developer platforms, and GitOps tooling should prioritise solutions that address these two pain points directly: clear developer/operator boundaries and genuine application-level lifecycle management.

The Big Picture: Cloud Growth in 2019

The competitive landscape of cloud computing did not change dramatically in 2019, but all three major players — Amazon Web Services (AWS), Microsoft Azure, and Google Cloud — continued to grow at a rapid pace, extending their dominance over smaller hosting and infrastructure providers.

In the quarter ending 30 September 2019:

• AWS reported 34% year-over-year revenue growth.
• Microsoft Azure reported 59% year-over-year revenue growth.
• Google reported that its “Other Revenues” segment — which includes Google Cloud — reached $6.4 billion, up 39% year-over-year, though it did not break out cloud revenue separately.

Market Share: Contested Numbers

AWS remains the largest IaaS (Infrastructure as a Service) provider by a significant margin, though the exact figures depend heavily on which analyst you consult and how they define “cloud.”

• Gartner (July 2019) estimated AWS at 47.8%, Azure at 15.5%, and Google at 4.0%.
• Canalys (October 2019) estimated AWS at 32.6%, Azure at 16.9%, and Google at 6.9%.

The wide variance between estimates reflects genuine methodological differences. IaaS is an inherently slippery term: a cloud-hosted virtual machine is clearly IaaS, but cloud vendors actively push customers toward premium managed services that blur the line between IaaS, PaaS (Platform as a Service), and SaaS (Software as a Service).

Microsoft’s numbers shift considerably if Office 365 is included, since the synergy between Office 365, Azure Active Directory, and on-premises infrastructure is a key driver of Azure adoption. AWS, by contrast, has very little SaaS presence. Neither Microsoft nor Google consistently breaks out IaaS-specific revenue, making direct comparisons difficult.

Most analysts agree that while AWS is still growing fast, its share of the overall market is gradually eroding as Azure and Google close the gap.

AWS in 2019: Outposts and the Hybrid Push

The most significant AWS development of 2019 was arguably AWS Outposts, first previewed at re:Invent 2018 and made generally available in 2019. Outposts brings AWS hardware, APIs, and services physically on-premises into a customer’s own data centre. The value proposition is clear: it addresses latency-sensitive workloads and regulatory requirements that prevent data from leaving a specific location, while preserving a consistent AWS operational experience.

The trade-off is cost — running your own servers through AWS Outposts is an expensive proposition compared to traditional on-premises infrastructure. Nevertheless, analyst firm Canalys estimated that AWS would become a top-four on-premises server vendor in the US and Western Europe within three years of the announcement.

Historically, hybrid cloud has been Microsoft’s territory, through products like Azure Stack and Azure AD Connect. Outposts signals that AWS is serious about competing in that space.

Google Cloud in 2019: Anthos and the Kubernetes Advantage

Google Cloud’s headline announcement of 2019 was Anthos, launched in April. Anthos is a hybrid and multi-cloud application platform built on Kubernetes (K8s) — the container orchestration system that Google originally developed internally before open-sourcing it as part of the Cloud Native Computing Foundation.

Anthos allows organisations to deploy and manage containerised applications consistently whether they run on-premises, on Google Cloud Platform (GCP), or on other public clouds such as AWS and Azure. This multi-cloud portability is a meaningful differentiator, particularly for enterprises wary of vendor lock-in.

Kubernetes is both Google Cloud’s biggest technical advantage and an awkward strategic topic for AWS. AWS supports K8s through Amazon EKS and claims to operate the largest Kubernetes footprint of any cloud provider, yet the ecosystem’s momentum and mindshare remain closely associated with Google. AWS CEO Andy Jassy addressed this tension directly, stating that he does not “believe in one tool to rule the world” — a clear reference to Kubernetes’ growing ubiquity.

Microsoft Azure in 2019: DevOps and GitHub

Microsoft completed its acquisition of GitHub in late 2018, but the platform’s integration with Azure deepened considerably through 2019. The most developer-relevant release was the general availability of GitHub Actions and GitHub Actions for Azure, which together allow teams to automate workflows from code commit through to deployment — including containerised applications, web apps, and serverless functions.

This tightly integrated CI/CD capability, combined with Azure DevOps (formerly VSTS), positions Microsoft strongly with development teams already inside the GitHub ecosystem. Analysts and practitioners noted that this tight coupling between developer tooling and cloud infrastructure is a meaningful on-ramp to broader Azure adoption.

Microsoft also benefited from a high-profile contract win: the US Department of Defence awarded the Joint Enterprise Defense Infrastructure (JEDI) contract to Microsoft rather than AWS — a decision that visibly frustrated Amazon and led to a legal challenge.

Conference Impressions: Cloud Next, Ignite, and re:Invent

Reporters attending all three flagship cloud events in 2019 noted distinct personality differences between the providers:

• Google Cloud Next (London) impressed with engineering depth and technical quality, but the platform still felt smaller in breadth compared to AWS and Azure.
• Microsoft Ignite demonstrated Microsoft’s strength in packaging complex cloud capabilities into approachable services, though the event also surfaced awkward moments around Azure’s handling of Intel speculative execution vulnerabilities.
• AWS re:Invent was the largest of the three by far, with Outposts as the headline announcement. AWS also showed visible frustration about both the Kubernetes debate and the JEDI contract loss to Microsoft.

Who Won 2019?

Picking a single winner is difficult. The original analysis from The Register leaned toward Microsoft as having the best 2019, largely due to the GitHub Actions integration, the JEDI win, and effective packaging of hybrid and DevOps capabilities. However, the same analysis rated AWS as more promising for 2020 given the Outposts rollout, and expected Google to continue gradually gaining market share on the back of Kubernetes and Anthos momentum.

The Cost Question

One recurring criticism of all three major providers is pricing. Commentators, including blogger Cal Peterson, noted what some have called the “Amazon premium” — the observation that AWS, Azure, and Google Cloud are significantly more expensive than smaller or specialised hosting providers for equivalent raw compute capacity.

PHP inventor Rasmus Lerdorf has been cited as having made direct VM-price comparisons across providers that highlighted this gap. The major cloud vendors justify higher prices through breadth of services, global infrastructure, compliance tooling, SLAs, and managed service availability — all of which matter in enterprise contexts. But for teams primarily buying raw compute and storage, the big three are rarely the cheapest option.

Looking Ahead

The 2019 landscape set clear trajectories. AWS was deepening its hybrid story through Outposts. Google was betting on Kubernetes and open-source credibility to attract cloud-native developers. Microsoft was leveraging its enterprise relationships and developer tooling — particularly through GitHub — to make Azure the natural choice for organisations already in the Microsoft ecosystem.

For DevOps engineers evaluating cloud strategy, the lesson from 2019 is that the “best” cloud depends heavily on workload type, existing tooling, compliance requirements, and budget. Market share numbers alone don’t tell the full story.

What Is Nutanix?

Nutanix is a San Jose, California-based cloud computing software company best known for pioneering hyper-converged infrastructure (HCI). The company sells HCI appliances and software-defined storage, and has increasingly positioned itself as a hybrid cloud platform vendor — enabling enterprises to run, move, and manage workloads across on-premises data centres and public clouds from a single management plane.

The Shifting Enterprise Technology Landscape

According to Anantharaman, a major shift is underway in how enterprises think about their IT systems. Organisations that were traditionally B2B and operated purely as systems of record are now building systems of engagement — technology layers designed to reach customers faster, improve acquisition, and deliver better digital experiences.

This shift is visible in the proliferation of SaaS applications being adopted by banks, financial institutions, and other large enterprises. The core question driving IT investment decisions has become: “What systems of engagement can I deploy to reach customers faster?”

How Cloud Computing Has Evolved in India

Anantharaman identifies two distinct phases of cloud adoption in India:

• Phase 1 – Peripheral applications: Enterprises moved workloads like HRMS, Learning Management Systems (LMS), customer experience platforms, and digital front-end applications to the cloud. These were relatively low-risk migrations.
• Phase 2 – Business-critical applications: Enterprises are now evaluating whether the next tier of applications — not just mission-critical systems like core banking, but business-critical workloads — are ready for cloud deployment. This workload evaluation is a defining challenge for enterprise IT teams today.

India’s startup ecosystem has also accelerated cloud adoption. Startups typically begin without owning any infrastructure, defaulting to public cloud from day one. As they scale, IT costs become a significant operational concern, which is where vendors like Nutanix offer an alternative to pure public cloud spend.

Nutanix’s Hybrid Cloud Strategy

Nutanix’s core value proposition centres on four capabilities:

1. Data centre modernisation: Collapsing traditional three-tier architecture (compute, storage, networking as separate silos) into a single unified hyper-converged fabric.
2. Single-pane management: Providing a unified management interface that reduces operational complexity and administrative overhead.
3. Cloud mobility: Enabling customers to move applications to public cloud providers when needed — and to move them back on-premises if cost, security, or performance requirements change.
4. Interoperability: Supporting workloads across multiple cloud environments, avoiding lock-in to any single public cloud provider.

This bidirectional flexibility — move to cloud, move from cloud — is positioned as a key differentiator. Anantharaman describes it as the company’s essential functionality: app mobility and cloud interoperability.

Key Products in the Nutanix Portfolio

Beyond core HCI, Nutanix has built a broader software portfolio addressing hybrid cloud management, security, and containerisation:

• Karbon: A Kubernetes management platform for building and running containerised applications on Nutanix infrastructure.
• Beam: A multi-cloud cost governance and compliance tool that gives customers visibility into public cloud spend versus on-premises costs, and across different public cloud providers.
• Flow: A network micro-segmentation product that provides application-level traffic control for virtual machines, adding a security layer without requiring additional physical network appliances.

Market Position and HCI Leadership in India

Anantharaman cited IDC data indicating Nutanix held a 47.4% share of the India HCI market at the time of the interview, maintaining market leadership for seven consecutive quarters. The company also highlights that it has made its software available across approximately 17 hardware platforms, allowing customers to run the Nutanix software stack on hardware from their preferred vendor rather than being tied to Nutanix-branded appliances.

This hardware-agnostic software model has contributed to Wall Street reclassifying Nutanix as a software and subscription company rather than a hardware appliance vendor — a strategic repositioning with significant implications for revenue predictability and gross margins.

Key Verticals and Customer Use Cases in India

Anantharaman identified the following sectors as primary targets:

• IT and ITES: The largest segment by cloud deployment volume in India.
• Banking and financial services: ICICI Bank was cited as a customer running its enterprise service bus — the integration layer between external-facing systems and internal architecture — on Nutanix.
• Government: Growing interest in cloud infrastructure among government agencies.
• Agriculture and enterprise: One of the world’s largest seed companies (unnamed) uses Nutanix for a hub-and-spoke model managing remote branch offices.
• System integrators: Nutanix built a self-service developer portal for one of India’s largest system integrators, enabling internal application launches without manual provisioning.

Hybrid Cloud Outlook for India

Nutanix’s 2018 Enterprise Cloud Index — an annual survey of enterprise cloud adoption trends — reported that hybrid cloud workload adoption in India was projected to more than triple, from 13% to 43%. The same report indicated that private cloud usage was expected to decline from 38% to 23% as enterprises migrated those workloads into hybrid cloud models.

Anantharaman summarised the broader market sentiment with a telling indicator: more CEOs in India are now actively asking their IT teams, “Are we cloud-like?” — signalling that cloud readiness has moved from a technical discussion to a board-level strategic priority.

Key Takeaways for DevOps and Infrastructure Teams

• Hybrid cloud is increasingly the default enterprise architecture, not a transitional state.
• Workload portability — the ability to move applications between on-premises and public cloud — is a critical requirement when evaluating infrastructure platforms.
• HCI reduces operational overhead by collapsing traditional three-tier data centre architecture into a single managed fabric.
• Cost governance tools like Nutanix Beam are becoming essential as multi-cloud environments make cloud spend harder to track and control.
• Micro-segmentation (Nutanix Flow) provides application-level network security for VM workloads without dedicated hardware.
• Container orchestration support (Nutanix Karbon) reflects the broader industry shift toward Kubernetes-based application deployment.

Original interview source: Financial Express. Interview conducted by Sudhir Chowdhary with Balakrishnan Anantharaman, VP & MD Sales – India & SAARC, Nutanix.

DevOps Has Never Stood Still

DevOps is now well over a decade old, and the concepts, practices, and strategies associated with the model have evolved substantially since practitioners first began using the term around 2009. When engineers talk about DevOps today, they often mean something quite different from what early adopters had in mind. Understanding that evolution helps explain both where DevOps is now and where it is likely to go.

Phase 1 — The Early Days: Uniting Dev and Ops (2009–2012)

In its earliest form, DevOps was deliberately simple. The message that emerged from the first DevOps conferences and blog posts was straightforward: software deployment would go more smoothly if developers—the people who write applications—and IT operations engineers—the people responsible for deploying and managing those applications—communicated better, understood each other’s constraints, and stopped assigning blame when things broke.

During this period, DevOps prescribed a goal rather than a method. Few specific tools or processes were attached to the label. Many of the tools that early DevOps advocates championed, such as continuous integration servers, had already been developed as part of the Agile movement, which predated DevOps by several years. Figuring out how to actually do DevOps was largely left to individual teams.

Phase 2 — Containers Give DevOps a Toolset (2013–2015)

The picture changed notably in 2013 when Docker containers became publicly available. Container technology itself was not new—the underlying concepts date back decades—but Docker arrived at a moment when DevOps practitioners were actively looking for something concrete to work with.

What Docker (and the container ecosystem that grew around it, including Kubernetes) provided was a portable application packaging format that worked consistently across development, staging, and production environments. This directly addressed one of the oldest friction points between developers and operations teams: the classic “it works on my machine” problem. By standardising the runtime environment, containers made collaboration between developers and infrastructure engineers meaningfully easier.

A few important caveats are worth stating clearly:

• Containers are not the only way to achieve DevOps goals. Teams can—and do—collaborate effectively without them.
• Containers have their own technical merit entirely separate from DevOps philosophy.
• Kubernetes in particular brought its own significant complexity, which created new operational challenges alongside the benefits.

Nevertheless, the mid-2010s saw containers become closely identified with DevOps practice, turning the movement from a philosophy into something with a recognisable, teachable toolset.

Phase 3 — “Everything Ops”: Expanding Beyond Dev and IT (2015–2018)

By the mid-2010s, another significant shift was underway. The DevOps conversation expanded beyond the two original stakeholders—developers and operations engineers—to include software testers, security professionals, compliance teams, and in some interpretations even marketing and business units.

This gave rise to a proliferation of compound terms:

• DevSecOps — embedding security practices throughout the delivery pipeline rather than treating them as a gate at the end.
• DevTestOps — integrating testing teams and automated quality assurance into the same continuous flow.
• DataOps — applying DevOps principles to data pipelines and analytics workflows.
• BizDevOps — aligning business stakeholders directly with delivery teams to reduce handoff delays.

By around 2016, analysts and vendors had identified more than a dozen such offshoots. Some practitioners called this broader trend “DevOps 2.0” or “everything Ops.” The core principle remained the same—reduce silos, improve collaboration, shorten feedback loops—but the scope had expanded well beyond a two-team conversation.

Phase 4 — Continuous Everything (2018–Present)

More recently, the word continuous has become attached to virtually every process in the software delivery lifecycle:

• Continuous Integration (CI) — merging code changes frequently and verifying them automatically.
• Continuous Delivery (CD) — keeping software in a deployable state at all times.
• Continuous Testing — running automated tests at every stage of the pipeline, not just before a release.
• Continuous Security — scanning for vulnerabilities at every pipeline stage rather than as a one-time audit.
• Continuous Monitoring — collecting telemetry and acting on it throughout the application lifecycle.

The word “continuous” is worth examining critically. Few of these processes are literally uninterrupted; they are triggered by events, run in parallel pipelines, and still take minutes or hours to complete. A more accurate description of the underlying goal might be: make every process comprehensive, automated, and predictable rather than manual, ad hoc, or reserved for specific milestones. When testing, security, and monitoring are embedded consistently at every stage, software quality and reliability improve—regardless of whether the cadence is strictly continuous.

Where Is DevOps Headed Next?

Several trends are already shaping what DevOps will mean in the years ahead.

AI and Machine Learning in the Pipeline

Machine learning is beginning to appear in DevOps tooling—for anomaly detection in monitoring, predictive scaling, automated incident triage, and intelligent test selection. The promise is that AI-assisted decision making can reduce the need for human intervention in routine pipeline steps, making delivery smoother. The tradeoff is that automated decisions can also propagate errors quickly, so teams must invest in appropriate guardrails and observability.

Cloud-Native as the Default Platform

Many core DevOps tools—CI servers, container runtimes, artifact registries—were designed to run on-premises or in the cloud interchangeably. That flexibility served the community well during a period of mixed infrastructure adoption. However, as cloud-native platforms, managed Kubernetes services, serverless architectures, and platform engineering practices mature, DevOps workflows are increasingly designed with cloud infrastructure as the assumed foundation rather than an optional deployment target.

Platform Engineering and Internal Developer Platforms

A newer evolution—gaining traction as of the early 2020s—is the formalisation of “platform engineering” as a discipline. Rather than each development team managing its own CI/CD pipelines and infrastructure abstractions, dedicated platform teams build and maintain Internal Developer Platforms (IDPs) that provide self-service capabilities to product engineers. This can be seen as DevOps maturing into a product rather than a practice: the “platform” is the product, and developer experience is its primary success metric.

What the Evolution of DevOps Tells Us

Looking back across the decade, a consistent pattern emerges:

1. DevOps started as a cultural idea with no prescribed tooling.
2. Tooling—primarily containers—gave the culture something concrete to work with.
3. The philosophy expanded outward to encompass more stakeholders and more disciplines.
4. Automation and “continuous” practices formalised what good DevOps process looks like in practice.
5. Cloud-native infrastructure, AI assistance, and platform engineering are driving the next round of change.

Each phase built on the last rather than replacing it. Teams that skipped the cultural foundation while adopting the tools often found the tools insufficient on their own. Teams that nailed the collaboration culture but never invested in automation hit scaling limits. The most effective DevOps organisations today have addressed both dimensions—and are already working on the next one.

Whatever the terminology evolves into next—DevOps 3.0, platform engineering, AIOps—the underlying goal has not changed: reduce friction in software delivery, shorten feedback loops, and give teams the confidence to ship reliable software quickly. The label matters less than the outcome.

2019 saw more data organizations running advanced analytics, AI, and machine learning workloads than ever before. Heading into 2020, the expectation was a spike in both the number of technologies and the data teams required to support those workloads internally. AI and analytics teams were predicted to merge into unified data organizations, tackling challenges such as hybrid cloud strategy, Kubernetes-based analytics, and the ongoing evolution of the Hadoop ecosystem.

1. The Rise of the Hybrid Cloud

After years of discussion, hybrid cloud was expected to move from concept to practical reality in 2020. Large enterprises were already refusing to expand on-premises Hadoop deployments, choosing instead to invest in public cloud capacity. However, most were unwilling to migrate core enterprise data off-premises entirely.

The predicted model: data stays on-prem, compute bursts to the cloud. This approach targets peak demand and unpredictable workloads, where elastic cloud compute is more cost-effective than maintaining excess on-premises capacity. Technologies enabling seamless data-local-compute-remote architectures were expected to lead the hybrid cloud wave.

2. One Machine Learning Framework to Rule Them All

By late 2019, operationalizing ML model training had become a priority across organizations of all sizes. Multiple frameworks competed for dominance, but no clear winner had emerged. The analogy drawn was to earlier data tooling consolidations: Apache Spark became the standard for data transformation, and Presto was emerging as the leading choice for interactive querying.

The prediction for 2020 was that the model training space would see similar consolidation, with PyTorch and TensorFlow identified as the leading contenders for the dominant framework position.

3. Kubernetifying the Analytics Stack

Containers and Kubernetes had proven highly effective for stateless workloads — web servers, microservices, self-contained databases — but adoption for advanced analytics and AI workloads lagged behind. The prediction was that 2020 would mark the shift toward AI and analytics workloads becoming mainstream on Kubernetes.

The core technical challenge: solving data sharing and elasticity. Moving data from remote silos into Kubernetes clusters improves data locality, which is critical for performance-sensitive analytics jobs. This required new patterns for stateful workload management that the community was actively developing.

4. HDFS Is Dead — But Hadoop Compute Lives On

The “Hadoop is dead” narrative was common heading into 2020, but the reality was more nuanced. The prediction separated two distinct components of the Hadoop ecosystem:

• HDFS (Hadoop Distributed File System): Predicted to decline due to operational complexity, high cost, and a fundamental inability to support elastic compute scaling. Data stored in HDFS would migrate to cloud object storage (such as S3 or GCS) or on-premises object storage alternatives.
• Hadoop compute frameworks (Spark, Presto): Predicted to remain strong. These tools had already been adopted well beyond the Hadoop ecosystem and were seen as foundational for large-scale data processing regardless of the underlying storage layer.

The key insight: compute and storage decoupling was accelerating, making storage-layer-agnostic compute frameworks more valuable, not less.

5. AI and Analytics Teams Merge Into a Unified Data Organization

The organizational structure of data teams was also expected to evolve. Hadoop platform teams had already transitioned into analytics and data engineering teams. The next step was absorbing AI and ML functions into the same organizational unit, reflecting how deeply intertwined structured data analytics and machine learning had become.

The convergence of statistical modeling and computer science into modern ML meant that data, analytics, and AI teams were often working on the same datasets with overlapping goals. The predicted outcome: organizations would build dedicated, unified data stack teams responsible for storage, compute, and modeling across on-prem and cloud environments.

6. The Talent Gap Will Constrain Data Technology Adoption

A persistent challenge identified for 2020 was the shortage of engineers who could bridge data engineering, cloud architecture, and AI/ML workloads. Building production data stacks that incorporate modern cloud, Kubernetes, and ML tooling requires a rare combination of skills.

The concern: as executive-level interest in data-driven strategies grew, the pipeline of engineers capable of architecting those systems was not keeping pace. Companies making ambitious “data-everywhere” commitments were expected to fall short without a parallel investment in developing or recruiting the necessary talent.

7. China’s Cloud Adoption Leapfrog

While U.S. enterprises had been migrating to public cloud infrastructure for several years, Chinese enterprises had largely invested in on-premises infrastructure for data-intensive workloads. The prediction for 2020 was that this would change dramatically — with Chinese enterprises leapfrogging incremental cloud adoption to deploy advanced workloads directly in the public cloud at significant scale.

Public cloud providers operating in China were expected to see growth rates potentially outpacing those of established Western cloud giants, driven by new use cases, burst compute for peak loads, and migration of existing on-prem workloads.

Key Takeaways for Data Engineering Teams

• Plan for hybrid architectures: Separating data residency from compute elasticity is a practical near-term strategy, not a distant goal.
• Watch the ML framework landscape: Framework consolidation simplifies toolchain decisions; betting on widely-adopted frameworks reduces long-term migration risk.
• Invest in Kubernetes expertise for analytics: Stateful analytics workloads on Kubernetes require different patterns than stateless services — build that knowledge proactively.
• Decouple storage from compute: Moving away from HDFS toward object storage unlocks the elasticity needed for modern cloud-native analytics.
• Build unified data teams: Siloed analytics and AI functions create duplication; unified teams sharing infrastructure and data assets are more efficient.
• Address the talent gap directly: Upskilling existing engineers in cloud-native and ML tooling is often faster than competing for a limited pool of specialists.

The trends outlined here represent the data engineering inflection points anticipated heading into 2020 — a year when advanced analytics and AI were expected to move from experimental to operational across a much wider range of organizations.

Background: Why the Acquisition Surprised the Industry

In late 2019, Mirantis — an enterprise cloud solutions vendor with roots in OpenStack — announced it had acquired Docker’s enterprise business. For much of the DevOps community, the news was unexpected. Docker had been the defining company of the container revolution, popularizing a technology that fundamentally changed how applications are built, shipped, and run. The idea that Docker Inc. was in trouble seemed almost paradoxical given how deeply its tooling had become embedded in modern software workflows.

To understand the acquisition, it helps to separate two distinct things: Docker the technology and Docker Inc. the business. The technology succeeded beyond most expectations. The business, however, struggled to turn that adoption into sustainable revenue.

What Made Docker Great — and What Went Wrong

Docker’s original value proposition was compelling: package an application and all its dependencies into a portable, lightweight container that runs consistently across environments. This eliminated entire categories of “works on my machine” problems, accelerated CI/CD pipelines, simplified microservices deployments, and made platform-agnostic development practical at scale. Enterprises adopted Docker rapidly because the operational benefits were immediate and measurable.

The business strategy, however, took a turn that would prove costly. As enterprise adoption grew, Docker Inc. shifted its focus toward Docker Enterprise — a commercial platform aimed at large organizations. This made sense on paper, but it coincided with the rapid rise of Kubernetes.

The Kubernetes Factor

Kubernetes, originally developed at Google and open-sourced in 2014, quickly became the dominant container orchestration platform. Its open-source community grew at a remarkable pace, and the Cloud Native Computing Foundation (CNCF) provided it with broad industry backing. Enterprises evaluating container strategies increasingly adopted Kubernetes directly — often without building a deep Docker Enterprise dependency first. When Docker eventually added Kubernetes support to its enterprise platform, much of the potential market had already standardized on Kubernetes independently.

Docker’s own orchestrator, Docker Swarm, never achieved the same level of adoption as Kubernetes, leaving Docker Inc. without a strong foothold in the orchestration market it had helped create.

The Financial Reality

Docker Inc. had raised approximately $280 million in total funding by the time of the acquisition. Despite that capital and widespread product adoption, the company had not achieved profitability, and an IPO remained off the table. Investors were pushing for an exit. In this context, the Mirantis acquisition was less a surprise and more an inevitable outcome of unresolved business model challenges.

Who Is Mirantis?

Mirantis built its reputation as an OpenStack specialist and cloud consulting firm. By 2019, it had pivoted toward Kubernetes, offering two primary products:

• Mirantis Cloud Platform (MCP): A solution for running private clouds based on Kubernetes and OpenStack.
• Mirantis Application Platform: Focused on continuous, cloud-native application delivery.

With approximately 450 employees at the time of the deal, Mirantis was a credible but relatively niche player compared to Docker’s brand recognition. The acquisition of Docker Enterprise gave Mirantis immediate scale, a large customer base, and a portfolio of widely-used developer tooling.

What Mirantis Acquired — and What It Gains

The acquisition covered Docker’s enterprise business. The financial terms of the deal were not publicly disclosed. Notably, shortly after the acquisition closed, Docker received an additional $35 million in funding from existing investors — a signal that confidence in Docker’s remaining developer-focused business remained intact.

What Mirantis gained was significant:

• ~700 enterprise customers, including major financial institutions such as PayPal, Visa, MetLife, and Societe Generale.
• ~300 Docker employees to integrate alongside its existing workforce.
• All existing Docker Enterprise contracts and strategic partnerships.
• Docker Enterprise technology, which Mirantis planned to extend with its own Kubernetes and lifecycle management capabilities.

Mirantis stated that Docker Enterprise aligned well with its strategic direction: providing developers with an on-demand, environment-agnostic experience. The combined entity positions Mirantis as a potential alternative to larger platform vendors such as VMware and IBM/Red Hat, with a particular emphasis on avoiding proprietary vendor lock-in.

Integration Plans

Mirantis signaled a gradual integration approach. Docker was to operate independently in the near term, with sales and marketing teams functioning separately before eventually merging into Mirantis. The goal was to minimize disruption for newly inherited customers during the transition period.

What the Deal Means for Docker’s Developer Products

While Mirantis took control of Docker Enterprise, the remaining Docker entity — led by new CEO Scott Johnston — refocused entirely on its developer tools: Docker Desktop and Docker Hub. Johnston described the new direction in public statements at the time:

“Going forward, Docker’s focus is to build on these foundations to advance developer workflows for modern apps. Along with real benefits, the last six years also resulted in additional complexities, an explosion of choices and new potential threats of lock-in. In light of these challenges, Docker and our community ecosystem have the opportunity to extend the open standards, functionality, automation tooling and cloud services of Docker Desktop and Docker Hub to better help developers build, share and run modern apps.”

— Scott Johnston, CEO, Docker

Key implications for Docker’s product roadmap at the time of the acquisition:

• Docker Desktop and Docker Hub would be the primary investment areas, targeting individual developers and development teams.
• Docker Swarm would be supported by Mirantis for at least two years post-acquisition but would not be the focus of active development. Kubernetes would become the primary orchestration target.
• Open-source Kubernetes-as-a-service solutions would be a joint priority for both companies.

The Broader Lesson for DevOps Tooling Companies

The Docker story is an important case study in the difference between technological success and business model success. Docker’s core innovation — the container image format and the developer experience around building and running containers — became foundational infrastructure for the entire cloud-native ecosystem. The OCI (Open Container Initiative) image specification is a direct descendant of Docker’s work.

What Docker Inc. struggled with was monetizing that foundational role at the enterprise layer, particularly as Kubernetes commoditized orchestration and cloud providers offered managed container services that reduced the need for a commercial container platform vendor.

For Mirantis, the acquisition represented a calculated bet: that Docker’s brand, customer relationships, and technology could accelerate its transition from an OpenStack specialist to a full-stack Kubernetes platform provider. Whether that bet would pay off remained to be seen at the time of writing.

Summary: Key Takeaways

• Mirantis acquired Docker Enterprise in late 2019; financial terms were not disclosed.
• Docker Inc. had raised ~$280 million but had not achieved profitability or an IPO.
• The rise of Kubernetes eroded Docker’s enterprise orchestration play and contributed to the business pressure.
• Mirantis inherited ~700 enterprise customers and ~300 employees from Docker.
• Docker Swarm received a minimum two-year support commitment from Mirantis, with Kubernetes as the long-term focus.
• The remaining Docker entity refocused on Docker Desktop and Docker Hub for developer workflows.
• Post-acquisition, Docker received $35 million in additional investor funding.

This article summarizes the core security practices every DevOps engineer should apply when working with Docker containers, regardless of the orchestration layer on top.

1. Treat the Container Like a Real Machine

A common misconception is that process isolation inside a container implies security. Isolation reduces the blast radius of a compromise, but it is not a security boundary on its own. Apply the same hardening principles you would to any server:

• Run as a non-root user. Add a USER instruction in your Dockerfile to drop privileges before the application starts. Running as root inside a container can translate to root on the host if container breakout occurs.
• Apply the principle of least privilege. Use Linux namespaces and cgroups to restrict what processes can see and consume. Docker exposes these through flags such as --cap-drop and --cap-add.
• Avoid world-writable directories and files. Audit filesystem permissions inside your images just as you would on a production server.
• Use read-only filesystems where possible. The --read-only flag prevents processes inside the container from writing to the container filesystem, limiting persistence opportunities for attackers.

# Example: non-root user in a Dockerfile
FROM node:20-alpine
RUN addgroup -S appgroup && adduser -S appuser -G appgroup
USER appuser
COPY --chown=appuser:appgroup . /app
WORKDIR /app
CMD ["node", "server.js"]

2. Only Use Images from Trusted Sources

Pulling a public image is convenient, but you are trusting everything the image author chose to include. Malicious or misconfigured layers can introduce backdoors, outdated vulnerable packages, or unexpected binaries.

Practical guidelines:

• Prefer official or verified images. Docker Hub marks images as “Docker Official Images” or “Verified Publisher.” These go through additional review and automated scanning.
• Enable Docker Content Trust (DCT). Setting the environment variable DOCKER_CONTENT_TRUST=1 instructs the Docker client to only pull images that carry a valid cryptographic signature from the image publisher. This protects against tampered images in transit.
• Host a private registry. For production workloads, mirror approved base images in a private registry (such as AWS ECR, Google Artifact Registry, or a self-hosted Harbor instance) and restrict developers to pulling from there.
• Scan images for vulnerabilities. Integrate a scanner such as Trivy, Grype, or Snyk into your CI pipeline to catch known CVEs before images reach production.

# Enable Docker Content Trust for the current shell session
export DOCKER_CONTENT_TRUST=1
docker pull myrepo/myimage:latest  # will fail if image is unsigned

Note on Docker Hub: A security incident disclosed at DockerCon 2019 affected approximately 190,000 Docker Hub accounts, serving as a reminder that no hosted registry should be trusted without additional controls. Content trust and private mirrors add meaningful defence-in-depth.

3. Use Docker Bench for Security

Docker Bench for Security is an open-source shell script maintained by the Docker team. It audits your Docker host and running containers against the CIS Docker Benchmark—a community standard covering daemon configuration, image hygiene, container runtime settings, and more.

Running it is straightforward:

# Run Docker Bench for Security using Docker itself
docker run --rm --net host --pid host --userns host --cap-add audit_control \
  -e DOCKER_CONTENT_TRUST=$DOCKER_CONTENT_TRUST \
  -v /etc:/etc:ro \
  -v /usr/bin/containerd:/usr/bin/containerd:ro \
  -v /usr/bin/runc:/usr/bin/runc:ro \
  -v /usr/lib/systemd:/usr/lib/systemd:ro \
  -v /var/lib:/var/lib:ro \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  --label docker_bench_security \
  docker/docker-bench-security

Each check is rated as PASS, WARN, or INFO. You can pipe the output into your CI system to fail a build when critical warnings appear, making security checks a first-class part of your delivery pipeline. The repository is available at github.com/docker/docker-bench-security.

4. Limit Direct Access to Kubernetes Nodes

When Docker containers are orchestrated by Kubernetes, the security perimeter extends to the nodes themselves. Unrestricted SSH access to nodes is one of the most common and dangerous misconfigurations in Kubernetes clusters.

• Restrict SSH access. Limit node SSH access to a very small group of platform administrators. Where cloud-provider tooling allows, remove direct SSH access entirely and use systems like AWS Systems Manager Session Manager or GCP OS Login.
• Use kubectl exec for debugging. Developers who need to inspect or debug a running container should use kubectl exec rather than accessing the underlying node. This gives them a shell inside the container namespace without host-level access.
• Enforce IAM and RBAC policies. Define Kubernetes RBAC roles that grant only the permissions required for each team or service account. Avoid binding the cluster-admin role to user-facing accounts.

# Open a shell in a running pod without touching the node
kubectl exec -it <pod-name> -n <namespace> -- /bin/sh

5. Isolate Workloads Using Kubernetes Namespaces and Policies

A compromised workload in a flat Kubernetes cluster can potentially reach every other workload. Namespaces and policy controls reduce this lateral movement risk.

• Use namespaces to segment workloads. Group related services into dedicated namespaces (e.g., payments, auth, logging) rather than leaving everything in default.
• Apply ResourceQuotas and LimitRanges. Namespaced resource quotas prevent a compromised or runaway container from consuming all cluster resources and affecting other tenants.
• Use Kubernetes RBAC authorization. Kubernetes’ built-in authorization plugin allows you to create fine-grained policies defining which users or service accounts can perform which actions on which resources, scoped to specific namespaces.
• Consider Pod Security Admission or OPA/Gatekeeper. Policy admission controllers let you enforce security standards (e.g., no privileged containers, no host network access) at the cluster level, before workloads are scheduled.

6. Protect the Network

Network controls are the last line of defence if a container is compromised. Even well-hardened containers should not be able to freely communicate with every other service in the cluster.

• Apply Kubernetes NetworkPolicies. NetworkPolicy resources define ingress and egress rules at the pod level. By default, Kubernetes allows all pod-to-pod communication; adding a default-deny NetworkPolicy and then allowlisting only the required connections significantly limits lateral movement.
• Use Ingress controllers to control external access. Define Ingress resources to explicitly declare which services are reachable via HTTP/HTTPS from outside the cluster, rather than exposing services directly with NodePort or LoadBalancer types.
• Consider a service mesh for mTLS. Tools like Istio or Linkerd can enforce mutual TLS between services automatically, ensuring that service-to-service traffic is encrypted and authenticated even within the cluster.
• Audit exposed ports. Review every container’s EXPOSE instruction and every Kubernetes Service definition. Eliminate any port that is not required for the service to function.

# Example: default-deny NetworkPolicy for a namespace
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-all
  namespace: payments
spec:
  podSelector: {}
  policyTypes:
    - Ingress
    - Egress

7. Keep Images and Dependencies Up to Date

Container images are point-in-time snapshots. An image built six months ago almost certainly contains packages with known CVEs by the time it reaches production, or shortly after. Treat image freshness as an ongoing operational concern:

• Rebuild base images on a regular schedule, not just when application code changes.
• Use minimal base images (e.g., alpine or distroless) to reduce the number of packages that need patching.
• Integrate vulnerability scanning (Trivy, Grype, Snyk) into both CI and as a recurring registry scan so you are notified when new CVEs affect images already in production.

Summary: Docker Security Checklist

Containers are powerful tools, but isolation is not invincibility. Each layer of your stack—image build, registry, runtime, orchestrator, and network—introduces its own risks and its own mitigations. Applying the controls above will meaningfully reduce your attack surface and make incidents easier to contain when they do occur.

Original article concept sourced from containerjournal.com. Expanded and updated for accuracy by the BestDevOps editorial team.

The Infrastructure Complexity Problem for Data Scientists

Artificial intelligence and machine learning have become essential disciplines for data scientists, but they bring significant operational overhead. Setting up GPU clusters, managing drivers, configuring runtimes, and keeping infrastructure healthy can consume time that would be better spent extracting insights from data.

“We’re working towards reducing that complexity and making sure that people that are data scientists, AI, or machine-learning engineers are able to focus on their core business values,” said Renaud Gaubert, lead container and OSS engineer at NVIDIA Corp., speaking at KubeCon + CloudNativeCon.

The ideal experience, as Diane Mueller, director of community development at Red Hat, described it, is straightforward: data scientists want to open a JupyterHub notebook, find everything they need already in place, train their models, and move on — without worrying about what is running underneath.

“[Data scientists] would like to just open up a JupyterHub notebook, have everything there they need, train their models, have them run, and then after they’re done, they’re done.”

Diane Mueller, Director of Community Development, Red Hat Inc.

Kubernetes Operators as the New Deployment Pattern

Kubernetes Operators encode operational knowledge — installation, upgrading, failure recovery, and monitoring — into software that runs inside the cluster itself. Rather than requiring a human operator to manage complex stateful workloads by hand, an Operator continuously reconciles the actual state of the cluster with the desired state defined in a custom resource.

At KubeCon, Red Hat announced an alpha release of OpenShift 4 built entirely on Operators. Mueller described this as a significant shift in how applications and services are deployed and managed on Kubernetes platforms.

“It’s an all Operators-based deployment of OpenShift. I think the Operator framework really has been the big thing that we’ve been really getting a lot of uptake on. It’s been the new pattern for deploying applications, or services, and getting things beyond just a basic install of a service on OpenShift or any Kubernetes.”

Diane Mueller, Red Hat Inc.

OperatorHub.io: A Shared Marketplace for Kubernetes Operators

Alongside the OpenShift 4 alpha announcement, Red Hat has been building out OperatorHub.io as a community hub for upstream Kubernetes Operators. Operators listed there — including the NVIDIA GPU Operator — are designed to run on any conformant Kubernetes distribution, not just OpenShift.

“OperatorHub.io, everything in there runs on any Kubernetes. And the goal is to be able to take stuff in a hybrid-cloud model [and] be able to run it anywhere you want.”

Diane Mueller, Red Hat Inc.

Free access to these Operators means platform teams can deploy them on any Kubernetes environment, supporting a hybrid-cloud model where workloads are not locked to a single provider or distribution.

The NVIDIA GPU Operator: Four Phases of Automation

The NVIDIA GPU Operator is built on Red Hat’s Operator SDK and demonstrates how the Operator pattern can dramatically simplify AI/ML infrastructure management. Gaubert outlined four distinct phases of automation the GPU Operator handles:

Phase 1: Component Installation

The Operator automates the installation of every component a GPU cluster requires: the NVIDIA driver, the container runtime, the Kubernetes device plugin, and the necessary mount points. This replaces a manual, error-prone bootstrap process with a single declarative resource definition.

Phase 2: Infrastructure Automation and Updates

Once the cluster is running, the Operator automates infrastructure builds and manages rolling updates to individual components. This removes the need for manual intervention each time a driver or plugin version changes.

Phase 3: Lifecycle Management and Self-Healing

In a fleet of GPU nodes, individual machines will inevitably enter degraded or failed states. Phase three addresses lifecycle management: the Operator detects nodes that have drifted from the desired state and automatically remediates them, recovering from bad states to good ones without manual intervention.

Phase 4: Monitoring and Observability

The final phase surfaces operational insights to users. By integrating monitoring into the Operator itself, GPU cluster health and utilization data becomes visible without requiring separate, manually configured tooling.

“The Operator SDK has helped us a lot here, just laying out these different steps, and, in a way, it’s done the same thing as what we’re trying to do for our customers, the different data scientists. Which is, basically, get out of our way and allow us to focus on core business values.”

Renaud Gaubert, NVIDIA Corp.

Why This Matters for AI/ML Teams

The Operator pattern addresses a genuine pain point: the gap between Kubernetes as a general-purpose platform and the specific, complex requirements of GPU-accelerated AI/ML workloads. By encoding operational expertise into software, Operators allow platform teams to offer a managed-service-like experience on self-hosted or hybrid infrastructure.

• Reduced toil: Routine tasks such as driver installation, updates, and node recovery are automated rather than handled manually.
• Consistency: The same Operator runs identically on OpenShift, upstream Kubernetes, or any cloud provider’s managed Kubernetes service.
• Self-service for data scientists: With infrastructure managed by Operators, data scientists can provision GPU-backed environments without engaging the platform team for each request.
• Hybrid-cloud portability: Operators listed on OperatorHub.io are not tied to a single distribution, supporting workload portability across environments.

Further Resources

To explore Operators available for your Kubernetes environment, visit OperatorHub.io. For documentation on building your own Operators, the Operator SDK is the recommended starting point for Go, Ansible, and Helm-based Operators.

Source: SiliconANGLE / theCUBE interview at KubeCon + CloudNativeCon, San Diego, November 2019.

Data Theorem, Inc. a provider of modern application security, introduced a new SPA security service that delivers automated discovery and continuous dynamic runtime vulnerability inspection of modern web single-page applications (SPAs). Purpose-built for SPAs, the new service is differentiated in its runtime security analysis that supports both GraphQL and REST API services, the popular services for SPAs that dynamically deliver a faster and richer web user experience.

With DevOps teams rapidly building web SPAs, security and IT teams using traditional web app scanners lack the ability to gather application insights and inspect for security vulnerabilities on these new modern web apps. Now with this new offering from Data Theorem, for the first time users can fully discover and inspect vulnerabilities with dynamic runtime analysis for both GraphQL and REST API services.

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“SPA security is the new frontier for modern web application security, and like mobile it is tightly coupled to the explosion and growth of GraphQL and API backend services,” said Doug Cahill, senior analyst and group practice director of cybersecurity for ESG. “To best protect these services from attack, organizations need a solution that delivers both continuous security vulnerability inspection and runtime analysis that supports both GraphQL and REST API services.”

Businesses today delivering modern web applications build SPAs to deliver a richer and faster user experience that is similar to what they deliver with their mobile apps. Similar to mobile app protection, traditional web app scanners lack the ability to add security insights to SPAs because of the dynamic nature of the SPA JavaScript architecture. In addition, GraphQL adds a new attack surface due to the enhanced flexibility it provides, making it difficult to protect against malicious queries. These attack queries could lead to denial of service attacks, or unauthorized access to private data.

“Growth of SPA deployment and usage increases every year because organizations want their web experience to be as good as their mobile app experience,” said Doug Dooley, Data Theorem COO. “But security tools have not kept up with this modern software development trend. With our first web app security offering launching today, Data Theorem is leaping ahead of the competitive landscape to now serve users’ complex security needs beyond API and mobile. We were already leading in runtime analysis for mobile apps, and now we offer similar depth of runtime analysis to protect these popular SPAs.”

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Today’s SPA security solution is offered as a component of Data Theorem’s API Discover and API Inspect, which together address security concerns such as Shadow APIs, Serverless Applications, and API Gateway cross-check validation by conducting continuous security assessments on API authentication, authorization, encryption, availability, serverless functions, and policy compliance.

The API security solutions support Amazon Web Services, Google Cloud, and Microsoft Azure to discover modern APIs and to enumerate the specification using standards such as Swagger and Open API 3.0.