Terraform Introduction and Basics with AWS

Today’s world is completely data-driven. Each encounter is a data point. The emails you send are managed by Google’s servers as data. Every interaction is a piece of data. However, as you may have noted, they are linked to the internet and not physically joined.  All of a company’s needs in terms of software as a service, platform as a service, and infrastructure as a service may be met by Cloud Computing. Nowadays, no one enjoys making software for each of their use cases, building platforms, or managing their own data center.

Steve Jobs said, and I quote, “I don’t need a hard disk in my computer, if I can get a server faster.” Carrying around those non-connected computers is byzantine by comparison. In this age of the cloud, Steve’s brilliance is obvious.

There are many software as a service (SaaS) providers nowadays, and many of them use cloud providers like Amazon Web Services, Microsoft Azure, Google Cloud Platform and others. However, the costs associated with maintaining such softwares will continue to rise indefinitely as their usage increases. The many services are each assumed to have their own infrastructure, including networks, virtual machines, databases, load balancers, and more. Not going to explain every nuance, but hopefully you get the gist. A large group of people would be needed to handle this. 

Following terraform, an entrant emerged. Terraform is an open-source infrastructure-as-code software solution that enables you to build, modify, and enhance your infrastructure in a reliable and consistent manner. Cloud LaaS resource provisioning, management, and modification became as easy as writing a single line of code.

What is Infrastructure as Code with Terraform?

Infrastructure as Code (IaC) tools let you control infrastructure with configuration files, optimal JSON (GUI). IaC allows you to develop, alter, and manage your infrastructure in a safe, consistent, and repeatable way.

HashiCorp makes Terraform, an infrastructure-as-code technology. It maintains your infrastructure’s lifespan and allows you to define resources and infrastructure in human-readable configuration files. Terraform is better than hand-managing infrastructure in various aspects.

  • Terraform supports multiple cloud platforms
  • Easy-to-read configuration language speeds up infrastructure coding
  • Terraform’s state lets you track resource changes during deployment
  • Allows safe collaboration on infrastructure by versioning configurations
  • It’s open-source

Terraform Basics

Getting Started with Terraform

What would you need? 

->Terraform CLI

-> AWS CLI

-> An Editor, preferably VS Code

-> Terraform VS Code Editor IDE

Installation on MacOS

Terraform Installation

Download Terraform MAC
Install CLI

Unzip the package

# Copy binary zip file to a folder

mkdir /Users/<YOUR-USER>/Documents/terraform-install

COPY Package to “terraform-install” folder

# Unzip

unzip <PACKAGE-NAME>

unzip terraform_0.14.3_darwin_amd64.zip

# Copy terraform binary to /usr/local/bin

echo $PATH

mv terraform /usr/local/bin

# Verify Version

terraform version

VS Code and Terraform Plugin Installation

Microsoft Visual Studio Code Editor
Hashicorp Terraform Plugin for VS Code

AWS CLI Installation 

# Install AWS CLI V2

curl “https://awscli.amazonaws.com/AWSCLIV2.pkg” -o “AWSCLIV2.pkg”

sudo installer -pkg AWSCLIV2.pkg -target /

which aws

aws –version

Configure AWS Credentials

aws configure

AWS Access Key ID [None]: AKIASUF7DEFKSIAWMZ7K

AWS Secret Access Key [None]: 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

Default region name [None]: us-east-1

Default output format [None]: json

# Verify if we are able list S3 buckets

aws sts get-caller-identity

Installation on Linux

Terraform Installation

Download Terraform MAC
Install CLI

VS Code and Terraform Plugin Installation

Microsoft Visual Studio Code Editor
Hashicorp Terraform Plugin for VS Code

AWS CLI Installation  and AWS Credentials

AWS CLI

Follow similar steps from macOS to configure AWS-CLI

Installation on Windows

Terraform Installation

Download Terraform
Install CLI

Unzip the package

Create new folder terraform-bins

Copy the terraform.exe to a terraform-bins

Set PATH in windows

VS Code and Terraform Plugin Installation

Microsoft Visual Studio Code Editor
Hashicorp Terraform Plugin for VS Code

AWS CLI Installation  and AWS Credentials

AWS CLI

Follow similar steps to configure AWS-CLI

Terraform Workflow and Commands

Terraform blocks can be very confusing. To make it simple to understand, we will cover terraform workflow and move right into some command basics. 

Command: terraform init

With this, the config directory, which stores all of the setup files, may be initialized. Basically, this is executed anytime a piece of code is added or removed from the configurations. The providers are downloaded at your command (basically AWS plugin in our case).

Command: terraform validate

Performs a syntax check and an integrity check on the configuration files to ensure they are valid. The term “internally consistent” refers to the fact that all resource changes have been checked. Attempting to delete an empty resource, for instance, should fail validation.

Command: terraform plan

This command creates an execution plan and notifies what all changes are likely to be made. 

Command: terraform apply

Apply is used to apply the configuration as the name suggests.

Command: terraform destroy

Used to destroy a configuration, and applies the change post user confirmation.

Demo Project to Create an EC2 machine using Terraform

With an understanding of what these commands perform, now write some code to build a VM in AWS, often known as EC2 or Elastic Cloud Compute. However, you must first meet the following requirements:

  1. There is an active VPC in the region
  2. AWs Credentials is working 

Let’s create our first terraform-manifests. You can use this sample code below.

# Terraform Settings Block

terraform {

  required_providers {

    aws = {

      source  = “hashicorp/aws”

      #version = “~> 3.21” # Optional but recommended in production

    }

  }

}

# Provider Block

provider “aws” {

  profile = “default” # AWS Credentials Profile configured on your local desktop terminal  $HOME/.aws/credentials

  region  = “us-east-1”

}

# Resource Block

resource “aws_instance” “ec2demo” {

  ami           = “ami-04d29b6f966df1537” # Amazon Linux in us-east-1, update as per your region

  instance_type = “t2.micro”

}

As you would see, Terraform employs three different blocks: the Settings block, the Provider block, and the Resource block. For the time being, we may ignore this and concentrate on the terraform init/validate/plan/apply/destroy command. In our subsequent blog, we’ll go into greater depth on this topic. 

The following is provided solely for your education: 

The cloud provider’s Terraform plug-in and the Terraform version are both specified in the settings block. It’s AWS for us. After declaring the resource in the resource block, we specify the cloud provider’s area in which the resource will be created in the provider’s block.

Command Execution

Step 1: Go to the location where you saved the manifest. The current working directory should be the same as the location of your Terraform manifests. Now let’s run terraform init. This would start the terraform and load the AWS HashiCorp plug-in. Along with it, a terraform.lock.hcl file is generated. It saves the version we’re using so that if there are any changes in the future, it can refer to it.

Step 2: At this point, we can run terraform validation. It will notify you if there is a problem with the file. Otherwise, a configuration valid message will be displayed on the screen.

Step 3: Now execute the terraform plan. This will print all of the resources that terraform will be producing as part of this project. The ‘+’ symbol indicates that it will create it, while the ‘-‘ sign indicates that it will modify it.

 

Step 4: Finally, run and apply terraform. This time, construct a plan and ask for yes or no confirmation. Choose yes – this will launch an ec2 instance.

Conclusion

A fundamental understanding of Terraform is now at your disposal. Declarative language makes it simple to define the exact system architecture you need to build. You can test your modifications and find bugs before releasing them with the plan command. You can maintain DRY and effective code with the help of variables, references and dependencies, which will be covered in subsequent blogs.

Remember, we have barely begun to scratch the surface. For how to manage Terraform state demonstrates, how Terraform remembers the infrastructure it has already built, and how it has far-reaching implications for the way you organize your Terraform code – stay tuned for the next Terraform series. 

 

 

How to unleash the full power of Azure DevOps

Microsoft Azure is the second most significant cloud provider behind Amazon’s AWS. Azure has over 20% of the cloud market share and is gradually catching up with AWS. Azure DevOps is a significant factor that contributed to Microsoft’s success in the cloud space.

Microsoft Azure DevOps offers the capability to deploy the hosted version of the cloud or effortlessly deploy your own data center. Its offerings include a one-stop suite of services to create continuous integration and continuous delivery (CI/CD) pipelines, version control, test case management, and requirements planning and management.

This article details how DevOps engineers can effortlessly cover much of the application lifecycle with the Azure out-of-the-box components.

1. Getting started 

When we pointed out that Azure DevOps technology tools easily integrate, we did not imply that simple out-of-the-box deployment is enough to get the best out of them.

When implementing a new setup on Microsoft Azure DevOps, you need to plan how you will put the elements together. Building a sophisticated pipeline can be much more challenging unless you understand CI/CD principles well. For instance, you must never stack together too many tasks in one stage when deploying an application with Azure DevOps. It leads to troubles when debugging, as all other tasks must be disabled to test a single job. This entire process is not only time-consuming but also frustrating, which defeats the purpose of DevOps.

2. Create proficiency with training 

Azure DevOps is designed to be an off-the-shelf solution. However, when working with sophisticated workflow pipelines, engineers must assemble them in a way that offers a bespoke solution per the organization’s needs.

An easy way to introduce proficiency in your team to handle Azure DevOps better is to provide them with a few weeks of training. In-house software developers and architects might not be fully aware of the capabilities of the DevOps technology that can be deployed with Azure. 

Also, your engineers must be preoccupied with projects which leave them little time or space to explore the full capabilities of Azure DevOps. An in-depth Azure DevOps training with people who have spent time in the trenches and have years of real-world experience can bring your development teams up to speed in no time.

3. Practice “un-deploy”

It would help if you could efficiently get the most out of Microsoft Azure. For this, you have to plan the application deployment in a phased-out manner to highlight the components that do not need to be moved to the cloud. One difference between in-premises hosting vs. cloud migration is that you are charged for everything you use. To cut costs and maximize the use of available Azure resources, you must un-deploy the processes that are not necessary. Also, DevOps engineers should create functional pipelines so that any component can be undeployed if required.

4. Put only the core team into Azure DevOps

Nothing is worse than building a failed deployment, often due to team mismanagement. With Azure DevOps, you can better manage teams reducing such failures. When too many members are working on Azure DevOps, somebody might tweak something without informing the DevOps team. 

However, Azure DevOps has several security features that solve this problem. You can easily manage access to Azure DevOps, permitting only the core DevOps team to deploy. Other engineers and developers on the unit can be part of other Azure DevOps security groups and access their deployments.

5. Maximum strategy and protocol

When you have a strategy in mind, you can always handle any aspect of the project without getting lost down the line. Multiple layers of planning must include different strategies for artifact naming, scaling, monitoring/logging, security, change management, and backups/reliability. It would help if you planned the maximum strategy by involving as many stakeholders as possible such as engineers, DevOps, and QA testers.

This also creates a deployment cadence for team members to have a better overview of the entire Azure project, enabling them to deploy anything, anytime, and in any environment. You must establish a protocol for the DevOps team to follow. Let your team practice the processes you set up on Azure DevOps such that they do not face difficulties later on. As the team starts building the protocol, they will encounter challenges that they have to address and resolve.

Conclusion 

Deploying to the cloud gives businesses an advantage to grow at scale. However, you have to constantly change and improve with the cloud as it makes your services better. If you are using Microsoft Azure, this guide will help you to use Azure DevOps at its full potential. Ensure you are flexible and ready to learn new tools and DevOps technology.

Talent500 is the remote team-building platform trusted by Fortune 500 companies and fast-growing startups. Sign up here to join the DevOps engineer pool and discover global job opportunities.

 

Kubernetes developer toolkit: In-demand skills, learning resources, online courses, interview prep, books & more

Kubernetes is one of the leading container orchestration platforms. Companies prefer Kubernetes for their projects because of its cloud-agnostic approach that efficiently manages workload whether your project resides in the cloud or on-premises. It has freed companies from being locked into the services offered by their cloud provider or managing entire team operations on-premises or virtualization platforms. This is why Kubernetes is one of the most critical technologies in the DevOps domain. In 2021, there were over 3.9 million Kubernetes developers worldwide, which is a 67% increase from the previous year. As the adoption of technology increases, it is a valuable skill to add to your resume. 

This Kubernetes developer toolkit lists the best resources to learn, upskill, and succeed in the industry.

Microservices: Kubernetes developers must know how to develop software applications based on microservices. Even when the programming languages are the same, developing applications for Kubernetes based on modern architecture requires different coding patterns and software release processes.

System administration: Releasing software updates regularly on a Kubernetes hosting platform requires advanced system administration skills. With the traditional approach of deploying code, there is no way to know the scalability flaws unless the software is installed and used by the customers. But the modern software architecture requires developers to learn Linux commands and database queries to test the software in production.

CI/CD: Kubernetes is for hosted applications with continuous integration and continuous delivery (CI/CD) as an integral part. As a Kubernetes developer, you must be familiar with CI/CD tools like Jenkins. You need to know how coding and software deployment steps can be automated. You will be asked about concepts like unit and functional tests, deployment of the Kubernetes pods, and code compilation in a hosted environment.  

API support: You need to be able to create an Application Programming Interface (API) for the software deployed on Kubernetes such that users who want to interact with your hosted application directly can do it. You must know the concepts of API, such as JSON or YAML files. Learn more about tips on creating robust APIs.

Top Kubernetes interview questions

40 top Kubernetes interview questions and answers for 2022

21 Kubernetes interview questions for senior and DevOps developers

Most asked interview questions of Kubernetes

15 essential Kubernetes interview questions

The must-know Kubernetes interview questions and answers in 2022

Official resources 

Kubernetes website

Official Kubernetes training resources

Official Kubernetes blog

Official Kubernetes community

Online courses and tutorials for Kubernetes developers 

Learn DevOps Kubernetes deployment by kops and terraform

This practical course teaches deploying Kubernetes on AWS using Kops and Terraform. It will detail how to deploy a Kubernetes cluster in AWS and run immutable infrastructure using Terraform. The course also includes lessons on horizontally scaled deployment in Kubernetes and details on using Kops to spin up the Kubernetes cluster. Among other concepts that are explored in the course are how to see logs and Docker container schematics within the pod in Kubernetes.

Just enough Kubernetes to be dangerous

A free and concise course to get started with Kubernetes, this course will get you started quickly, to be precise, in just 1.5 hours. The prerequisite to benefit from this course is programming experience as it takes a practical approach to teach Kubernetes. It includes topics like setting up a Kubernetes cluster with Google Kubernetes Engine, how to deploy a micro-services application, how to dynamically scale applications in the cloud, release application updates, and achieve Zero downtime deployments.

Getting Started with Google Kubernetes Engine

This course, created by the Google Cloud team in association with Coursera, is a one-week, accelerated training class that teaches you some essential concepts of Kubernetes. It teaches how to containerize applications in Docker containers, deploy them to Google Kubernetes Engine, and scale the applications automatically as the demand increases.

Containers 101

It is another free Kubernetes course to learn how to build and deploy containerized applications. It teaches you the basics of Docker and how to use Kubernetes on Docker to host applications. The course includes concepts of modern DevOps, such as orchestration, packaging, Kubernetes, and Helm stack. This is the right course for you if you want to learn how to build applications on the fast-growing Docker.

Books for Kubernetes developers 

The Kubernetes Book

Suitable for beginners, it is ideal for learning the fundamentals of Kubernetes and dives deeper into its architecture, API, and how it is built.

Kubernetes in Action: 1st Edition

One of the best books on Kubernetes, this one is a complete guide on skillfully developing and running apps in a Kubernetes ecosystem. It covers Docker, Kubernetes, and the detailed analysis of the container orchestration systems on a deeper level.

Cloud-Native DevOps with Kubernetes

Cloud-Native DevOps with Kubernetes focuses on a more practical learning approach and teaches developers how to deploy cloud-native applications with complete infrastructure. It also covers the essential DevOps skills for developers working in an organization.

Learn Kubernetes in a Month of Lunches

An interesting title, this book is aimed at working developers. An excellent resource for experienced software developers, it is an ideal book for those with a basic knowledge of containerization and Docker. It teaches the advanced concepts of application deployment and lifecycle. A complete guide about how to model, deploy, and manage applications, this book is ideal for learning Kubernetes fast if you have the basic knowledge.

Online communities for Kubernetes developers 

Kubernetes Slack channel

Kubernetes on GitHub

Kubernetes StackOverflow

Kubernetes Twitter

Kubernetes YouTube

Podcasts for Kubernetes developers

Kubernetes podcast from Google

The Kubelist podcast

PodCTL – Enterprise Kubernetes

As essential as technical skills are for Kubernetes developers, they also need soft skills to be part of the DevOps teams.

Communicate clearly: DevOps environment requires transparent communication. You must develop communication skills to share ideas and ask questions comfortably when needed. You must always be clear about the intentions in your conversions, such that there is no scope for misinterpretation or ambiguity.

Foster creativity: DevOps engineers need to think outside the box to push the limits and further the project’s potential. You are free to experiment with different approaches and tools to solve the given problem as long as you are within the DevOps guardrails.

Be willing to learn: A learner’s attitude is mandatory to progress in your DevOps career. You have to take the initiative and be willing to take on challenges you are not ready to accept. Active learning on the job sets you apart from the rest of the developers. We have listed dozens of learning resources above; use them to improve your skills continuously.

More resources on soft skill development:

Most valuable soft skills of exceptional software engineers

6 soft skills that still impress employers in 2022

How soft skills transitioned to become the new power skills

5 tips to present new ideas as a software developer

Conclusion 

Kubernetes is not a skill that you can master quickly. It demands a deep understanding of concepts and the ability to transform the knowledge into real-world projects practically. We created this Kubernetes developer toolkit to help you learn these concepts and succeed as a DevOps engineer.

Talent500 is the platform for DevOps engineers to find opportunities with fast-growing startups and Fortune 500 companies. Sign up here to explore possibilities.

 

Terraform developer toolkit: In-demand skills, learning resources, online courses, interview prep, books & more

Terraform is an infrastructure as a code tool that developers use to define the project resources both on the cloud or on-premise in human-readable configuration files. It makes it easy to reuse and share any code in a native cloud environment. More and more companies are using Terraform to manage the infrastructure and application lifecycle via a consistent workflow. As a DevOps engineer, learning Terraform gives you the skills to handle even the most low-level components of hosted applications like CPU, storage, and networking resources.

Here is a complete Terraform developer toolkit with a list of resources that help you learn, upskill, and prepare for the interviews.

1. Terraform’s Language (HCL): Also called Hashicorp Configuration Language, you must be familiar with the syntax to use Terraform. Here’s an example syntax: 

Terraform_command “provider_resource_name” “resource_name” {

“option_1”: “option_1”,

  “option_1”: “option_1”

}

When you understand the Terraform code’s basic structure, it becomes much easier to use Terraform. Any Terraform code block has four critical elements: Command, Provider Resource Name, and Options.

2. State: One of the most important concepts of Terraform is the State. As a client-based application, Terraform must be able to keep track of the resources that it creates. For this functionality, it uses the concept of state. You must know how to make a State JSON file and use it for resource optimization.

3. Dependency resolution: Creating a cloud infrastructure requires many resources that are created simultaneously. There is an overlapping dependency between these resources that need management. With Terraform, you can quickly identify which resources depend on each other to prepare a well-planned execution strategy. As a Terraform developer, you must know the concept of dependency resolution, which is extremely important to deploy resource updates in the correct order.

4. Infrastructure as Code: Any DevOps engineer will have to ensure that the systems they design are built in a repeatable manner using Infrastructure as Code. It is an essential skill because it is how you can document cloud objects as version-controlled code. You must learn the concepts of IaC to get better opportunities as a Terraform developer.

Top 35+ most asked Terraform interview questions and answers

28 real-time Terraform interview questions and answers

Top 40 Terraform interview questions and answers for 2022

11 most asked Terraform interview questions & answers [for freshers]

Top 100 Terraform interview questions with answers

4 great tips to stand out in a coding interview

Difficult interview questions & how to answer them

10 common questions to expect during your remote job interview

7 Common full-stack developer interview questions

7 Dos & don’ts for passing your technical interview

Official resources 

Terraform website

Terraform registry

Terraform tutorials

Terraform docs 

Terraform community

Online courses and tutorials for Terraform developers 

Hashicorp Certified — Terraform Associate

A course designed to make you a Terraform Associate, it is offered by Udemy. We recommend this online course for DevOps engineers who plan to implement Terraform in their organization. It covers all the Terraform modules and best practices in detail. This is the best course if you plan to learn the Infrastructure as Code approach.

Terraform for absolute beginners

For experienced developers, this 1-hour Coursera Terraform course is ideal. It is aimed at developers unfamiliar with Infrastructure as Code or who have not used Terraform before. This course covers Infrastructure as code concepts and ideas, Terraform basics and its execution flow, HCL language (Harshicorp Configuration Language System) syntax, and how to create Infrastructure using Terraform. Following the examples in the course, you will develop critical skills like DevOps, IT Automation, and Infrastructure as Code.

Terraform: From Beginner to Master with Examples in AWS

If you are an AWS developer planning to use Terraform for infrastructure deployment, this is the course for you. One distinctive quality of this course is its text-based interactive course. Implying you can practice right in the browser while learning Terraform. It is another excellent course to learn Terraform if you are a beginner. It elaborates on what challenges can be solved with Terraform in real-world applications. It covers all the critical features of Terraform, and as you don’t have to set up any local environment for practice, it’s much more user-friendly.

Books for Terraform developers 

Pipeline as Code: Continuous Delivery with Jenkins, Kubernetes, and Terraform

A book that teaches you how to automate your development pipeline in a cloud-native, service-driven world, Pipeline as Code is excellent for learning Terraform. It covers all essential concepts like CI/CD pipelines, best practices, and how to build reliable CI/CD pipelines for cloud-native applications using Terraform, and other modern tools.

Terraform: Up & Running

This hands-on book teaches you how to get up and running with Terraform fast. You will learn how to write production-grade Terraform modules and perform manual and automated testing for your code. This book also compares Terraform to Chef, CloudFormation, Puppet, Ansible, and SaltStack, so you better understand its features.

Patterns and Practices for Infrastructure as Code

This is a quick-start guide to getting started with Terraform in a cloud environment. The book begins with beginner’s concepts and moves on to features to manage Infrastructure at scale. It details advanced topics like state management, modules, and team collaboration on Terraform. As the book uses Python for Terraform, it is excellent for Python developers.

Online communities for Terraform developers 

Terraform on GitHub

Google Terraform YouTube Channel

Podcasts for Terraform developers

All About Terraform

Getting Started with Terraform on AWS with Cobus Bernard

Teamwork: DevOps teams work with both developers and operations teams. As part of a DevOps team, you should have a teamwork spirit. Learn to work well with other stakeholders and accommodate their ideas and requirements. Most successful teams are the ones that enable collaboration among members.

Patience: When working as a DevOps engineer, you must have patience. Not everyone will get your idea or the reason behind your decisions immediately. Other developers in your team might need more time to understand things. For this reason, you have to be patient. The operations team might be unable to understand how complex programming is or how long product update releases might take; for such reasons, you should anticipate the need for patience.

Open-mindedness: When you keep your mind open to new ideas, whether yours or someone else’s, you are more approachable and innovative. It would help if you took your time to understand the ideas presented to you before dismissing them. Exploring ideas is how you progress and find new ways to solve a challenge.

More resources on soft skill development:

5 Necessary soft skills for DevOps engineers

How soft skills transitioned to become the new power skills

4 key characteristics of a great software development team

Tips for good communication within a software development team

Conclusion 

Terraform is a vital tool in the native cloud software application environment. Adding Terraform to your skillset, you will be eligible to explore more challenging and better-paying DevOps engineer opportunities.

Talent500 is the platform for DevOps engineers in India to explore opportunities at global tech companies. Sign up here to join our elite pool of talent.

 

How does DevOps improve deployment frequency?

DevOps has overtaken every other development methodology. Almost every successful company depends on DevOps to optimize its development and operations.

Simply put, DevOps is a set of processes that allow companies to improve the coordination between teams within the engineering and operations departments. This is important to create and enhance products faster and more agilely with a higher success rate than traditional software development methods such as waterfall methodology.

This article focuses on one of the great benefits of DevOps, which is to improve deployment frequency. We explore how companies can improve product delivery and deployment while automating the process.

What is deployment frequency? 

The deployment frequency is one of the essential data-driven metrics of DevOps and represents the effectiveness of CI/CD. This metric function measures the frequency at which the development teams deploy code to development, test, and production environments. The deployment should include functional pieces of code, improved versions, or bug fixes.

DevOps improves development by making continuous code deliveries in smaller, faster, and more testable modules. This is why the deployment frequency is closely related to the DevOps continuous delivery philosophy. It is an essential metric in the overall software development life-cycle and provides an opportunity to monitor how well the development processes work. This more extensive overview helps identify broader issues like lack of professionalism among the team members and inefficient processes.

What causes low deployment frequency? 

The success of any DevOps project depends on continuously measuring the team’s effectiveness. Your goal should be to improve the deployment frequency for delivering projects faster. Several actions can be taken in the software development environment to increase deployment frequency.

Before DevOps engineers can plan a strategy, they must understand the causes of low deployment frequency. Numerous reasons can bring down the deployment frequency – project size, the number of engineers in the team, and the deadline. However, here are the most common reasons:

  • Substantial changes are introduced in the code that requires hundreds of lines of code to be written and tested.
  • Fewer people in the team; for instance, if a senior DevOps engineer leaves when the project is still in development. Their absence can delay the project.
  • Any organizational structure changes can disturb the workflow.
  • Hurdles in the development process such as technological deficiencies, dependencies, etc.
  • Unplanned development cycle or bottlenecks in routes to production.

How can the deployment frequency be improved with DevOps?

DevOps implements CI/CD best practices in the team, allowing developers to increase software delivery speed. Continuous integration and delivery are known to automate several development pipelines and reduce the time it takes to include a new feature or update the existing product.

More minor and self-contained changes; as mentioned above, DevOps enables teams to segment the project update or development requirements into smaller tasks. One of the benefits of working with a smaller version of changes is that it makes it much easier to get feedback faster and resolve any issues sooner. This contributes to improving the deployment frequency.

DevOps reduces technical debt. In software engineering, technical debt is described as the implied cost of additional rework that needs to be done because the team chooses an easy (often limited) solution to solve a problem instead of a better approach. With DevOps, any team can significantly reduce technical debt and remove bugs that increase the rate of subsequent deployments.

Test automation with DevOps technology. Manual testing is resource intensive and takes longer, so incorporating automated tests at every stage of the CI/CD pipeline reduces the delivery time multiple folds. It also helps DevOps engineers to catch issues earlier during the development of the code, which further improves deployment frequency.

DevOps uses automated code review tools like GitHub and Review Board that can help improve the code quality and save valuable time. This provides development teams with enough time to regressively review code changes faster.

DevOps uses release trains for more enormous monoliths. Primarily for enterprise-level products, multiple changes are often released together. For such projects, DevOps engineers can batch numerous changes together and release them all simultaneously, thus coordinating the updates without slowing down the deployment frequency.

Conclusion

Deployment frequency is one of the essential metrics to analyze and improve the efficiency of any engineering team. It is valuable data that helps you understand the capabilities of your team and estimate how quickly you can provide value to your customers when required. With DevOps, any organization can promptly improve deployment frequency, as detailed in this guide.

Talent500 is a remote team-building platform helping Fortune 500 companies and fast-growing start-ups hire the best DevOps engineers. Sign up here and join our pool of talented Indian DevOps engineers and be on the path of building successful engineering teams at some of the largest tech companies.

 

What is the future of DevOps technologies?

DevOps is one of software development’s most widely adopted automation processes. DevOps engineers are responsible for accelerating the development of products and frequent releases of updates. Some of the most renowned companies in the world, such as Google, Netflix, and Amazon, use this methodology to increase the efficiency and productivity of their teams.

According to market research, the expansion of DevOps is expected to reach $287.9 billion by the year 2026. This growth will demand more DevOps engineers to join the engineering teams. If you are wondering about the future of DevOps technologies, the prospects are up-and-coming.

This article explores the future of DevOps technologies and how organizations are improving the development culture by implementing this methodology.

What is DevOps? 

DevOps is the methodology that provides an alternative to the agile development environment for creating faster and smoother development of applications. It is a continuous process that also includes last-mile delivery. This methodology ensures that product testing, development, and operation are in sync and that delivery can be made in less time. Several processes are involved in DevOps, such as voting, building and integration, continuous testing, reporting, monitoring, and configuration.

For a successful career as a DevOps engineer, you need to focus on building skills in the following technologies.

1. AI/ML in the DevOps

The DevOps methodology has made the software development life cycle much more efficient. Combined with a cloud-native approach and microservice architecture, it is possible to build any software with limited resources and time. With this approach, you can integrate testing and production environments to find problems with the application before it goes live proactively.

But there is scope for further improvement in DevOps pipelines by applying artificial intelligence and machine learning. With these technologies, you can build and automate much better, which is why the industry is gradually transitioning from DevOps to DataOps and AIOps. The focus is shifting to using artificial intelligence and machine learning technologies to log and monitor metrics that give more control automatically. It would help if you learned tools like Moogsoft and BigPanda, a leader in AIOps.

2. Serverless computing

DevOps teams are actively using serverless architecture for maintaining their production pipelines because of the massive operational cost of server infrastructure. DevOps automates software development and deployment based on several components. When you get a modular component view of the pipeline, it becomes much easier to identify the bottlenecks and unnecessary server resources. The serverless architecture can mitigate the challenges common to the product’s deployment and development.

If you plan to become a DevOps engineer, you should learn how serverless computing can streamline overall DevOps operations. Companies prefer serverless architecture because it dramatically reduces server maintenance costs and workloads. The flexibility, reliability, cost efficiency, and speed of serverless DevOps infrastructure improve the efficiency of the complete software life cycle from development to deployment.

3. Low-Code/No-Code applications

Many organizations are adopting the low-code/no-code development approach to deploy applications swiftly and to keep up with the demand for new features and applications from users. DevOps engineers experience higher agility and can develop fast-track applications to meet the market’s demand. Adopting a low-code approach in the DevOps framework is a game changer.

With no-code application development, you can allow non-technical contributors to be part of software development through the help of visual interface integrations. As DevOps engineers play an essential role in connecting end users with developers, they play a crucial role in adopting this trend. This is another DevOps technology that you should focus on.

4. Container technology

Another technology that is evolving fast and emerging strongly is container technology. Containers are handy for sandbox applications for security or resource constraints reasons. There are also instances when companies use containers per user or user session to micro-monitor the behavior of their products. Including container technology in DevOps allows limitless possibilities to improve user and system security and enhance user analytics.

Simply put, container technology allows putting all the software components in a single unit that can be used to run microservices or larger applications alike. All the code, libraries, configuration information, and other dependencies can be withheld in this container, making it much smoother to deploy software across the network. Another reason why this DevOps technology is becoming popular is because of its lightweight architecture. Unlike deploying full-scale servers to test applications, containers make it possible to deploy multiple versions of the same application and multiple operating systems.

Conclusion

DevOps is evolving fast and driving a significant transformation in the software industry. The DevOps technologies we have listed here are helping organizations to move rapidly beyond simple automation and enhance their ability to design, produce, launch, and maintain high-quality software products. As a DevOps engineer, you must familiarize yourself with these technologies for a successful career.

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5 Necessary soft skills for DevOps engineers

Software development methodologies play an essential role in the overall quality of the product. These methodologies define the principles and practices that allow teams to work efficiently. DevOps is one such methodology that brings development and business operations teams together. It is a culture of creating one unified system that combines tasks and goals across different groups.

To ensure that the DevOps philosophy is successful with projects in real life, DevOps engineers and IT teams must work together to keep up continuous updates and deployment. This collaboration requires soft skills. The success of DevOps engineers dramatically depends on their ability to communicate and collaborate with team members to solve problems.

What are DevOps soft skills?

Soft skills are character traits that enable a person to work with others professionally. In the software industry, the technical skills get you noticed, but your soft skills make you stand out and help you advance within a team or a company.

DevOps soft skills are the traits that allow engineers to ensure team coordination and convince teammates to work towards a unified goal. Great DevOps engineers are usually excellent communicators and listeners.

Here we list some necessary soft skills for DevOps engineers. 

1. Great listening  

Listening is much more than just hearing out what others have to say. Great listeners can understand the orator’s point of view. The ability to listen might seem like a given characteristic trait, but it’s a difficult skill to master.

DevOps culture depends a lot on collaboration between teams. DevOps engineers must anticipate what other people are thinking and trying to express. Listening also implies understanding the impact, negative and positive, on the overall project by including other stakeholders’ ideas. Software development is a complicated process that includes multiple layers of communication. Troubleshooting sophisticated engineering problems is difficult, but the feeling of not being heard or understood can be highly frustrating for developers, designers, and other team members.

Develop listening skills to ensure that you understand what team members are trying to communicate.

2. Clear communication 

DevOps methodology is significantly dependent on communication. One of the core responsibilities of DevOps engineers is to ensure an environment where developers and operations team members can ask questions and share ideas comfortably. The most successful software development teams have different people with different experiences coming together and creating great products.

Communication is vital for significant product development and delivery. Excellent communication means being clear about your intentions in conversations. There must be no scope for anxiety or confusion within a DevOps team. Practice transparent communication to ensure no ambiguity in your statements that can harm the project’s success.

3. Adaptability

Adaptability is mandatory for DevOps engineers and anyone working in the software industry. As software and hardware evolve continuously, adaptation is needed to keep pace with the new frameworks, programming languages, and tools.

DevOps engineers must be prepared to invest consistently in upskilling. Adaptability is a continuous process, and you can ensure that you are adaptable by:

  • Learning a new programming language or skill extends your flexibility to work with teams. Continuous learning is one of the best ways to become adaptable.
  • Challenge yourself to work with diverse teams and make yourself comfortable about working with others. As part of a team, DevOps engineers are responsible not only for the development but also for the deployment of projects. You must move beyond technical skills to work with business operations teams.

4. Be aware of when to ask for help

A remarkable characteristic of great leaders is that they know when to reach out to others for help. As a DevOps engineer, you must know when you need assistance from supervisors. DevOps methodology encourages the balance between self-help and collaboration.

Once you have gone through all the basic troubleshooting steps and taken the necessary provisions to solve a problem on your own, escalate to a superior. There is no point in getting stuck in a situation for hours or days that can be solved with a few minutes of conversation. Too much or too little collaboration is detrimental to a project. You must try to resolve issues independently and be aware of when to ask for help.

5. Foster creativity

Outside-the-box thinking helps engineers push the limits of science and solve the most challenging problems. Creativity is one of the essential DevOps soft skills allowing professionals to experiment with different approaches and tools to solve problems within the restrictions of DevOps methodology.

Fostering creativity is vital to spark inspiration and can prove to be a great team-building soft skill. Focus on promoting collective problem-solving, developing new ideas, and brainstorming different approaches within the team.

Conclusion

DevOps is the modern culture of the IT industry that has proven to be excellently efficient at accelerating product development and deployment. To be a successful DevOps engineer, focus on these soft skills along with other technical skills. Here’s a complete guide on how software engineers can explore DevOps opportunities.

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3 DevOps implementation mistakes to avoid

DevOps is the modern software engineering culture that brings software development and IT business teams together. To deliver projects faster and maintain an efficient pipeline of future update releases, DevOps creates an ecosystem to accelerate and automate software development. It also enables more intensive software testing before releasing it.

A significant improvement seen in companies that adopt DevOps is that operation teams in such companies no longer work in isolation. DevOps engineers work at the intersection of software development and business operation infrastructures.

Given the rise in the adoption of DevOps (its market size exceeded $4 billion in 2019 and is expected to grow by 20% CAGR between 2020 and 2026), it is a promising market for software engineers. However, you have to tread carefully to maximize the benefits of DevOps; you have to avoid some implementation mistakes proactively.

Here are some common mistakes committed by DevOps engineers that you must avoid.

1. Choosing DevOps tools without thorough consideration 

You will find many DevOps tools to help improve your team’s performance. But how do you decide what tools your team requires?

Common mistake companies make when implementing DevOps is to go with the trend. For instance, Docker and Kubernetes are pretty popular in the DevOps community. IBM reports that organizations that use containers experience benefits across industries. In its study, IBM found that 78% of companies noticed improved application quality and reduced defects, 73% reported reduced application downtime, and 74% higher customer satisfaction.

Does it imply that you must implement containers as soon as possible?

No.

Hasty decisions only result in overspending on DevOps tools that teams never use.

It would be best if you focused on creating a suitable DevOps tool kit for your organization. Ask some essential questions like:

  • Is your team ready to implement the tool?
  • Do you need the DevOps tool?
  • How impactful will it be on the way the DevOps teams work?

Remember, the more complex tool you introduce, the more changes will occur and the more time and effort needed to adopt it. Do not adopt tools that can severely complicate workflows and deployment. Look for more straightforward ways to solve the problem.

2. Oversimplification

DevOps is complex on all frontiers. Whether it’s creating DevOps teams, frameworks, or strategies, sophistication is involved. As a DevOps engineer, you must not try to make an isolated team to manage the DevOps strategy. Hiring new engineers and creating a segregated team will unnecessarily add new processes, which are often lengthy and complicated.

Your focus must be on optimizing the existing DevOps teams instead of creating new ones. To create an efficient DevOps environment, processes must be streamlined to leverage the right resources to develop operational products faster. It would help if you managed all aspects related to DevOps, such as resource management, goals, budgeting, and progress tracking.

Implementing DevOps is not easy and demands a cultural overhaul. It is why you must rush or oversimplify the process. The adoption of this methodology must be in a phased and measured transition.

3. Assuming DevOps can’t be measured

Improvements can only be made when they can be measured, and DevOps is no exception. You will fail by implementing DevOps without strategizing what crucial metrics must be calculated. Accurate analysis is mandatory to determine whether or not the DevOps strategy is working for your teams. Simply put, you cannot succeed with DevOps implementation until you pay enough attention to the metrics.

Some critical DevOps metrics to assess the success of the initiatives are change lead time, deployment frequency, and mean time to recovery (MTTR). The deployment frequency is the primary keyword here as it shows how fast the code completes its journey from the organization to the production. It is an indicator that shows how efficient your DevOps teams are. Change lead time measures the lead time for code changes from the start of the deployment cycle until it is released. MTTR is the recovery time measure that shows how much time DevOps teams take on average to restore service, components, or systems after an outage.

The goal of any DevOps team is to reduce the MTTR. By measuring crucial metrics, you are in a better position to evaluate and introduce adjustments that can further improve the efficiency of DevOps teams.

Conclusion 

You will create an efficient production pipeline for frequent software deployment as a DevOps engineer. To deliver, you must overcome the challenges of implementing the DevOps methodology. Above listed common mistakes can be easily avoided as long as you are aware of their existence. Through DevOps, any organization can enjoy greater agility, improved employee morale, and better customer satisfaction.

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6 key differences between DevOps & Agile Culture

As advanced software development practices become a norm in the modern world, software development methodologies’ evolution has become mandatory. Initially, the waterfall method focused on delivering software products based on a pre-defined timeline. Here the approach was to focus more on product delivery. 

However, as the software products became more complex, there was a need for a methodology to optimize the software development life-cycle or SDLC. This gave rise to the agile software development methodology. But today, another methodology that unites the development and operations in a single team is the DevOps software development methodology.

Both Agile and DevOps are paving the way for modern software development. To determine which methodology best suits your software development requirements, it is essential to understand the differences between Agile and DevOps.

This article explains the critical differences between these two software development methodologies.

DevOps and Agile have their strengths and are reliable in their ways. Let’s understand both methodologies in detail.

 

What is DevOps?

DevOps software development methodology focuses on enhancing the communication, integration, and collaboration between developers and other IT professionals to accelerate the development of products.

The terminology derives its name from development and operations. The primary objective is to create a culture that promotes collaboration between development teams and operations management. The DevOps methods have proven to automate code deployment by making production faster. Organizations use this methodology to speed up the delivery of applications and services. At its core, DevOps culture creates an environment that leads to the alignment of the development and IT operations.

 

What is Agile? 

Agile methodology is based on the continuous iteration of development and testing within the software development life-cycle process. This culture emphasizes iterative, incremental, and evolutionary improvement of the software development process. Instead of focusing on collaboration between development and operation, the goal is to break the product development into smaller pieces. Then during the development of these smaller tasks, test cases are integrated into the process to make final testing much more efficient.

There are several ways to implement Agile methodologies, such as Scrum, Kanban, and XP.

 

Main differences between the DevOps and Agile software development methodologies 

DevOps Agile Methodology
Guiding Principle The DevOps culture is based on the collaboration between development and operations teams working as a single unit. The aim is to cultivate productivity and establish collaboration. Agile culture is more focused on managing and continuously delivering smaller, incremental project changes. Iterative development and ongoing testing are an integral part of Agile software development. 
Primary Goal DevOps methodology emphasizes rapid deliveries and continuous testing. Here the deliveries are made every few hours or daily as the primary goal is to manage end-to-end engineering and business solutions and enable faster delivery of products.  Here the primary focus is on constant changes and incremental developments made to the software product after each sprint. Agile methodology is usually used for complicated projects that require frequent mid-project changes. 
Delivery & Documentation  DevOps methodology is usually used for prebuilt software that is ready to release. It looks after deploying the software securely and faster. Documentation is another essential requirement of DevOps. Emphasis on documenting processes, communications, and product updates is much higher here. Unlike DevOps, Agile methodology is used to build and create software and plays no role in the deployment of the software. In Agile methodology, more importance is put on well-working software over comprehensive documentation. This is why documentation in this culture is not as comprehensive as DevOps.
Team Size & Skills  As DevOps methodology functions on integrating different teams, here, the number of people involved is much larger. DevOps culture requires people with different specialization and functional skill sets to achieve better goals and product delivery success.  Agile works with a smaller team for faster execution of the end goals. When fewer people are involved in software development, they perform at different levels and become highly skilled all-rounder developers. This methodology creates highly experienced developers for an organization.
Feedback Structure DevOps continuously works on bridging the gap between the developer and IT operations team. In DevOps culture, internal communication channels are established to address concerns and improve the speed and quality of product development.  Agile culture bridges the communication gap between a customer, and the development and testing teams. Here no operations team is involved. Feedback is directly received from customers, and adjustments to the product are made accordingly. 
Communication Methodology Design and specification documents are used for communication between teams.  Agile uses the Scrum framework for communication. Daily scrum meetings are scheduled to discuss briefs and the status of various tasks.

 

Using DevOps & Agile together

Most software development teams already use Agile methods like Scrum to guide product development. However, some proponents believe that DevOps is an extension of Agile. Typically, an Agile culture relies on cross-functional teams, including a developer, a tester, and a designer. 

DevOps takes Agile a step further by adding an operations member. This eases the transition from software development to deployment. You can automate processes and enhance transparency across all teams with DevOps. 

Takeaway 

DevOps and Agile methodologies have well-defined differences, but their overall goal is to speed up the delivery of quality software. Fortunately, both can co-exist and significantly improve the product quality and SDLC when implemented. 

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How AI is changing DevOps

While humans are capable of many unthinkable and often impossible feats, there are limitations. One particular area is in data handling, its management, analysis, and interpretation. With the sheer magnitude of incoming raw data, thanks to rapid digitalization, adoption of automation, and several other such factors, relying on human resources alone is inefficient. In a field like DevOps, where high efficiency, accuracy, and speed are all underpinning elements, it seems only natural that artificial intelligence models and technology are a perfect fit. 

In fact, DevOps is now a critical piece of the enterprise IT environment, and both AI and ML are readily adopted to increase efficiency. Around 75% use AI and ML for testing and in some cases, this has caused an uptick in new code released by nearly 10X! Considering the nature of the job, it would be near-impossible to do it without the use of advanced technologies. Moreover, AI improves several key DevOps practices and allows teams to leverage their potential to the maximum. It reduces waste by handling large computing scenarios that would otherwise overwhelm human-led operation cycles.

However, recently AI’s application in the DevOps space has brought about rapid change. Several new advancements are afoot, and you should know about them to stay ahead. Read on to know how AI is transforming DevOps.

Improved security and tracking capabilities

A key prowess of AI and ML models is their ability to analyze volumes of data and do it at remarkable speeds with accuracy. As such, its application in tracking and security is undeniable. In a DevOps setting, this advantage comes in handy when analyzing threats and usage data for better optimizations. Since models can be designed to inspect and track user data at every touchpoint, DevOps teams can leverage to deliver a better user experience simply based on data collected by modules doing the heaviest lifting. 

On to security, improved tracking capabilities can pinpoint areas in systems that are most vulnerable to breaches. AI better defense against DDOS attacks and can even lend a hand in fraud detection. Since AI models can be designed to spot unusual data patterns and can do this in real-time, tracking and stopping fraudulent acts is a lot more reliable.

Increased reliance on automation 

DevOps inherently includes complex systems, run on distributed application environments and operational models. Keeping up and effectively absorbing information isn’t a simple undertaking, especially if it is completely manual. While DevOps does bring some level of automation to the table, AI betters it in every way. With AI, DevOps professionals can leverage the full power of automation to streamline tedious tasks in the operational cycle that were otherwise error-prone due to human involvement.

In fact, this reliance on AI-backed automation has gone so far as to institute systems that can self-heal without requiring external intervention. Essentially, AI can not only jump into the driver’s seat but can get the team there too! However, stubborn DevOps teams are resilient to this level of independent function, but it is only a matter of time until it becomes standard practice.  

Swifter, more reliable pattern and anomaly detection

Complex application systems bring with them another problem area — error tracking and analysis. For instance, in an IoT environment with several microservices in use along with its numerous touchpoints, pinpointing failures with accuracy and speed simply isn’t possible. There are troves of raw data to go through and AI models can easily handle these without nearly as much effort. ML and AI models can be designed to look for abnormalities that trigger failure events and even suggest optimizations. 

Naturally, when exposed to such analysis over time, these models can be trained to recognize patterns and employ predictive analysis. This lends itself to anomaly detection, which can be vital to DevOps cycles as teams can then address these directly in the development pipeline and ensure they make it out to the live version. 

Better and refined access to data

An ever-present challenge to DevOps is the lack of access to complex data streams. With data gushing through every stream and data point in the system, professionals often struggle to keep up. AI models can address such a problem and deliver refined data from various sources and collate them for easy access. In fact, data trapped in siloed operations can also be harnessed to ensure that insight are meaningful.

Enhanced resource management

Certain AI tools lend themselves to non-technical business users, normally in charge of operations. With AI, data mapping and integration can be simplified, and this frees up the IT department for more high-value tasks. In fact, with better data integration, teams are in a better position to innovate, customize, optimize, and grow as the mundane and uninspiring tasks are no longer taking away time and effort. While it may seem like it could result in reduced collaboration, this isn’t the case. Unburdened by technical challenges, the focus is shifted to more big-picture undertakings, which often involve teams across departments to collaborate.

The integration of AI into DevOps is the natural cycle of advancement within the field of software development. There was a time when the traditional development life cycle distinguished development from operations, but in just a few years, it evolved into what we now know as DevOps. Similarly, a new acronym, AIOps, is taking hold and in this new model, the ‘Developer’ is no longer part of the equation. Is this the future of AI? It may still be too soon to tell, but developers would do well to upskill and branch out into other fields that work in line with this technology.

Those with skills that can leverage AI and drive development are sure to find themselves amongst the changemakers in the industry.

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