In today’s software development world, containerization has revolutionized the way applications are built, deployed, and managed. Docker is the most popular containerization platform, enabling developers and organizations to create, run, and manage applications in isolated environments. It eliminates the traditional “works on my machine” problem by ensuring consistency across multiple environments.

Docker is widely used in DevOps, CI/CD pipelines, microservices architectures, cloud-native development, and more. This blog will explore what Docker is, its top use cases, key features, architecture, installation process, and a step-by-step guide to getting started.

What is Docker?

Docker is an open-source platform that allows developers to build, package, and run applications in lightweight, portable containers. A Docker container is a standardized unit of software that includes everything needed to run an application: code, runtime, libraries, dependencies, and configurations.

Why Use Docker?

• Portability: Containers can run on any system with Docker installed.
• Consistency: Ensures identical application behavior across environments (development, testing, production).
• Isolation: Runs applications in isolated environments, avoiding conflicts.
• Efficiency: Uses fewer resources compared to traditional virtual machines (VMs).

How Docker is Different from Virtual Machines (VMs)?

Docker makes application deployment faster, more scalable, and cost-effective by simplifying how software is packaged and shipped.

Top 10 Use Cases of Docker

1. Application Containerization

Docker encapsulates applications with all their dependencies, ensuring they run identically in any environment.

2. Microservices Architecture

Docker is ideal for breaking down monolithic applications into microservices, allowing teams to develop, deploy, and scale components independently.

3. Continuous Integration & Continuous Deployment (CI/CD)

Docker integrates seamlessly with Jenkins, GitHub Actions, GitLab CI/CD, enabling fast, automated software releases.

4. Cloud-Native Development

Docker works across AWS, Azure, Google Cloud, making it easy to deploy applications in hybrid, multi-cloud, and Kubernetes environments.

5. Simplified Development and Testing

Developers can use Docker Compose to create isolated development environments, reducing conflicts between dependencies.

6. Automated Scaling & Orchestration

With Docker Swarm or Kubernetes, applications can be automatically scaled up or down based on demand.

7. Big Data and Machine Learning

Docker simplifies the deployment of AI/ML frameworks like TensorFlow, PyTorch, and Apache Spark by packaging dependencies in containers.

8. Edge Computing and IoT

Docker runs lightweight containers on IoT devices and edge servers, optimizing compute resources.

9. Database Containerization

Databases like MySQL, PostgreSQL, and MongoDB can be containerized, making them easier to manage and scale.

10. Legacy Application Modernization

Organizations can move legacy applications into containers without rewriting the entire codebase, extending their lifespan.

What Are the Features of Docker?

1. Containerization

Docker provides an efficient way to package and isolate applications along with their dependencies.

2. Portability

Containers run consistently across different environments, from a developer’s laptop to production servers.

3. Version Control & Rollbacks

Docker allows versioning of images, enabling easy rollbacks to previous states.

4. Lightweight & Fast

Containers use less CPU and memory compared to traditional VMs.

5. Multi-Platform Support

Docker runs on Windows, macOS, Linux, and cloud environments.

6. Security & Isolation

Each container runs in its own isolated environment, improving security.

7. Docker Compose

Defines and runs multi-container applications using a simple YAML configuration file.

8. Built-in Networking

Docker allows seamless container-to-container communication.

9. Integration with DevOps Tools

Supports popular tools like Kubernetes, Terraform, Jenkins, GitHub Actions.

10. Scalable and Flexible

Works in both single-node setups and large-scale deployments with Kubernetes.

How Docker Works and Architecture

1. Docker Engine

• The core of Docker, is responsible for running containers.
• Consists of Docker Daemon, CLI, and REST API.

2. Docker Images

• Read-only templates that define how containers should run.
• Created using Dockerfiles.

3. Docker Containers

• Running instances of Docker Images.

4. Docker Hub

• Public/private registry to store and share Docker images.

5. Docker Compose

• Tool for managing multi-container applications.

6. Container Orchestration

• Docker Swarm and Kubernetes help manage large-scale container deployments.

How to Install Docker

Installing Docker on Linux

sudo apt update
sudo apt install docker.io -y
sudo systemctl start docker
sudo systemctl enable docker

Installing Docker on macOS (via Homebrew)

brew install --cask docker

Installing Docker on Windows

1. Download Docker Desktop from Docker’s official website.
2. Follow the installation wizard and restart your system.

Verify Docker Installation

docker --version
docker run hello-world

Basic Tutorials of Docker: Getting Started

1. Running a Simple Container

docker run -d -p 8080:80 nginx

• This pulls and runs an Nginx web server container on port 8080.

2. Listing Running Containers

docker ps

3. Stopping a Running Container

docker stop <container_id>

4. Removing a Container

docker rm <container_id>

5. Pulling an Image from Docker Hub

docker pull mysql

6. Creating a Custom Docker Image

1. Create a Dockerfile: FROM python:3.9 COPY app.py /app/app.py WORKDIR /app CMD ["python", "app.py"]
2. Build and run the image: docker build -t my-python-app . docker run my-python-app

7. Running Multiple Containers with Docker Compose

1. Create docker-compose.yml: version: '3' services: web: image: nginx ports: - "8080:80" database: image: postgres
2. Run with: docker-compose up -d

8. Viewing Container Logs

docker logs <container_id>

9. Executing Commands Inside a Running Container

docker exec -it <container_id> bash

10. Removing All Containers and Images

docker system prune -a

The post What is Docker and Use Cases of Docker? appeared first on Artificial Intelligence.

As modern applications become more complex and distributed, managing containerized workloads efficiently is critical for scalability, reliability, and performance. Kubernetes, often abbreviated as K8s, is the industry-leading open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Originally developed by Google and now maintained by the Cloud Native Computing Foundation (CNCF), Kubernetes has become the standard for managing cloud-native applications.

Kubernetes eliminates many of the challenges associated with manually deploying and managing containers across distributed environments. It provides organizations with the agility, flexibility, and automation required to run containerized applications seamlessly across on-premises, cloud, and hybrid environments.

In this blog, we will explore what Kubernetes is, its top use cases, features, architecture, installation process, and a step-by-step guide to getting started.

What is Kubernetes?

Kubernetes is an open-source container orchestration platform designed to manage containerized workloads and services. It provides automation for deployment, scaling, networking, and storage for applications running in containers.

Key Characteristics of Kubernetes:

• Automated container orchestration: Eliminates manual efforts in deploying and managing containers.
• Self-healing capabilities: Restarts failed containers and reschedules workloads automatically.
• Scalability: Allows horizontal scaling of applications based on demand.
• Multi-cloud compatibility: Runs on AWS, Azure, Google Cloud, and on-premises environments.
• Declarative Configuration: Uses YAML files to define infrastructure as code (IaC).

Why Kubernetes?

Before Kubernetes, organizations relied on traditional virtual machines (VMs) or bare-metal servers, leading to resource inefficiencies. Kubernetes provides an efficient way to deploy, manage, and scale applications without worrying about infrastructure constraints.

With Kubernetes, developers can: ✔ Deploy applications faster
✔ Scale up or down automatically
✔ Manage application failures with self-healing mechanisms
✔ Optimize resource usage

Top 10 Use Cases of Kubernetes

1. Container Orchestration

Kubernetes automates container deployment, management, and scaling, reducing manual intervention in distributed applications.

2. Microservices Management

Kubernetes simplifies the management of microservices-based applications, ensuring seamless communication between services and optimizing resource allocation.

3. Hybrid and Multi-Cloud Deployments

With Kubernetes, businesses can run applications across multiple cloud providers (AWS, Azure, GCP) and on-premises environments with minimal configuration changes.

4. Auto-Scaling Applications

Kubernetes automatically scales applications up or down based on CPU, memory, or custom-defined metrics using the Horizontal Pod Autoscaler (HPA).

5. CI/CD Automation for DevOps

Kubernetes integrates with Jenkins, GitLab CI/CD, and ArgoCD to enable continuous integration and continuous deployment (CI/CD) pipelines.

6. Big Data & AI/ML Workloads

Kubernetes manages Big Data analytics, AI/ML model training, and processing using frameworks like TensorFlow, Apache Spark, and Jupyter notebooks.

7. Serverless Computing

With Kubernetes-based serverless frameworks like Knative and OpenFaaS, developers can run event-driven applications without managing infrastructure.

8. Disaster Recovery and High Availability

Kubernetes ensures fault tolerance by automatically replacing failed containers and replicating workloads across multiple nodes for high availability.

9. IoT and Edge Computing

Kubernetes is used for deploying containerized workloads on IoT devices and edge environments, ensuring seamless operation across distributed systems.

10. Multi-Tenant SaaS Applications

Kubernetes supports multi-tenancy, allowing SaaS providers to run multiple customer applications in an isolated and secure environment.

What Are the Features of Kubernetes?

Kubernetes provides a robust set of features that make it a powerful container orchestration platform:

1. Automated Deployments and Rollbacks

• Kubernetes enables rolling updates and rollbacks, ensuring smooth deployment without downtime.

2. Self-Healing Mechanism

• Automatically restarts failed containers.
• Replaces unhealthy nodes or pods.
• Reschedules workloads to healthy nodes.

3. Horizontal & Vertical Scaling

• Horizontal Pod Autoscaler (HPA) dynamically scales applications based on demand.
• Vertical Pod Autoscaler (VPA) adjusts resource allocations for efficient CPU and memory usage.

4. Load Balancing and Service Discovery

• Kubernetes provides built-in service discovery and load balancing through Services and Ingress controllers.

5. Multi-Cloud and Hybrid Support

• Run workloads across on-premises, cloud, and hybrid environments seamlessly.

6. Secrets and Config Management

• Kubernetes securely manages secrets, environment variables, and configuration data.

7. Networking and Service Mesh

• Supports Kubernetes-native networking, enabling seamless communication between containers.
• Works with Istio, Linkerd, and Consul for service mesh implementation.

8. Persistent Storage Management

• Integrates with AWS EBS, Azure Disks, Google Persistent Disks, and on-prem storage.

9. Role-Based Access Control (RBAC)

• Implements fine-grained access controls for securing cluster resources.

10. Observability and Monitoring

• Works with Prometheus, Grafana, and ELK Stack for monitoring and logging.

How Kubernetes Works and Architecture

Kubernetes Architecture Overview

Kubernetes follows a master-worker node architecture to manage containers efficiently.

1. Master Node (Control Plane)
   • API Server: Manages communication between components.
   • Scheduler: Assigns workloads to worker nodes.
   • Controller Manager: Manages cluster state and ensures desired configurations.
   • etcd: Stores cluster configuration and metadata.
2. Worker Nodes
   • Kubelet: Agent running on each node to manage container execution.
   • Kube Proxy: Handles network communication.
   • Container Runtime (Docker/Containerd): Runs containerized applications.
   • Pods: The smallest deployable unit containing one or more containers.

How to Install Kubernetes

Kubernetes can be installed in multiple ways, including Minikube, kubeadm, managed Kubernetes (EKS, AKS, GKE), or on-prem setups.

Installing Kubernetes using Minikube (For Local Development)

Step 1: Install Minikube

curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
sudo install minikube-linux-amd64 /usr/local/bin/minikube

Step 2: Start Kubernetes Cluster

minikube start

Step 3: Verify Kubernetes Installation

kubectl cluster-info
kubectl get nodes

Installing Kubernetes using kubeadm (For Production)

Step 1: Install kubeadm, kubectl, and kubelet

sudo apt update && sudo apt install -y kubeadm kubelet kubectl

Step 2: Initialize Kubernetes Cluster

sudo kubeadm init

Step 3: Configure kubectl

mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo chown $(id -u):$(id -g) $HOME/.kube/config

Step 4: Join Worker Nodes

kubeadm join <master-node-ip>:6443 --token <token> --discovery-token-ca-cert-hash sha256:<hash>

Basic Tutorials of Kubernetes: Getting Started

1. Deploying a Sample Application

kubectl create deployment nginx --image=nginx
kubectl expose deployment nginx --port=80 --type=NodePort

2. Scaling Applications

kubectl scale deployment nginx --replicas=5

3. Viewing Running Pods

kubectl get pods -o wide

4. Deleting a Deployment

kubectl delete deployment nginx

The post What is Kubernetes and Its Use Cases? appeared first on Artificial Intelligence.

Introduction

Docker components are the fundamental building blocks of Docker, a platform for building, running, and managing containerized applications. These components work together to provide a consistent and efficient way to develop and deploy applications across different environments.

The components in Docker are categorized into two groups:

• Basic
• Advanced

Basic Docker Components:

1. Docker Client: The Docker client is a command-line tool that allows users to interact with the Docker daemon and manage Docker objects, such as images, containers, and volumes. It provides a user-friendly interface for executing Docker commands and managing Docker environments.
2. Docker Daemon: The Docker daemon, also known as the Docker engine, is the backend service that manages Docker containers. It listens for instructions from the Docker client and executes them, creating, running, and managing containers according to the user’s requests.
3. Docker Image: A Docker image is a read-only template that contains the instructions for creating a Docker container. It includes the operating system, application code, dependencies, and configuration files necessary to run the application.
4. Docker Container: A Docker container is a running instance of a Docker image. It is an isolated and self-contained environment that packages the application and its dependencies, allowing it to run consistently across different environments.
5. Docker Registry: A Docker registry is a centralized location for storing and sharing Docker images. It allows users to pull and push images to make them available for use in different environments.
6. Docker Networking: Docker networking facilitates container communication and comprises five primary network drivers described below.

• None: Disabling the networking system prevents container connectivity with others.
• Bridge: This default driver links multiple containers to the same Docker host.
• Host: For scenarios where container-host isolation isn’t needed, the host network driver removes this isolation.
• Overlay: The overlay network driver enables communication among various swarm services across different hosts.
• macvlan: By assigning a MAC address and directing traffic through it, the macvlan driver makes a container appear as a physical device.

Advanced Docker Components:

• Docker Compose: Docker Compose is a tool for defining and running multi-container Docker applications. It allows users to define the services that make up an application, their dependencies, and how they should be connected together.
• Docker Swarm: Docker Swarm is a clustering tool for managing multiple Docker hosts as a single swarm. It allows users to deploy and manage applications across multiple hosts, providing scalability and fault tolerance.
• Docker Buildx: Docker Buildx is a tool for building Docker images. It provides advanced features for building images, such as support for multi-stage builds, caching, and parallel builds.
• Docker Content Trust: Docker Content Trust is a security feature that allows users to verify the integrity and authenticity of Docker images. It uses cryptographic signatures to ensure that images have not been tampered with.
• Docker Desktop: Docker Desktop is a bundled Docker environment for macOS, Windows, and Linux. It includes the Docker client, daemon, and other tools, making it easy to get started with Docker on local machines.

The post What are the components of Docker and their types? appeared first on Artificial Intelligence.

IT certifications have always been playing a vital role in getting a job or required knowledge.

In an interview, if you have a certification, you have more advantages to get the job and I have experienced it personally.

There are lots of other channels as well to learn or to enhance the knowledge and skills these days but the thing which matters a lot is the certification, and no one can give a certified degree instead of an institute, and like the way things are evolving the demand of certification is getting increased as they need an expert for their work.

So having knowledge before going to ask for the job is much beneficial to you.

So today I am going to share the top 10 high-paying IT certifications in the world in 2022. So let’s begin.

Master in DevOps engineering (MDE) certification – This certification gives you entire information about DevOps and their related toolsets.

DevOps is just a process to be followed to achieve a high quality of software by continuous integration and continuous delivery, and their open-source tools help it to achieve the goal efficiently and effectively.

Basically, DevOps only direct the way but the major works are done by these toolsets and you can have the proper knowledge and skills by only getting trained in any institute and have the completion certification.

The demand for certification is getting higher to get a good job role in the IT sector.

DevOps is liable to do the planning, designing, coding, testing, deploying, and monitoring.

As DevOps has shown its capability the salary of candidates will be more in 2022 and will be continued.

Site reliability engineering (SRE) certification – SRE is also one of the important certifications. SRE is mainly focused on operations where the goal of SRE is to improve the reliability of software systems, through automation and continuous integration and delivery.

SRE has also open-source toolsets that cover during the certification. SRE has shown tremendous growth till now and getting used all over the world.

It’s expecting the demand of SRE would be consistent and will be on a high-paying salary list.

SRE is for those software engineers who want to work as an operation team.

DevSecOps certified professional certification – It had been forecasted to be achieved a growth of 33.7% during the period of 2017-2023. And even it has been seen the growth in the market.

So as per the result, it will dominate the market in 2022 as well.

The national average salary for a Devsecops Engineer is Rs 10,00,000 in India.

DevSecOps course is for security professionals who are willing to work in the security field like cyber security.

DevSecOp’s assumption is security is everyone’s priority and everyone should work by keeping security concerns in mind. DevSecOps also works in the collaboration with DevOps.

Docker Certified Associate (DCA) certification – Docker is a containerization tool that creates containers and allows to build, test and deploy applications.

This certification helps to learn how Docker is used to package and ship the app as well as how to create containers and so many things.

Docker has become the number 1 choice of all companies and its demand is high.

The average salary of Docker candidates in India is Rs 4,79, 074 to Rs 8,14,070, and in the USA $1,45000.

Being a Docker certified candidate is much important to get a job.

Certified Kubernetes Administrator (CKA) Certification – It has been seen CKA course is at the top to get the certification into. Kubernetes are much important to organize the containers. So Kubernetes certification is important as here you will learn so many things and most significantly how to integrate with Docker to work with.

Kubernetes has already shown its growth as it is in demand at all companies.

Kubernetes candidates can earn salaries up to 6 to 8 lakh in India and in USA between $92,500 and $147,500 per year as per a new report.

AIOps Certified Professional (AIOCP) certification – AIOps stands for artificial intelligence for operations drive automation to solve the issues by speed analyzing the root cause of the issue and taking care of the events with any human interruption.

AIOps is now trending to market and is achieving heights of success. So the growth of AIOps is getting really good and opening so many job roles in AI.

AIOps certification is very important to get into this job domain as certification can grow your chances more to pass the interview and to get the full knowledge.

Based on research the average salary of AIOps is 21 lakh per annum in India.

Master in artificial intelligence – The AI is future and there is no doubt the candidates who are trying to achieve mastery in AI studies have a great future.

Certification is playing a key role here to get your foot into the AI world.

Having good knowledge and the advantage to get the priority in an interview is not so bad. This is the advantage of certifications.

The average salary of AI in the USA is $164, 769 and in India Rs 9,01,800 per annum.

AI is the main driver of emerging technologies like big data, robotics, and IoT.

GitOps certification – GitOps is a set of practices to manage infrastructure and application configurations using Git.

Gitops uses Git as the main repository for managing all the information, documentation. It maintains infrastructure as code and keeps them too in Git.

Some developers believe Gitops is the future of DevOps that replace the Developer part with a single repository that grasps all the information needed by a developer. 

That’s why GitOps certification is important.

GitOps employee’s salary is also high according to experience. One candidate has 45 lakh per annum.

The job openings are also in good numbers to apply.

MlOps certification – Mlops is communication between data scientists and the operation or production team, it is deeply collaborative in nature, designed to eliminate waste automate as much as possible, and produce richer and consistent insights through machine learning.

Mlops is the major function of machine learning engineering.

Mlops Goals –

• faster experimentation and model development
• faster deployment of the updated model into production
• Quality assurance.

The salary for an MLOps Engineer in India is approx Rs 11,40,000 per annum.

It has been predicted to be more job openings in 2022 and the certification is a must to get the job as certification can give you an advantage during an interview.

Its shows at least you have such knowledge pertaining to this and you are trained.

DataOps certification – As per Andy Palmer “DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals”.

The aim of DataOps is to quickly deliver business value from data.

The DatOps engineer’s salary in India is Rs 7,78,290 and  $92,468 in the United States per annum.

It is predicted, to improve data quality and reduce time to insight, enterprises will increasingly embrace DataOps practices across the data life cycle in 2022.

The certification will play a vital role here to get the job as Dataops is new and also it has so many scenarios to cover so certification is a must.

And it has always been seen certifications always give an advantage during an interview. That means certification increase the status of your knowledge as well as your resume.

                      Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

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Container tools and security fixes for Kubernetes cluster orchestrator continued to be rolled out as the microservices ecosystem evolves.

With Kubernetes security concerns growing as deployments scale, container security partners NeuVector and Sonatype this week released centralized container defenses along with open source software security tools. The goal of the integration is providing greater visibility into open source risks associated with Kubernetes and containers deployments.

Elsewhere, Mirantis, the company that acquired container pioneer Docker’s enterprise offerings last November, rolled out a cloud-based container platform designed to ship code faster on public clouds and internal infrastructure. Along with application portability, the Docker tool also eases use of Kubernetes for developers and operators across public and private cloud.

Mirantis said its container cloud enables multi-cloud deployments across public and private platforms along with bare metal. Other capabilities include multi-cluster management and on-demand, self-service clusters. Meanwhile, continuous software updates and lifecycle management across stacks can be automated using the tools.

“Unlike lock-in solutions like IBM/Red Hat and VMware that force you to deploy their rigid stack, Container Cloud empowers you to deploy your own multi-cloud everywhere,” asserted Adrian Ionel, CEO and co-founder of Mirantis.

The container cloud is available free for up to three clusters totaling 15 nodes. Annual subscriptions are required for larger, enterprise deployments, the company said this week.

The container and Kubernetes security tools also released this week reflect the growing number of companies running those microservices, making them an inviting target for hackers. “Kubernetes and containers are just as vulnerable to attacks and exploits from hackers and insiders as traditional environments, making streamlined security critical to all enterprises,” partners NeuVector and Sonatype said in releasing their integrate container security platform.

“Customers need a holistic approach to analyze, monitor and track the contents and runtime configurations of their containers to realize risk,” said Brian Fox, CTO and co-founder of Sonatype.

“End-to-end container threat visibility and protection is vital to defending enterprises’ micro-perimeters from increasingly sophisticated attacks and to ensure regulatory compliance,” added NeuVector CTO Gary Duan.

The partners said the container security platform would allow DevSecOps teams to inject security policies as code in order to secure production workloads.

Container security and tool vendors note the growing velocity and scale of container-based workloads along with the vulnerabilities associated with runtimes and unsecured image registries. Recent industry studies reveal that half the companies surveyed said they are running 250 or more containers.

That scaling has generated ease-of-use tools like the Mirantis container cloud along with heighten awareness of security vulnerabilities.

The post List of Kubernetes Tools, Defenses Grows appeared first on Artificial Intelligence.

The history of computing is punctuated by a set of seismic shifts in enterprise IT architectures.  

Monolithic, highly integrated applications moved to integrated software stacks and N-tier architectures. Distributed computing has also gone through several incarnations. There have been multiple attempts at standardising inter-application communications, such as remote procedure calls on Unix, distributed object model, common object request broker architecture and web services. All have tried to promote code reuse, and publish and share application programming interfaces (APIs) in a bid to avoid programmers having to “reinvent the wheel”. 

Since the mid 2000s, thanks to the growth of JavaScript on servers and Java application servers, service-oriented architecture (SOA) emerged as the new enterprise integration champion. Like its predecessors, this blueprint for distributed computing was engineered before the era of cloud-native computing. Companies built in the cloud have taken a very different approach, based around the idea of containers and loosely coupled microservices. 

Now, thanks to the success of Docker and Kubernetes, more businesses are looking at deploying containers. The reason for the popularity of this approach is that it helps businesses develop cloud-native applications, which can be delivered quickly to power digital transformation initiatives.  

Forrester’s Now tech: enterprise container platforms, Q2 2018 report notes: “Container-centric, microservice-oriented, dynamically orchestrated cloud-native technologies help firms create highly differentiated apps and services that create compelling customer experiences. They’ve quickly become important elements of digital business transformation as they promise to speed software delivery and improve scale, resiliency, flexibility and implementation.” 

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Microservices have been a focus across the open source world for several years now. Although open source technologies such as Docker, Kubernetes, Prometheus, and Swarm make it easier than ever for organizations to adopt microservice architectures, getting your team on the same page about microservices remains a difficult challenge.

For a profession that stresses the importance of naming things well, we’ve done ourselves a disservice with microservices. The problem is that that there is nothing inherently “micro” about microservices. Some can be small, but size is relative and there’s no standard measurement unit across organizations. A “small” service at one company might be 1 million lines of code, but far fewer at another organization.

Some argue that microservices aren’t a new thing at all, rather a rebranding of service-oriented architecture (SOA), whereas others view microservices as an implementation of SOA, similar to how Scrum is an implementation of Agile. (For more on the ambiguity of microservice definitions, check out this upcoming book Microservices for Startups.)

How do you get your team on the same page about microservices when no precise definition exists? The most important thing when talking about microservices is to ensure that your team is grounded in a common starting point. Ambiguous definitions don’t help. It would be like trying to put Agile into practice without context for what you are trying to achieve or an understanding of precise methodologies like Scrum.

Finding common ground

Knowing the dangers of too eagerly hopping on the microservices bandwagon, a team I worked on tried not to stall on definitions and instead focused on defining the benefits we were trying to achieve with microservices adoption. Following are the three areas we focused on and lessons learned from each piece of our microservices implementation.

1. Ability to ship software faster

Our main application was a large codebase with several small teams of developers trying to build features for different purposes. This meant that every change had to try to satisfy all the different groups. For example, a database change that served only one group had to be reviewed and accepted by other groups that didn’t have as much context. This was tedious and slowed us down.

Having different groups of developers sharing the same codebase also meant that the code continually grew more complex in undeliberate ways. As the codebase grew larger, no one on the team could own it and make sure all the parts were organized and fit together optimally. This made deployment a scary ordeal. A one-line change to our application required the whole codebase to be deployed in order to push out the change. Because deploying our large application was high risk, our quality assurance process grew and, as a result, we deployed less.

With a microservices architecture, we hoped to be able to divide our code up so different teams of developers could fully own parts. This would enable teams to innovate much more quickly without tedious design, review, and deployment processes. We also hoped that having smaller codebases worked on by fewer developers would make our codebases easier to develop, test, and keep organized.

2. Flexibly with technology choices

Our main application was large, built with Ruby on Rails with a custom JavaScript framework and complex build processes. Several parts of our application hit major performance issues that were difficult to fix and brought down the rest of the application. We saw an opportunity to rewrite these parts of our application using a better approach. Our codebase was intertangled, which make rewriting feel extremely big and costly.

At the same time, one of our frontend teams wanted to pull away from our custom JavaScript framework and build product features with a newer framework like React. But mixing React into our existing application and complex frontend build process seemed expensive to configure.

As time went on, our teams grew frustrated with the feeling of being trapped in a codebase that was too big and expensive to fix or replace. By adopting microservices architecture, we hoped that keeping individual services smaller would mean that the cost to replace them with a better implementation would be much easier to manage. We also hoped to be able to pick the right tool for each job rather than being stuck with a one-size-fits-all approach. We’d have the flexibility to use multiple technologies across our different applications as we saw fit. If a team wanted to use something other than Ruby for better performance or switch from our custom JavaScript framework to React, they could do so.

3. Microservices are not a free lunch

In addition to outlining the benefits we hoped to achieve, we also made sure we were being realistic about the costs and challenges associated with building and managing microservices. Developing, hosting, and managing numerous services requires substantial overhead (and orchestrating a substantial number of different open source tools). A single, monolithic codebase running on a few processes can easily translate into a couple dozen processes across a handful of services, requiring load balancers, messaging layers, and clustering for resiliency. Managing all of this requires substantial skill and tooling.

Furthermore, microservices involve distributed systems that introduce a whole host of concerns such as network latency, fault tolerance, transactions, unreliable networks, and asynchronicity.

Setting your own microservices path

Once we defined the benefits and costs of microservices, we could talk about architecture without falling into counterproductive debates about who was doing microservices right or wrong. Instead of trying to find our way using others’ descriptions or examples of microservices, we instead focused on the core problems we were trying to solve.

• How would having more services help us ship software faster in the next six to 12 months?
• Were there strong technical advantages to using a specific tool for a portion of our system?
• Did we foresee wanting to replace one of the systems with a more appropriate one down the line?
• How did we want to structure our teams around services as we hired more people?
• Was the productivity gain from having more services worth the foreseeable costs?

In summary, here are five recommended steps for aligning your team before jumping into microservices:

1. Learn about microservices while agreeing that there is no “right” definition.
2. Define a common set of goals and objectives to avoid counterproductive debates.
3. Discuss and memorialize your anticipated benefits and costs of adopting microservices.
4. Avoid too eagerly hopping on the microservices bandwagon; be open to creative ideas and spirited debate about how best to architect your systems.
5. Stay rooted in the benefits and costs your team identified.

Focus on making sure the team has a concretely defined set of common goals to work off. It’s more valuable to discuss and define what you’d like to achieve with microservices than it is to try and pin down what a microservice actually is.

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