Tag: Scalability

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Getting Started with Cloud-Native App Development: Best Practices, Learning Materials, and Videos to Watch

As a new engineer, understanding the concept of cloud-native app development is important for several reasons.

First, cloud-native app development is a key aspect of building and deploying applications in the cloud. It is the practice of using technologies, tools, and best practices that are designed to work seamlessly with cloud environments. By understanding how cloud-native app development works, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, cloud-native app development allows for better scalability and cost efficiency. By using technologies, tools, and best practices that are designed to work seamlessly with cloud environments, it allows for resources to be automatically allocated and scaled up or down as needed, without incurring additional costs.

Third, cloud-native app development promotes better collaboration and DevOps culture. By using technologies, tools, and best practices that are designed to work seamlessly with cloud environments, it becomes easier for different teams and developers to work together on the same application.

Fourth, cloud-native app development allows for better security, by using technologies, tools, and best practices that are designed to work seamlessly with cloud environments, it ensures that the application and infrastructure are protected from threats and can continue to operate in case of an attack or failure.

In summary, as a new engineer, understanding the concept of cloud-native app development is important because it is a key aspect of building and deploying applications in the cloud, allows for better scalability and cost efficiency, promotes better collaboration and DevOps culture, and allows for better security.

Learning Materials

Here’s a list to get you started learning about cloud-native app development. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Best practices in Kubernetes app development

This document outlines best practices for developing with Kubernetes, such as using a tailored logging interface, debugging with CLI commands, and creating project templates with Cloud Code. Google Cloud DevTools are introduced as a way to simplify the process of incorporating best practices into the Kubernetes development workflow.

A Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different app development tools such as Kubernetes Deployments, Services, and ConfigMaps. This can be done by following tutorials and guides, and deploying a simple application on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of app development, you can begin to explore the underlying concepts and technologies such as Kubernetes pods, services, and volumes. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: App development is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of app development such as containerization, scaling, and rolling updates.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with app development for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using app development tools in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

Hey there, let’s talk about cloud native application development. So, what is it exactly? Well, it’s all about developing applications that are specifically designed to run in a cloud environment, like Kubernetes, which is one of the most popular container orchestration platforms out there.

What makes cloud native application development different from other approaches is that it’s all about leveraging the benefits of the cloud, like scalability and flexibility, to create applications that can easily adapt to changing workloads and environments.

When it comes to developing applications for Kubernetes, there’s a typical software development workflow that you’ll need to follow. First, you’ll need to choose the right programming languages and frameworks. Some of the most popular languages for cloud native development are Go, Java, Python, and Node.js.

Once you’ve chosen your languages and frameworks, you’ll need to design your application architecture to work well with Kubernetes. Some of the best patterns for Kubernetes include using microservices, containerization, and service meshes. On the other hand, monolithic applications and stateful applications are not well suited for running on Kubernetes.

And finally, let me tell you, we really like VS Code around here. It’s one of the best tools for cloud native application development, especially when working with Kubernetes. It provides a lot of great features for working with containerization and orchestration, like excellent plugins for Kubernetes, debugging, and integration with other popular tools like Helm and Kustomize. So, if you haven’t already, give it a try, you might like it too.

Be sure to reach out on LinkedIn if you have any questions.

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Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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Understanding Cloud-Native Storage for Kubernetes: What It Is and How It Works

As a new engineer, understanding the concept of cloud-native storage is important for several reasons.

First, it is a key component of cloud native application development. It is the practice of using storage solutions that are native to the cloud and designed to work seamlessly with cloud-native applications. By understanding how cloud-native storage works, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, it allows for better scalability and cost efficiency. By using storage solutions that are native to the cloud, resources can be automatically allocated and scaled up or down as needed, without incurring additional costs.

Third, it promotes better collaboration and DevOps culture. By using storage solutions that are native to the cloud, it becomes easier for different teams and developers to work together on the same application.

Fourth, it allows for better data availability. By using storage solutions that are native to the cloud, data is stored in multiple locations and can be accessed from any location.

In summary, understanding the concept of cloud-native storage is important because it is a key component of cloud native application development, allows for better scalability and cost efficiency, promotes better collaboration and DevOps culture, and allows for better data availability. It is a powerful tool for building and deploying applications in a cloud environment and is essential for any engineer working in the field today.

Learning Materials

Here’s a list to get you started learning about cloud-native storage. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

CNCF Webinar – Introduction to Cloud Native Storage

This webinar provides an overview of the evolving cloud native storage requirements and data architecture, discussing the need for layered services that can be seamlessly bound for the user experience. It explores the extensible, open-source approach of abstractions and plugins that give users the choice to select the storage technologies that fit their needs. It also looks at the importance of persistent volumes for containers, allowing for the flexibility and performance needed for stateful workloads.

A Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different cloud-native storage options such as Kubernetes Persistent Volumes (PVs), Persistent Volume Claims (PVCs), and StatefulSets. This can be done by following tutorials and guides, and deploying these options on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of cloud-native storage, you can begin to explore the underlying concepts and technologies such as Kubernetes storage classes, storage provisioners, and storage backends. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes and storage providers, as well as books and blogs on the topic.

Understanding the principles and best practices: Cloud-native storage is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices, such as data replication, data durability, and data accessibility.

Joining a community: Connect with other people who are learning and working with cloud-native storage for Kubernetes by joining online forums, meetups, and social media groups.

Practice, practice, practice: The best way to learn is by doing. The more you practice deploying and using cloud-native storage options in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

Hey there! I’m glad you’re interested in learning more about cloud native storage. It’s a pretty important concept to understand, especially if you’re working with Kubernetes.

So, what is cloud native storage? Essentially, it’s a type of storage that is built specifically for use in cloud environments. This means that it’s designed to work seamlessly with cloud technologies like Kubernetes, and can handle the unique challenges that come with a cloud environment, like scalability and high availability.

Why is it necessary for certain solutions built on Kubernetes? Well, Kubernetes is a powerful tool for managing containerized applications, but it doesn’t come with its own storage solution. So, if you’re building a solution on Kubernetes, you’ll need to use a separate storage solution that’s compatible with it. That’s where cloud native storage comes in – it’s designed to work with Kubernetes, so it’s the perfect match.

Now, let’s talk about how it works. Essentially, cloud native storage solutions provide a way to store and access data that’s running on Kubernetes. They do this by creating a “volume” that can be mounted to a Kubernetes pod, which allows the pod to access the data stored in the volume. This way, you can store data in a persistent way, even if the pod is deleted or recreated.

Here are a couple of popular Kubernetes cloud native storage options and the types of solutions they’re used for:

  1. Rook: Rook is an open-source storage solution for Kubernetes. It’s great for use cases where you need to store data in a distributed way, like a big data analytics solution or a large-scale data storage system.
  2. GlusterFS: GlusterFS is another open-source storage solution for Kubernetes. It’s great for use cases where you need to store data in a highly available way, like a web application or a database.

Both of these options are great choices for different types of solutions, and they’re both built specifically to work with Kubernetes.

So, that’s the basics of cloud native storage. It’s a powerful tool that can help you build robust and scalable solutions on Kubernetes. If you have any more questions, feel free to ask on LinkedIn!

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Unlocking the Power of Service Discovery in Kubernetes

As a new engineer, understanding the concept of service discovery is important for several reasons.

First, it is a key component of microservices architecture. Service discovery allows services to find and communicate with each other, regardless of their location or IP address. This makes it easier to build, deploy, and manage microservices-based applications.

Second, service discovery enables greater scalability and flexibility. Services can be added or removed without affecting the rest of the system, and new services can be introduced without changing existing code.

Third, service discovery facilitates better collaboration and DevOps culture. By making it easy for services to find and communicate with each other, different teams and developers can work together on the same application.

Fourth, service discovery allows for better resilience. It enables the system to automatically route traffic to healthy instances of a service, even if some instances are unavailable.

In summary, understanding service discovery is important for any engineer working in the field today. It is a powerful tool for building and deploying applications in a microservices environment and is essential for achieving greater scalability, flexibility, collaboration, and resilience.

Learning Materials

Here’s a list to get you started learning about Service Discovery. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

What is Service Discovery?

Moving from a monolith to a Cloud-based Microservices Architecture presents several challenges, such as Service Discovery, which involves locating resources on a network and keeping a Service Registry up to date. Service Discovery can be categorized by WHERE it happens (Client Side or Server Side) and HOW it is maintained (Self Registration or Third-party Registration). Each approach has its own pros and cons, and further complexities such as Service Mesh can be explored in further detail.

Possible Learning Path (Service Discovery for Kubernetes)

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different service discovery mechanisms such as Kubernetes Services, DNS, and Load Balancing. This can be done by following tutorials and guides and deploying these services on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of service discovery, you can begin to explore the underlying concepts and technologies such as Kubernetes Services, DNS, and Load Balancing. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: Service discovery is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of service discovery such as service registration, service discovery, and service resolution.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with service discovery for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using service discovery mechanisms in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

So, you know how in a microservice architecture, we have all these different services that need to talk to each other? Well, service discovery is kind of like a phone book for those services (this is DNS). It helps them find each other and communicate with each other.

In traditional networks, service discovery is often done using a centralized server or load balancer. This means that all the services need to know the IP address or hostname of this central server in order to communicate with other services.

But in Kubernetes, service discovery is built into the platform. Each service gets its own unique IP address and DNS name, and Kubernetes automatically handles routing traffic between them. This means that the services don’t need to know anything about other services except their own name.

And the best part? Kubernetes service discovery is dynamic, which means that it automatically updates when new services are added or removed, so you don’t have to manually update the phone book every time something changes.

But that’s not all, Kubernetes also provides a way to expose your services externally, so that you can access them from outside the cluster, which is very useful for example if you want to access your services from the internet.

So, with service discovery in Kubernetes, you don’t have to worry about keeping track of IP addresses and hostnames, and you don’t have to worry about updating a central server when things change. It’s like having a personal assistant who always knows the latest phone number of your services and also makes sure that they are accessible from anywhere.

Basically, service discovery in Kubernetes provides a way for services to easily find and communicate with each other, and it’s built right into the platform. It’s a game-changer for managing and scaling a microservice architecture.

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Understand Auto-Scaling to Maximize Cloud Native Application Development

As a new engineer, understanding auto-scaling is important for several reasons. It is a key component of cloud native application development, allowing for greater scalability and cost efficiency, better collaboration and DevOps culture, and better availability and resilience. Auto-scaling automatically adjusts the number of resources used by an application based on current demand, ensuring the system can handle a large number of requests while minimizing cost. It also makes it easier for different teams and developers to work together on the same application. Auto-scaling is a powerful tool for building and deploying applications in a cloud environment, and is essential for any engineer working in the field today.

As a new engineer, understanding the concept of auto-scaling is important for several reasons.

First, auto-scaling is a key component of cloud native application development. It is the process of automatically adjusting the number of resources (such as servers) used by an application based on the current demand. By understanding how auto-scaling works, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, auto-scaling allows for greater scalability and cost efficiency. By automatically adjusting the number of resources used by an application, it ensures that the system can handle a large number of requests while minimizing the cost of running the application. This makes it easy to handle the increasing demand for more computing power and storage.

Third, auto-scaling allows for better collaboration and DevOps culture. By automating the process of scaling resources, it becomes easier for different teams and developers to work together on the same application.

Fourth, auto-scaling allows for better availability and resilience, by automatically adjusting resources when traffic increases, it ensures that the application is always available to handle requests, and when traffic decreases, it reduces the resources which allows to save cost.

In summary, as a new engineer, understanding the concept of auto-scaling is important because it is a key component of cloud native application development, allows for greater scalability and cost efficiency, better collaboration and DevOps culture, and better availability and resilience. It is a powerful tool for building and deploying applications in a cloud environment and is essential for any engineer working in the field today.

Learning Materials

Here’s a list to get you started learning about auto-scaling. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Kubernetes cluster autoscaling for beginners

Kubernetes is a powerful tool for deploying applications, but it’s important to understand the resource requirements of the applications and set resource requests and limits to allow the scheduler to make informed decisions when placing pods onto nodes. This will help ensure that the nodes are not over-utilized and that applications are given the resources they need.

A Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different auto-scaling mechanisms such as Kubernetes Horizontal Pod Autoscaler and Cluster Autoscaler. This can be done by following tutorials and guides and deploying these services on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of auto-scaling, you can begin to explore the underlying concepts and technologies such as Kubernetes Horizontal Pod Autoscaler and Cluster Autoscaler. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: Auto-scaling is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of auto-scaling, such as scaling policies, monitoring, and alerting.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with auto-scaling for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using auto-scaling mechanisms in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

Before I explain an auto-scaler, let me quickly tell you the story of the Porsches crashing into walls every 15 minutes.

Back when I was a newer developer working for the e-commerce department of an electronics parts seller, we had a web outage. We had no idea why the webserver was down. This was in the olden days when the best monitoring tool we had was named ELMA. It was better for debugging and development than anything you would want to put into production.

The e-commerce website was down and we developers were scrambling to find out why. The president of the company was standing in our cubicles, shaking his head and looking at his watch. Every few minutes he would proclaim loudly, “There goes another one.”

After a while, one of our more senior developers asked, “’Another one,’ what, sir?”

He replied, “You just crashed a brand new Porsche into a brick wall.”

That’s how much money we were losing every 15 minutes while the site was down.

We found out that the problem had nothing to do with our code. It was an issue with our servers. We didn’t have enough. We had recently opened up to new markets overseas and this added traffic was crashing our old Internet Information Server systems. We didn’t have auto-scaling.

Auto-scaling is like having a personal trainer for your infrastructure. It makes sure that your infrastructure is always in tip-top shape by automatically adjusting the number of resources (like servers or containers) based on the current demand.

In traditional IT environments, auto-scaling is often done manually, which can be time-consuming and error-prone. But in a cloud-native environment like Kubernetes, auto-scaling is built into the platform and can be easily configured with Kubernetes resources. This makes it a lot more efficient and reliable.

With Kubernetes, you can set up auto-scaling rules that specify how many resources you want to have available for a given service. For example, you can set a rule that says “if the CPU usage goes above 80% for more than 5 minutes, then add another server.” And when the demand goes down, the system will also automatically scale down the resources. This way, you can ensure that you always have the right amount of resources to handle the current load and also save money by not having unnecessary resources running.

Another benefit of using auto-scaling in a cloud-native environment is that it allows you to handle unexpected traffic spikes, such as a viral social media post or a news article. This way, you don’t have to worry about your service going down because of a sudden increase in traffic.

So, using auto-scaling in a cloud-native environment has many benefits, including increased efficiency, reliability and cost savings. Plus, it’s like having a personal trainer that makes sure your infrastructure is always ready for action. It might also save on the number of Porsches you crash into brick walls.

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Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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What is Load Balancing, and How Can it Help Your Cloud-Native Applications?

As a new engineer, understanding the concept of load balancing is important for several reasons.

First, it is a key component of cloud native application development. It is the process of distributing incoming network traffic across multiple servers to ensure that no single server is overwhelmed. By understanding how it works, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, load balancing allows for greater scalability and availability. By distributing traffic across multiple servers, it ensures that the system can handle a large number of requests and can continue to function even if one server goes down. This makes it easy to handle the increasing demand for more computing power and storage.

Third, it facilitates better collaboration and DevOps culture. By making it easy to distribute traffic across multiple servers, it becomes easier for different teams and developers to work together on the same application.

Fourth, it enhances security, by distributing the traffic across multiple servers, it makes it harder for attackers to target a single point of failure.

In summary, as a new engineer, understanding the concept of load balancing is important because it is a key component of cloud native application development, allows for greater scalability and availability, better collaboration and DevOps culture, and better security. It is a powerful tool for building and deploying applications in a cloud environment and is essential for any engineer working in the field today.

Learning Materials

Here’s a list to get you started learning about load balancing. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

How load balancing and service discovery works in Kubernetes

Kubernetes provides a range of features to enable applications to communicate with each other, including service discovery, load balancing, DNS, and port management. These features are enabled through the use of namespaces, labels, services, and Linux networking features such as bridges and IP tables.

A Possible Learning Path

Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different load balancing mechanisms such as Kubernetes Services, Ingress, and External Load Balancers. This can be done by following tutorials and guides and deploying these services on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of load balancing, you can begin to explore the underlying concepts and technologies such as Kubernetes Services, Ingress, and External Load Balancers. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.

Understanding the principles and best practices: Load balancing is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of load balancing such as traffic distribution, service availability, and fault tolerance.

Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with load balancing for Kubernetes. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using load balancing mechanisms in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.

A Note from the Architect

A load balancer is like an air traffic controller for your network. It ensures that traffic is evenly distributed across all servers, so that no single server becomes overwhelmed and crashes. This is especially important when there is a lot of incoming traffic or a large number of users accessing the application.

In traditional networks, load balancers are typically dedicated hardware devices that sit in front of the servers and manage the traffic. However, in cloud-native networks, load balancers are usually software-based and run as part of the infrastructure. This is where Kubernetes comes into play.

Kubernetes has a built-in load balancer that can be easily configured with the Kubernetes resources. It automatically distributes traffic to the services running in the cluster. This is a great advantage, as it allows for more flexibility, scalability, availability, and the ability to handle failover automatically.

Another benefit of using a load balancer in a cloud-native network is that it makes it possible to expose services to the outside world. This means that services can be accessed from anywhere, which is especially useful if you want to access them from the internet.

Using a load balancer in a cloud-native network has many advantages, such as increased availability, scalability, and flexibility, as well as the ability to handle failover automatically. Plus, it’s like having a superhero that ensures traffic is evenly distributed and users are happy.

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Understanding Microservices Architecture: Key Concepts, Learning Resources & More

As a new engineer, understanding the concept of microservices architecture is important for several reasons.

First, it is a key component of cloud native application development. It allows for faster development, easier scaling, and more flexible deployment options. By understanding how microservices work, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, microservices architecture promotes modularity, which allows for greater flexibility, scalability, and maintainability of the system. Each microservice can be developed, deployed, and scaled independently, making it easier to handle increasing demand.

Third, microservices architecture facilitates better collaboration and DevOps culture. By breaking down the application into smaller, independent units, different teams and developers can work together on the same application.

Fourth, microservices architecture allows for greater resilience. By isolating the failure in one service, it will not impact the entire system.

In summary, understanding the concept of microservices architecture is essential for any engineer working in the field today. It is a powerful tool for building and deploying applications in a cloud environment and provides benefits such as faster development, easier scaling, more flexible deployment options, greater flexibility, scalability, and maintainability, better collaboration and DevOps culture, and greater resilience.

Learning Materials

Here’s a list to get you started learning about Microservices architecture. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Microservices explained – the What, Why and How?

In this video, the Nana explains the concept of microservices architecture and its advantages over monolith architecture. He also discusses best practices for microservices communication, such as using API calls, message brokers, and service meshes. Finally, he mentions the importance of a CI/CD pipeline for deploying microservices.

Possible Learning Path

Hands-on experience: Start by experimenting with building simple microservices using technologies such as Node.js, Spring Boot, or Flask. This can be done by following tutorials and guides and deploying these services on a cloud platform like AWS, Azure, or GCP.

Theoretical learning: Once you have a basic understanding of microservices, you can begin to explore the underlying concepts and technologies. This can be done through online resources such as tutorials, courses, and documentation provided by microservices architecture, as well as books and blogs on the topic.

Understanding the principles and best practices: Microservices architecture is not just about technology; it’s also about principles and best practices. It’s important to understand the key principles and best practices of microservices, such as loose coupling, autonomy, and scalability.

Joining a community: Joining a community of microservices enthusiasts will help you connect with other people who are learning and working with microservices architecture. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice building and deploying microservices, the more comfortable and proficient you will become with the architecture.

A Note from the Architect

I’m not in the camp that thinks monolithic architecture is necessarily bad. However, I believe that, in the long run, microservices have a better chance of success. Are microservices more work? Yes, there is an overhead associated with them, but I think it’s worth it for the added flexibility.

To explain the difference between microservices and traditional monolithic architectures, a monolithic architecture is when all the different parts of an application, such as the user interface, the database, and the backend, are bundled together in one package. This can work well for small projects, but as the project grows and becomes more complex, it can become harder to manage and maintain.

On the other hand, microservice architecture breaks the application down into smaller, individual services. Each service is responsible for a specific task, such as user authentication or payment processing.

The benefits of microservices include the ability to make changes to one service without affecting the others, as well as more flexibility and scalability, since each service can be deployed and scaled independently.

For example, here’s some code in Python that demonstrates a microservice:

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

if __name__ == '__main__':
    app.run()

This code is a simple web service that listens for a request to the root URL and returns the string “Hello, World!”.

That doesn’t do much, but it does show how you can quickly create a service. A series of these services working together could create a more robust solution. I won’t dive into the Actor Pattern, but it’s probably the ideal approach to microservices.

Keep in mind, an organization would select microservices over other architectures because they are more flexible, scalable, and easier to maintain as the project grows. Plus, it’s way cooler to say you’re working with microservices than a monolithic architecture. Trust me, it’s like being in a secret club of developers who know how to handle complexity in the best way possible.

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Understanding Containerization: A Guide for New Engineers

As a new engineer, understanding the concept of containerization is important for several reasons.

First, containerization is a key component of cloud native application development. Containers are a lightweight and portable way to package software, making it easy to run and manage applications on cloud infrastructure. By understanding how containers work, you will be able to build, deploy, and manage cloud-native applications more effectively.

Second, containerization allows for greater consistency and portability across different environments. With containers, you can package an application and its dependencies together, ensuring that it will run the same way regardless of where it is deployed. This eliminates the “works on my machine” problem and makes it easier to move applications between different environments.

Third, containerization allows for greater scalability and resource efficiency. Containers use less resources than traditional virtual machines, and can be easily scaled up or down as needed. This makes it easier to handle the increasing demand for more computing power and storage.

Fourth, containerization also allows for better collaboration and DevOps culture, as containers can be easily shared and reused, making it easier for different teams and developers to work together on the same application.

In summary, as a new engineer, understanding the concept of containerization is important because it is a key component of cloud native application development, allows for greater consistency and portability across different environments, enables greater scalability and resource efficiency, and promotes collaboration and DevOps culture. It is a powerful tool for building and deploying applications in a cloud environment and is essential for any engineer working in the field today.

Learning Materials

Here’s a list to get you started learning about containerization. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to Watch

Kubernetes Crash Course for Absolute Beginners

Kubernetes is an open source container orchestration framework designed to manage applications made up of hundreds or thousands of containers across multiple environments. It offers features such as high availability, scalability, and disaster recovery, as well as a virtual network that enables communication between pods. This video provides an overview of the Kubernetes architecture and components, and a use case of a web application with a database to illustrate how it works.

Possible Learning Path

Hands-on experience: Start by installing Docker and Kubernetes on your local machine or in a virtual environment. This can be done by following the official documentation provided by Docker and Kubernetes. After that, you can follow tutorials and guides to build and deploy simple applications in containers using Docker and Kubernetes.

Theoretical learning: Once you have a basic understanding of Docker and Kubernetes, you can begin to explore the underlying concepts and technologies. This can be done through online resources such as tutorials, courses, and documentation provided by Docker and Kubernetes, as well as books and blogs on the topic.

Joining a community: Joining a community of Docker and Kubernetes enthusiasts will help you connect with other people who are learning and working with these technologies. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice building and deploying containerized applications using Docker and Kubernetes, the more comfortable and proficient you will become with these technologies.

Specialize in a specific use case: Docker and Kubernetes can be used in a wide variety of scenarios and use cases, so it is beneficial to specialize in one or two that align with your business or career goals.

A Note from the Architect

I’m trying to think of the first time I worked with a Docker container. I believe it was almost four years ago, which in technology timeframes was forever. I was trying to decide if I wanted to use Angular or React to create components for a plugin framework running on SharePoint. It wasn’t the type of development I was used to doing at the time, but I knew the industry was heading away from Angular and more toward React. So, I installed Docker on my laptop, learned how to check out the images, and eventually started learning the basics of containers. I was hooked. Here was a great way to build apps, host them locally, and get the same experience locally that I could expect in production. No more, “It worked on my machine.”

In the last couple of years, I’ve used the remote connection capabilities of VS Code to run pretty much all of my development in containers. It gives me the freedom to try out different languages, frameworks, and libraries without ever needing to install those on my local operating system. I’m proud to say that I never get bugged for Java or .Net updates now. I just get the latest images and add a volume that connects to a local folder where I manage my Git repositories. It’s made my life as a developer much easier.

If you’re wondering, “What’s the big deal with containers? I just want to write code. Why do I need to use containers?” I’ll try to answer that question. Because we don’t just write code anymore. As developers and as operations engineers, we’re beginning to move into a phase where we are sharing the overall solution. This means that when I create something or have a hand in creating something, I have ownership over that thing. I’m responsible for it.

Now, you might work for an enterprise that’s still a bit behind the times. You may write code, and some other team might test that code, and then some other team might try to deploy that code. In that situation, you probably aren’t using containers or anything that looks like modern DevOps. And in that situation, the team between you and the customers who will derive value from your code is a bottleneck. If you rely on a QA team, they will find bugs, because that’s what they are incentivized to do. It’s their job to compare your code against some form of requirements and fail it if it doesn’t meet those requirements. Operations in this type of environment is incentivized to keep systems running smoothly, so they’ll look for any excuse to deny your code entry into production—that usually looks like a set of meetings designed to make sure you met all the criteria needed for something to go into production.

That is the old way of developing software. Let me tell you, if you’re working some place like that, get out. No. Seriously. Leave. Find a better job.

I believe this is the way software should be developed:

The Most Powerful Software Development Process Is The Easiest

In an ideal software development process, the only work done is understanding the problem, writing code to solve it, testing to confirm it is solved, and making progress in small steps to retain the ability to change the system when needed. The goal is to minimize work and maximize learning, allowing for changes to be made easily and with confidence.

Containers make this process easier. Your code remains modular, making it easier to version and manage libraries and dependencies. You can even build out the needed infrastructure for a container management system, such as Kubernetes, without involving operations in some cases.

As a developer and an architect, I have found that containers have improved the quality of my development life. They have allowed me to have more control over the solutions I deliver. I believe that if you start working regularly with containers, you will feel the same way too.

Connect with Shawn
Connect with Shawn

Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

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Why Should a New Engineer Learn the Cloud Native Concepts?

As a new engineer, learning cloud native concepts is important for several reasons.

First, cloud computing is becoming increasingly popular and is now the norm for many organizations. Many companies are moving away from traditional on-premises data centers and migrating their infrastructure and applications to the cloud. Knowing how to build, deploy, and manage cloud-native applications will give you a valuable skill set that is in high demand in the job market.

Second, cloud native concepts and technologies are designed to be flexible, scalable, and efficient. They enable faster development and deployment of applications and make it easier to handle the increasing demand for more computing power and storage. By learning these concepts, you will be able to build applications that can handle large amounts of traffic and data and can easily scale up or down as needed.

Third, cloud native concepts and technologies are designed to work well together. They are all part of a larger ecosystem that is designed to make it easy for developers to build, deploy, and manage applications on cloud infrastructure. By learning these concepts, you will be able to take advantage of the full range of cloud-native tools and services, and will be able to create more powerful and efficient applications.

In summary, as a new engineer, learning cloud native concepts will give you a valuable skill set, allow you to build flexible, scalable, and efficient applications, and enable you to take advantage of the full range of cloud-native tools and services. It is an essential skill set for many companies today and will be essential in the future.

What is Cloud Native?

Cloud native is a term used to describe an approach to building, deploying, and running applications on cloud infrastructure. It involves containerization, microservices architecture, and the use of cloud-native tools and services.

Containerization packages software, its dependencies, and configuration files together in a lightweight and portable container, allowing it to run consistently across different environments.

Microservices architecture designs and builds software as a collection of small, independent services that communicate with each other via well-defined APIs. This approach enables faster development, easier scaling, and more flexible deployment options.

Cloud-native tools and services are designed specifically for cloud environments and provide capabilities such as auto-scaling, load balancing, and service discovery. They allow for faster and more efficient deployment and management of applications.

In summary, cloud native is a way of designing, building, and running applications on cloud infrastructure. It leverages containerization and microservices architecture, and utilizes cloud-native tools and services for faster and more efficient deployment and management of applications. As a new engineer, it is important to understand these concepts and how they work together in order to build cloud-native applications.

Learning Materials

Here’s a list to get you started learning about Cloud Native. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.

Beginner:

Intermediate:

Advanced:

Videos to watch

What is Cloud Native and Why Should I Care?

Wealth Grid is a mid-sized firm that has product and service market fit, but is struggling to shorten its time to value and stay off the front page news. To do this, they must embrace cloud native technologies, but this is not business as usual. With the help of the Cloud Native Computing Foundation, Wealth Grid can learn from their mistakes and use tools and techniques to achieve their goals.

Expert talk: Cloud Native & Serverless • Matt Turner & Eric Johnson • GOTO 2022

Matt Turner and Eric Johnson discuss the importance of Cloud Native Concepts for the new engineer to learn, such as Continuous Integration and Continuous Delivery, and the benefits of testing in production to catch certain classes of bugs.

A Possible Learning Path

Hands-on experience: It is important to start by experimenting with different cloud providers, such as AWS, Azure, and GCP, to understand the basic concepts and services offered by each. This can be done by creating a free account and following tutorials and guides to build and deploy simple applications.

Theoretical learning: Once you have a basic understanding of cloud computing, you can begin to explore cloud native concepts such as containerization, microservices, and service discovery. This can be done through online resources such as tutorials, courses, and documentation provided by cloud providers, as well as books and blogs on the topic.

Joining a community: Joining a community of cloud native enthusiasts will help you connect with other people learning and working with cloud native technology. This can be done through online forums, meetups, and social media groups.

Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice building and deploying cloud native applications, the more comfortable and proficient you will become with the technology.

Specialize in a specific cloud provider: Cloud providers each have their own set of services and ways of working, so it is beneficial to specialize in one or two providers that align with your business or career goals.

A Note From the Architect

Don’t be intimidated by the volume of information you’ll need to learn to be proficient in cloud-native technologies. Because I have a secret for you from a twenty-five year veteran. There’s little chance you’ll ever be much more than competent. You may be able to master a few of these subject areas, and that’s great if you do, but it’s not necessary if you truly understand one important thing.

I call this important thing, “The Why”.

In each of these articles where I present an important topic from big concepts in cloud-native development I will give you my opinion based on personal experience as to why you should consider using this technology, what are other possibilities, and what are the trade offs.

I believe that ‘The Why’ is one of the most important parts of a technology consideration. So what is the, “why,” of Cloud Native? In my opinion, it’s the ability to develop and deliver solutions on their built-to-fit platform. Even though there’s still a huge market for large systems like SAP, Microsoft Dynamics, and Oracle, the future belongs to value created solutions running on platforms that best fit their need.

I’m sure some people are wondering if everything really needs to be containerized. No, there are plenty of alternative options for running your workloads that don’t involve containers.

As a developer, I have come across several alternatives to cloud native technologies. One alternative is using virtual machines (VMs) instead of containers. VMs offer a higher level of isolation and security, but they also have a larger footprint and are less portable. Another alternative is using on-premises infrastructure, which provides greater control over data and security, but also comes with higher costs and maintenance responsibilities.

Another alternative is using a platform-as-a-service (PaaS) instead of containers. PaaS provides a higher level of abstraction and can simplify the deployment process, but it also limits the level of control and customization that you have over the infrastructure.

It’s important to note that, while these alternatives can be viable options depending on the specific use case, they often trade off some of the benefits of cloud native technologies such as scalability, flexibility, and cost-efficiency. Ultimately, it’s important to weigh the tradeoffs and choose the solution that best aligns with the needs of your project and organization.

What I hope to accomplish with this series is to open your eyes to the possibilities of what Cloud Native has to offer.

Connect with Shawn
Connect with Shawn

Connect with me on LinkedIn. It’s where I’m most active, and it’s the easiest way to connect with me.

Shawn’s LinkedIn