If you have been in the tech industry for a while, you are probably aware of the buzzword known as cloud-native. The more people you ask what it means, chances are, the more varied answers you will receive, and what’s bizarre is that all of them would be accurate in their own way. So, why the different answers? Well, the answer is simple – cloud-native technology and the stack is ever evolving, and each engineer, based on the use case of their cloud-native technology, would consider that in of itself to be cloud-native. However, based on the definition set out by the CNCF and my practical experience of using these technologies for the past many years, instead of defining a broader term of cloud-native computing, I would rather define what it means for an application to be cloud-native:

“Cloud-native is the architectural style for any application that makes this application cloud-deployable as a loosely coupled formation of singular services that is optimized for automation using DevOps practices.”

Let’s delve into understanding what that means in the industry. Cloud-native is an application design style that enables engineers to deploy any software in the cloud as each service. These services are optimized for automation using DevOps practices such as Continuous Integration and Continuous Deployment (CI/CD) and Infrastructure as Code (IaC). This approach allows for faster development, testing, and deployment of applications in the cloud, making it easier for organizations to scale and adapt to changing business needs. Additionally, the use of microservices and containerization in cloud-native architecture allows for greater flexibility and resiliency in the event of service failures. Overall, cloud-native architecture is designed to take full advantage of the cloud’s capabilities and provide a more efficient and effective way to build and deploy applications.

Why consider using cloud-native architecture?

I have always found the best way to approach any problem is to start with why. As for our current endeavor, it is prudent to think about why we would even care about thinking of a different approach to building our applications when we can get away with the current style of development. While you wouldn’t be completely wrong, there are some pretty strong arguments to be made otherwise. While we can address the need for this architecture, further for now, we can try contemplating the benefits of development. A few of them are listed as follows:

• Scalability: One of the primary benefits of cloud-native architecture is the ability to easily scale applications horizontally and vertically, to meet changing demands. This is particularly important for applications that experience fluctuating levels of traffic as it allows for resources to be allocated in real time, without the need for manual intervention.
• Flexibility: Cloud-native architecture also provides greater flexibility in terms of where and how applications are deployed. Applications can be deployed across multiple cloud providers or on-premises, depending on the needs of the organization, including but not limited to the organization’s compliance policies, business continuity, disaster recovery playbooks, and more.
• Cost savings: Cloud-native architecture can lead to cost savings as well. By taking advantage of the pay-as-you-go pricing model offered by cloud providers, organizations only pay for the resources they use, rather than having to invest in expensive infrastructure upfront. Additionally, the ability to scale resources up and down can help reduce the overall cost of running applications.
• Improved security: Cloud-native architecture also offers improved security for applications. Cloud providers typically offer a range of security features, such as encryption (such as AWS KMS, which is used for encryption key management and cryptographic signing) and multi-factor authentication, which can be applied to applications. Additionally, the use of containerization and microservices can help isolate and secure individual components of an application.
• Faster deployment: Cloud-native architecture allows for faster deployment of applications. Containerization, for example, allows you to package applications and dependencies together, which can then be easily deployed to a cloud environment. Frameworks such as GitOps and other IaC solutions help significantly reduce the time and effort required to deploy new applications or updates.
• Improved resilience: Cloud-native architecture can also help improve the resilience of applications. By using techniques such as load balancing and automatic failover, applications can be designed to continue running even in the event of a failure. This helps ensure that applications remain available to users, even in the event of disruption.
• Better performance: Cloud-native architecture can lead to better performance for applications. By using cloud providers’ global networks, applications can be deployed closer to users, reducing latency and improving the overall user experience. Additionally, the use of containerization and microservices can help improve the performance of the individual components of an application.
• Improved collaboration: Cloud-native architecture can also improve collaboration among developers. By using cloud-based development tools and platforms, developers can work together more easily and efficiently, regardless of their location. Additionally, the use of containerization and microservices can help promote collaboration among teams by breaking down applications into smaller, more manageable components.
• Better monitoring: Cloud-native architecture can also enable better monitoring of applications. Cloud providers typically offer a range of monitoring tools, such as real-time metrics and log analysis, that can be used to track the performance and usage of applications. This can help organizations quickly identify and resolve any issues that may arise.
• Better business outcomes: All the aforementioned benefits can lead to better business outcomes. Cloud-native architecture can help organizations deploy new applications, improve the performance and availability of existing applications, and reduce the overall cost of running applications quickly and easily. This can help organizations stay competitive, improve customer satisfaction, and achieve their business goals.

Essentially, there is no silver bullet when it comes to architecting cloud-native applications – the method of architecture heavily depends on the primal stage of defining factors of the application use cases, such as the following:

• Scalability requirements: How much traffic and usage is the application expected to handle and how quickly does it need to scale to meet changing demands?
• Performance needs: What are the performance requirements of the application and how do they impact the architecture?
• Security considerations: What level of security is required for the application and how does it impact the architecture?
• Compliance requirements: Are there any specific compliance regulations that the application must adhere to and how do they impact the architecture?
• Deployment considerations: How and where will the application be deployed? Will it be deployed across multiple cloud providers, availability zones, or on-premises?
• Resilience and fault-tolerance: How should the architecture be designed to handle service failures and ensure high availability?
• Operational requirements: How should the architecture be designed to facilitate monitoring, logging, tracing, and troubleshooting of the application in production so that compliance policies such as service-level indicators (SLIs), service-level objectives (SLOs), and error budgets can be applied to the telemetry data that’s been collected?
• Cost and budget: What is the budget for the application and how does it impact the architecture?
• Future scalability and extensibility: How should the architecture be designed to allow for future scalability and extensibility of the application?
• Integration with existing systems: How should the architecture be designed to integrate with existing systems and data sources?

While we will discuss a few of those factors in detail in the subsequent chapters, it is important to address the problems and identify the pain points that warrant the use of a cloud-native approach and a design architecture to enable more efficient, scalable systems.

Cloud models

Before we sail into understanding the cloud-native model, it is prudent to understand the existing cloud models for deployment. In this book, to understand the different cloud-native deployment models, I will segregate the cloud offering into two categories.

Cloud deployment model

This deployment model explains strategies of cloud infrastructure deployment from the perspective of the cloud architecture used within the organization and the type of cloud offering that the organization chooses for deployment.

Public cloud

The public cloud is a cloud deployment model in which resources and services are made available to the public over the internet. This includes a wide range of services, such as computing power, storage, and software applications. Public cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), own and operate the infrastructure and make it available to customers over the internet. Public cloud providers offer a range of services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), which can be used on a pay-as-you-go basis.

Advantages of the public cloud include flexibility and scalability, as well as cost savings, as customers only pay for the resources they use and do not need to invest in and maintain their infrastructure. Public cloud providers also handle the maintenance and updates/upgrades of the infrastructure, which can free up IT staff to focus on other tasks. Additionally, public clouds are known for providing a global reach, with multiple locations and availability zones, which can help with disaster recovery and business continuity.

While the public cloud offers many advantages, there are also a few potential disadvantages to consider:

• Security concerns: Public cloud providers are responsible for securing the infrastructure, but customers are responsible for securing their data and applications. This can create security gaps, especially if customers do not have the necessary expertise or resources to properly secure their data and applications.
• Limited control and customization: Public cloud providers offer a wide range of services and features, but customers may not have the same level of control and customization as they would with their own on-premises infrastructure.
• Vendor lock-in: Public cloud providers may use proprietary technologies, which can make it difficult and costly for customers to switch to a different provider if they are not satisfied with the service or if their needs change. The operational cost may also rise significantly if the cloud vendor decides to increase the cost of their services, which is difficult to counter in this scenario.
• Dependence on internet connectivity: Public cloud services are provided over the internet, which means that customers must have a reliable internet connection to access their data and applications. This can be an issue in areas with limited or unreliable internet connectivity.
• Compliance: Public cloud providers may not be able to meet the compliance and regulatory requirements of certain industries, such as healthcare and finance, which may prohibit the use of public cloud services.
• Data sovereignty: Some organizations may have data sovereignty requirements that prohibit them from storing their data outside of their own country, and therefore may not be able to use public cloud services.

It’s important to carefully evaluate your organization’s specific needs and constraints, and weigh them against the benefits of public cloud, before deciding to use public cloud services.

Private cloud

A private cloud is a cloud deployment model in which resources and services are made available only to a specific organization or group of users and are typically operated on-premises or within a dedicated data center. Private clouds are often built using the same technologies as public clouds, such as virtualization, but they are not shared with other organizations. This allows for greater control and customization, as well as higher levels of security and compliance.

In a private cloud, an organization can have full control of the infrastructure and can configure and manage it according to its specific needs and requirements. This allows organizations to have a high degree of customization, which can be important for certain applications or workloads.

The advantages of a private cloud include the following:

• Greater control and customization: An organization has full control over the infrastructure and can configure and manage it to meet its specific needs
• Improved security: Since the infrastructure is not shared with other organizations, it can be more secure and better protected against external threats
• Compliance: Private clouds can be configured to meet the compliance and regulatory requirements of specific industries, such as healthcare and finance
• Data sovereignty: Organizations that have data sovereignty requirements can ensure that their data is stored within their own country

Here are some of the disadvantages of a private cloud:

• Higher cost: Building and maintaining a private cloud can be more expensive than using a public cloud as an organization has to invest in and maintain its infrastructure
• Limited scalability: A private cloud may not be able to scale as easily as a public cloud, which can be an issue if an organization’s needs change
• Limited expertise: An organization may not have the same level of expertise and resources as a public cloud provider, which can make it more difficult to properly maintain and update the infrastructure

It’s important to carefully evaluate the specific needs and constraints of an organization before deciding to use private cloud services.

Hybrid cloud

A hybrid cloud is a combination of public and private clouds, where sensitive data and workloads are kept on-premises or in a private cloud, while less sensitive data and workloads are in a public cloud. This approach allows organizations to take advantage of the benefits of both public and private clouds while minimizing the risks and costs associated with each.

With hybrid cloud, organizations can use public cloud services, such as IaaS and SaaS, to handle non-sensitive workloads, such as web-facing applications and testing environments. At the same time, they can keep sensitive data and workloads, such as financial data or customer data, on-premises or in a private cloud, where they have more control and security.

Here are some of the advantages of a hybrid cloud:

• Flexibility: Organizations can use the best cloud services for each workload, which can help improve cost-efficiency and performance
• Improved security: Organizations can keep sensitive data and workloads on-premises or in a private cloud, where they have more control and security
• Compliance: Organizations can use public cloud services to handle non-sensitive workloads while keeping sensitive data and workloads on-premises or in a private cloud to meet compliance and regulatory requirements
• Data sovereignty: Organizations can store sensitive data on-premises or in a private cloud to meet data sovereignty requirements

Disadvantages of a hybrid cloud include the following:

• Complexity: Managing a hybrid cloud environment can be more complex than managing a public or private cloud, as organizations need to integrate and manage multiple cloud services
• Limited scalability: A hybrid cloud may not be able to scale as easily as a public cloud, which can be an issue if an organization’s needs change
• Limited expertise: An organization may not have the same level of expertise and resources as a public cloud provider, which can make it more difficult to properly maintain and update the infrastructure
• Hybrid cloud latency: If an application in one environment is communicating with a service in another cloud environment, there’s a high chance for a bottleneck to be created due to the higher latency of one of the services, leading to increasing the overall latency of the applications

It’s important to note that a hybrid cloud environment requires a good level of coordination and communication between the different parts of the organization, as well as with the different cloud providers, to ensure that the different services and data are properly integrated and secured.

Multi-cloud

Multi-cloud is a deployment model in which an organization uses multiple cloud services from different providers, rather than relying on a single provider. By using multiple cloud services, organizations can avoid vendor lock-in, improve resilience, and take advantage of the best features and pricing from different providers.

For instance, an organization might use AWS for its computing needs, Microsoft Azure for its storage needs, and GCP for its big data analytics needs. Each of these providers offers different services and features that are better suited to certain workloads and use cases, and by using multiple providers, an organization can select the best provider for each workload.

Let’s look at some of the advantages of the multi-cloud model:

• Avoid vendor lock-in: By using multiple cloud services, organizations can avoid becoming too dependent on a single provider, which can be a problem if that provider raises prices or experiences service disruptions
• Improved resilience: By using multiple cloud services, organizations can improve their resilience to service disruptions or outages as they can fail over to a different provider if one provider experiences an outage
• Best features and pricing: By using multiple cloud services, organizations can take advantage of the best features and pricing from different providers, which can help improve cost-efficiency and performance
• Flexibility: Multi-cloud deployment allows organizations to pick and choose the services that best fit their needs, rather than being limited to the services offered by a single provider

The disadvantages of the multi-cloud model include the following:

• Complexity: Managing multiple cloud services from different providers can be more complex than managing a single provider as organizations need to integrate and manage multiple cloud services.
• Limited scalability: A multi-cloud environment may not be able to scale as easily as a single-cloud environment, which can be an issue if an organization’s needs change.
• Limited expertise: An organization may not have the same level of expertise and resources as a public cloud provider, which can make it more difficult to properly maintain and update the infrastructure.
• Higher costs: Managing multiple cloud services from different providers can be more expensive than using a single provider as organizations need to pay for services and resources from multiple providers. Also, the organization would have to hire multiple engineers that had expertise across all cloud vendors.

It’s important for organizations to carefully evaluate their specific needs and constraints, and weigh them against the benefits of multi-cloud, before deciding to use multi-cloud services.

Community cloud

A community cloud is a type of private cloud that is shared by a group of organizations that has similar requirements and concerns. This type of cloud is typically owned, operated, and managed by a third-party provider, and is used by a specific community, such as a group of businesses in a particular industry or a group of government agencies.

Community cloud is a way for organizations to share the costs and benefits of a private cloud infrastructure while maintaining control over their data and applications. For example, a group of healthcare providers may set up a community cloud to share electronic medical records and other healthcare-related data and applications.

The advantages of a community cloud include the following:

• Cost savings: Organizations can share the costs of building and maintaining a private cloud infrastructure, which can help reduce costs
• Specialized resources and expertise: Community clouds are typically managed by third-party providers that have specialized resources and expertise, which can help improve performance and security
• Compliance: Community clouds can be configured to meet the compliance and regulatory requirements of specific industries, such as healthcare and finance
• Data sovereignty: Organizations that have data sovereignty requirements can ensure that their data is stored within their own country

Let’s look at some of the disadvantages of a community cloud:

• Limited control and customization: Organizations may not have the same level of control and customization as they would with their own on-premises infrastructure
• Security concerns: Organizations are responsible for securing their data and applications, but they may not have the necessary expertise or resources to properly secure their data and applications
• Limited scalability: A community cloud may not be able to scale as easily as a public cloud, which can be an issue if an organization’s needs change
• Limited expertise: An organization may not have the same level of expertise and resources as a public cloud provider, which can make it more difficult to properly maintain and update the infrastructure

It’s important for organizations to carefully evaluate their specific needs and constraints, and weigh them against the benefits of community cloud, before deciding to use community cloud services. Additionally, it’s important for organizations using a community cloud to establish clear governance and service-level agreements with other members of the community to ensure smooth operation and prevent conflicts.

Important note

Mostly within organizations in the industry, you would observe a multi-cloud architecture. A part of that reason is that each cloud vendor delivers a particular service in a more efficient way that fits the use case of the application. For those reasons, it is very important to avoid vendor lock-in. This is only feasible if the application is developed in a cloud-native way.

Cloud computing service categories

Cloud computing service categories refer to different levels of abstraction and control over the underlying infrastructure, and they provide different types of services and capabilities. These can be seen in the following diagram:

Figure 1.1 – Cloud service model

Let’s take a closer look.

IaaS

IaaS is a cloud computing service category that provides virtualized computing resources over the internet. IaaS providers offer a range of services, including servers, storage, and networking, which can be rented on demand, rather than you having to build and maintain the infrastructure in-house. IaaS providers typically use virtualization technology to create a pool of resources that can be used by multiple customers.

IaaS providers typically offer a range of services, including the following:

• Virtual machines (VMs): Customers can rent VMs with specific configurations of CPU, memory, and storage. This allows them to run their operating systems and applications on VMs.
• Storage: IaaS providers offer various storage options, such as block storage, object storage, and file storage, that customers can use to store their data.
• Networking: IaaS providers offer virtual networks that customers can use to connect their VMs and storage to the internet, as well as to other VMs and services.

The advantages of using IaaS include the following:

• Cost savings: Organizations can rent computing resources on demand, rather than building and maintaining their own infrastructure. This can help reduce capital and operational expenses.
• Scalability: Organizations can easily scale their computing resources up or down as needed, which can help improve cost-efficiency and performance.
• Flexibility: Organizations can choose from a range of VM configurations and storage options, which can help improve performance and security.
• Improved disaster recovery: Organizations can use IaaS providers to create backups and replicas of their VMs and storage in different locations, which can help improve disaster recovery and business continuity.

Here are the disadvantages of using IaaS:

• Limited control: Organizations may not have the same level of control and customization as they would with their own on-premises infrastructure
• Security concerns: Organizations are responsible for securing their VMs and storage, but they may not have the necessary expertise or resources to properly secure their data and applications

PaaS

PaaS is a category of cloud computing services that provides a platform for developers to build, test, and deploy applications without the complexity of managing the underlying infrastructure. PaaS providers typically offer a web server, database, and other tools needed to run an application, such as programming languages, frameworks, and libraries.

PaaS providers typically offer a range of services, such as the following:

• Development tools and environments, such as integrated development environments (IDEs), version control systems, and debugging tools.
• Deployment and scaling tools, such as automatic load balancing and scaling, and easy rollback and roll-forward of application versions.
• Database services, such as SQL and NoSQL databases, and data storage services.
• Security and compliance features, such as encryption, authentication, and access controls.
• Monitoring and analytics tools, such as logging, performance monitoring, and error reporting.
• Examples of popular PaaS providers include Heroku, AWS Elastic Beanstalk, and Google App Engine. These providers offer a variety of services and tools to help developers quickly and easily build, test, and deploy their applications, without the need to manage the underlying infrastructure. Additionally, PaaS providers often offer usage-based pricing models, making them cost-effective for small and medium-sized businesses.

Let’s look at some of the advantages of using PaaS:

• Faster time to market: Developers can quickly build, test, and deploy applications without the need to manage the underlying infrastructure, which can help reduce the time to market for new applications.
• Scalability: PaaS providers often offer automatic scaling, which allows applications to scale up or down as needed, based on usage or demand
• Lower costs: PaaS providers often offer pay-as-you-go pricing models, which can help reduce costs for small and medium-sized businesses
• Reduced complexity: PaaS providers often offer pre-configured development environments and tools, which can help reduce the complexity of application development and deployment
• Improved collaboration: PaaS providers often offer collaboration tools, such as version control systems, which can help improve collaboration among developers

Here are some of the disadvantages of using PaaS:

• Limited control: Developers may not have the same level of control and customization as they would with their own infrastructure or with an IaaS provider
• Vendor lock-in: Developers may become reliant on the PaaS provider’s tools and services, which can make it difficult to switch providers in the future
• Compatibility issues: Applications developed on one PaaS provider may not be compatible with another provider, which can limit flexibility and portability
• Security concerns: Developers are responsible for securing their applications and data, but they may not have the necessary expertise or resources to properly secure their applications and data

SaaS

SaaS is a software delivery model in which a software application is hosted by a third-party provider and made available to customers over the internet. SaaS providers manage and maintain the infrastructure, security, and scalability of the software, while customers access the software through a web browser or other remote means.

SaaS applications are typically subscription-based, with customers paying a monthly or annual fee for access. They can be used for a wide range of purposes, including customer relationship management, enterprise resource planning, and human resources management, among others.

SaaS applications are often accessed through a web browser but can also be accessed through mobile apps. They can be used by businesses of all sizes and in a variety of industries, from small start-ups to large enterprise companies. A few examples of applications with SaaS offerings are Jira, Office 365, and Stripe.

The advantages of using SaaS include the following:

• Easy access: SaaS applications can be accessed from anywhere with an internet connection, making it convenient for users to access applications from any location or device.
• Scalability: SaaS providers often offer automatic scaling, which allows applications to scale up or down as needed, based on usage or demand.
• Lower costs: SaaS providers often offer pay-as-you-go pricing models, which can help reduce costs for small and medium-sized businesses. Additionally, SaaS providers are responsible for maintaining the underlying infrastructure and software, which can help reduce IT costs for organizations.
• Faster implementation: SaaS applications can be quickly deployed, often within hours or days, without the need for hardware or software installation.
• Improved collaboration: SaaS applications often include collaboration tools, such as document sharing and project management tools, which can help improve collaboration among team members.

The disadvantages of using SaaS include the following:

• Limited control: Users may not have the same level of control and customization as they would with on-premises software
• Security concerns: SaaS providers are responsible for securing the underlying infrastructure and software, but users are responsible for securing their data and applications
• Dependence on internet connectivity: SaaS applications require a reliable internet connection, and downtime or slow internet speeds can impact productivity and user satisfaction
• Data ownership: Users may have limited control over their data, and there may be limitations on exporting or transferring data to other systems
• Vendor lock-in: Users may become reliant on the SaaS provider’s applications and services, which can make it difficult to switch providers in the future

Overall, SaaS is a popular and cost-effective way for businesses to access and use software applications without the need to manage and maintain the underlying infrastructure

As organizations increasingly adopt cloud computing to improve their agility, scalability, and cost-effectiveness, it’s becoming critical to think “cloud-native” when designing, building, and deploying applications in the cloud. Cloud-native is an approach that emphasizes the use of cloud computing services, microservices architecture, and containerization to enable applications to be developed and deployed in a more efficient, flexible, and scalable manner.

To help organizations assess their cloud-native capabilities and maturity, the CNCF has developed the Cloud Native Maturity Model (CNMM) 2.0. This model provides a framework for organizations to evaluate their cloud-native practices across four levels of maturity: starting out, building momentum, maturing, and leading. Each level includes a set of best practices and capabilities that organizations should strive for as they progress toward cloud-native excellence. By following this model, organizations can ensure that they are building and deploying cloud applications that are optimized for performance, resilience, and scalability, and that can adapt to the dynamic nature of the cloud computing landscape.

CNMM 2.0

CNMM 2.0 is a framework that helps organizations assess and improve their capabilities in developing, deploying, and operating cloud-native applications. It provides a set of best practices and guidelines for designing, building, and running cloud-native applications, along with a set of metrics and indicators to measure an organization’s progress and maturity level in implementing these best practices.

The model defines four maturity levels, each representing a different stage of cloud-native maturity – Initial, Managed, Proactive, and Optimized. Each level builds on the previous one and has a set of specific characteristics, best practices, and goals that organizations need to achieve to advance to the next level.

CNMM 2.0 is designed to be flexible and adaptable and can be used in any organization, regardless of its size, industry, or cloud provider. It’s not limited to a specific cloud service provider.

It’s a continuously evolving model that’s updated regularly to reflect the latest trends and best practices in cloud-native development and operations.

CNMM 2.0 is a framework that is structured around four maturity levels and four key components. Let’s take a look.

Maturity levels

The model defines four maturity levels that organizations can achieve in developing, deploying, and operating cloud-native applications. These levels are displayed in the following diagram:

Figure 1.2 – CNMM paradigm

• Level 1 – Initial: This level represents an organization’s first steps toward cloud-native development and deployment. Organizations at this level may have limited experience with cloud-native technologies and may rely on manual processes and ad hoc solutions.

Here are the characteristics of this level:

• Limited use and understanding of cloud-native technologies
• Monolithic application architecture
• Limited automation and orchestration
• Manual scaling and provisioning of resources
• Limited monitoring and analytics capabilities
• Basic security measures

Here are the challenges and limitations:

• Difficulty in scaling and managing the application
• A limited understanding of these technologies makes the implementation more error-prone and time-consuming
• Limited ability to respond to changes in demand
• Lack of flexibility and agility
• Limited ability to diagnose and troubleshoot issues
• Increased risk of security breaches
• Limited cost optimization

• Level 2 – Managed: This level represents a more mature approach to cloud-native development and deployment, with a focus on automation, governance, and standardization. Organizations at this level have implemented basic cloud-native best practices and have a clear understanding of the benefits and limitations of cloud-native technologies.

Here are the characteristics of this level:

• Adoption of cloud-native technologies
• Microservices architecture
• Automated scaling and provisioning of resources
• Basic monitoring and analytics capabilities
• Improved security measures

Here are the challenges and limitations:

• Difficulty in managing the complexity of microservices
• Limited ability to optimize resources
• Limited ability to diagnose and troubleshoot issues
• Limited ability to respond to changes in demand
• Limited cost optimization

• Level 3 – Proactive: This level represents an advanced level of cloud-native maturity, with a focus on continuous improvement, proactive monitoring, and optimization. Organizations at this level have implemented advanced cloud-native best practices and have a deep understanding of the benefits and limitations of cloud-native technologies.

Here are the characteristics of this level:

• Advanced use of cloud-native technologies and practices
• Self-healing systems
• Advanced automation and orchestration
• Advanced monitoring and analytics capabilities
• Advanced security measures
• Optimization of resources

Here are the challenges and limitations:

• Complexity in maintaining and updating automation and orchestration
• Difficulty in keeping up with the fast-paced evolution of cloud-native technologies
• Difficulty in maintaining compliance with security and regulatory requirements

• Level 4 – Optimized: This level represents the highest level of cloud-native maturity, with a focus on innovation, experimentation, and optimization. Organizations at this level have implemented leading-edge cloud-native best practices and have a deep understanding of the benefits and limitations of cloud-native technologies.

Here are the characteristics of this level:

• Fully optimized use of cloud-native technologies and practices
• Continuous integration and delivery
• Predictive analytics and proactive problem resolution
• Advanced security measures
• Cost optimization

Here are the challenges and limitations:

• Difficulty in keeping up with the latest trends and innovations in cloud-native technologies
• Difficulty in implementing advanced security measures
• Difficulty in maintaining cost optimization

Key components

The model defines four key components that organizations need to focus on to achieve different maturity levels. These components are depicted in the following figure:

Figure 1.3 – Software deployment component realm

Let’s take a look at each component one by one:

• Application Architecture

Application architecture refers to the design and structure of a cloud-native application. It includes characteristics, such as microservices architecture, containerization, cloud agnosticism, and continuous delivery and deployment, all of which are specific to cloud-native applications. These characteristics allow for greater flexibility and scalability in deployment and management on a cloud platform. Best practices for designing and building cloud-native applications include starting small and growing incrementally, designing for failure, using cloud-native services, and leveraging automation.

Here are the characteristics of cloud-native architecture:

• Microservices architecture: Cloud-native applications are typically built using a microservices architecture, which involves breaking down a monolithic application into smaller, independent services that can be deployed and managed separately. This allows for greater flexibility and scalability in deployment and management on a cloud platform.
• Containerization: Cloud-native applications are often packaged and deployed using containers, which are lightweight, portable, and self-sufficient units that can run consistently across different environments. This allows for greater consistency and ease of deployment across different cloud providers and on-premises environments.
• Cloud-agnostic: Cloud-native applications are designed to be cloud-agnostic, meaning they can run on any cloud platform and can easily be moved from one platform to another. This allows for greater flexibility in choosing a cloud provider and in avoiding vendor lock-in.
• Continuous delivery and deployment: Cloud-native applications are designed to make use of automated processes and tools for development and operations, such as CI/CD to speed up the development and deployment cycle.

Let’s look at the best practices for designing and building cloud-native applications:

• Starting small and grow incrementally: Start with a small, simple service and incrementally add more services as needed. This allows for a more manageable and scalable development process.
• Designing for failure: Cloud-native applications should be designed to handle failures gracefully, such as by using circuit breakers, load balancers, and self-healing mechanisms.
• Using cloud-native services: Utilize the native services provided by the cloud platform, such as databases, message queues, and storage services, to reduce the need for custom infrastructure.
• Leveraging automation: Automate as much of the development and deployment process as possible. An example would be to use IaC and CI/CD tools to speed up the development and deployment cycle.

• Automation and Orchestration

Automation and orchestration are key components in cloud-native environments as they help speed up the development and deployment cycle, ensure consistency and reliability in the deployment process, and enable teams to focus on more strategic and value-adding activities. Automation can be achieved by using configuration management tools such as Ansible, Puppet, or Chef to automate the provisioning and configuration of infrastructure, using container orchestration platforms such as Kubernetes, Docker Swarm, or Mesos to automate the deployment, scaling, and management of containers, and using CI/CD tools such as Jenkins, Travis CI, or CircleCI to automate the build, test, and deployment process.

Let’s look at the importance of automation in cloud-native environments:

• Automation helps speed up the development and deployment cycle, reducing the time and cost of launching applications to market
• Automation also helps ensure consistency and reliability in the deployment process, reducing the risk of human error
• Automation enables teams to focus on more strategic and value-adding activities

Here are the best practices for automation and orchestration:

• Use an automation tool such as Ansible, Puppet, or Chef to automate the process of provisioning and configuring the infrastructure
• Use container orchestration platforms such as Kubernetes, Docker Swarm, or Mesos to automate the deployment, scaling, and management of containers
• Use CI/CD tools such as Jenkins, Travis CI, or CircleCI to automate the build, test, and deployment process
• Use a service mesh such as Istio or Linkerd to automate how service-to-service communication is managed

• Monitoring and Analytics

Monitoring and analytics are crucial in cloud-native environments as they help ensure the availability and performance of cloud-native applications, provide insights into the behavior and usage of the applications, and help identify and troubleshoot issues. Best practices for monitoring and analytics include using a centralized logging and monitoring solution such as Elasticsearch, Logstash, and Kibana (ELK). For monitoring metrics and Telemetry, Prometheus and Grafana are commonly used together to collect and visualize system and application-level metrics. Additionally, you can use a distributed tracing system such as Jaeger or Zipkin to trace requests and transactions across microservices and use an application performance monitoring (APM) solution such as New Relic, AppDynamics, or Datadog to monitor the performance of individual services and transactions.

Let’s look at the importance of monitoring and analytics in cloud-native environments:

• Monitoring and analytics help ensure the availability and performance of cloud-native applications
• Monitoring and analytics can provide insights into the behavior and usage of the applications, allowing teams to optimize the applications and make informed decisions
• Monitoring and analytics also help you identify and troubleshoot issues, allowing teams to resolve problems quickly and effectively

Here are the best practices for monitoring and analytics:

• Use a centralized logging and monitoring solution such as ELK
• Use a distributed tracing system such as Jaeger or Zipkin to trace requests and transactions across microservices
• Use an APM solution such as New Relic, AppDynamics, Prometheus, or Datadog to monitor the performance of individual services and transactions
• Use an A/B testing and experimentation platforms such as Optimizely or Google Optimize to conduct experiments and test new features
• Use a Business Intelligence (BI) tool such as Tableau, Looker, or Power BI to analyze data and generate reports

• Security

Security is an essential component in cloud-native environments as applications and data are often spread across multiple cloud providers, making them more vulnerable to attacks. It’s also crucial to protect sensitive data, such as personal information, financial data, and intellectual property. Best practices for securing cloud-native applications include using a cloud-native security platform, using a secrets management tool, using a network security solution, using an identity and access management (IAM) solution, using encryption to protect data at rest and in transit, and implementing a vulnerability management solution to scan, identify, and remediate vulnerabilities regularly.

Let’s look at the importance of security in cloud-native environments:

• Security is crucial in a cloud-native environment as applications and data are often spread across multiple cloud providers, making them more vulnerable to attacks
• Security is also critical in a cloud-native environment to protect sensitive data, such as personal information, financial data, and intellectual property
• Security is a key part of compliance with regulations, such as the HIPAA, SOC2, and the GDPR

Here are the best practices for securing cloud-native applications:

• Use a cloud-native security platform such as Prisma Cloud, Aqua Security, or StackRox to provide security across the entire application life cycle.
• Use a secrets management tool such as Hashicorp Vault, AWS Secrets Manager, or Google Cloud Secret Manager to securely store and manage sensitive data.
• Use a network security solution such as AWS Security Groups, Google Cloud Firewall Rules, or Azure Network Security Groups to secure ingress/egress network traffic.
• Use an IAM solution such as AWS IAM, Google Cloud IAM, or Azure Active Directory to control access to resources and services.
• Use encryption to protect data at rest and in transit. Multiple cloud vendors provide native cryptographic key signing solutions for encryption; they should be regularly revoked and rotated.
• Implement a vulnerability management solution to scan, identify, and remediate vulnerabilities regularly.

CNMM 2.0 provides a set of best practices, metrics, and indicators for each of these four key components, along with a roadmap for organizations to follow as they progress through the four maturity levels. It’s designed to be flexible and adaptable, allowing organizations to choose which components and maturity levels they want to focus on, based on their specific needs and goals.

Components of a cloud-native system

As such, multiple projects are a part of the CNCF. For this book, I have agglomerated the platforms and tools that we will use in depth in this book, along with the use case for each platform. However, I strongly recommend that you check out a lot of the others at https://landscape.cncf.io/:

Figure 1.4 – CNCF platform landscape

We will be looking at tools from the following categories:

• Orchestration
• Application development
• Monitoring
• Logging
• Tracing
• Container registries
• Storage and databases
• Runtimes
• Service discoveries and service meshes
• Service proxy
• Security
• Streaming
• Messaging

Important note

You must have a preliminary understanding of how/why these platforms are used in a real system design since the following chapters on threat modeling and secure system design require you to understand how each platform works independently within a cloud-native system, as well as how it integrates with other platforms/tooling/automated processes within the cloud-native system. Also, all the platforms that will be discussed here are cloud-vendor-agnostic.

Orchestration

One of the key projects within the cloud-native space, and the project that we will focus most of our time on, is Kubernetes. Let’s take a closer look.

Kubernetes

Kubernetes is a container orchestration system. It allows you to deploy, scale, and manage containerized applications, which are applications that are packaged with all their dependencies, making them more portable and easier to run in different environments.

Kubernetes uses a concept called pods, which are the smallest and simplest units in the Kubernetes object model that you can create or deploy. Each pod represents a single instance of a running process in your application. Multiple pods can be grouped to form a higher-level structure called a ReplicaSet, which ensures that a specified number of replicas of the pod are running at any given time.

Furthermore, Kubernetes also provides a feature called Services, which allows you to expose your pods to external traffic. It also provides a feature called Ingress, which allows you to route external traffic to multiple services based on the URL path.

Additionally, Kubernetes provides advanced features, such as automatic rolling updates, self-healing, and automatic scaling, which makes it easy to manage and maintain a large number of containers, with some limitations on the number of pods and nodes.

Overall, Kubernetes provides a powerful and flexible platform for deploying and managing containerized applications at scale, making it easier to run, scale, and maintain applications in a production environment.

Monitoring

Multiple tools exist for monitoring code performance, security issues, and other data analytics within the code base, all of which can be leveraged by developers and security engineers. Anecdotally, the following platforms have been widely used in the industry within production environments with the least downtime and the best ease of use.

Prometheus

Prometheus is an open source monitoring and alerting system. It is commonly used for monitoring and alerting on the performance of cloud-native applications and infrastructure.

Prometheus scrapes metrics from different targets, which could be a system, an application, or a piece of infrastructure, and stores them in a time-series database. It also allows users to query and analyze the metrics and set up alerts based on those metrics.

Prometheus is a time-series database that is designed to be highly scalable, and it can handle a large number of metrics, making it suitable for monitoring large-scale systems. It also has a built-in query language called PromQL, which allows users to perform complex queries on the metrics, and a rich set of visualization tools such as Grafana that can be used to display the metrics in a user-friendly way.

Prometheus is also a CNCF project. It is a well-established monitoring tool in the cloud-native ecosystem and is often used in conjunction with other CNCF projects such as Kubernetes.

In summary, Prometheus is an open source monitoring and alerting system that is designed for cloud-native applications and infrastructure. It allows users to scrape metrics from different targets, store them in a time-series database, query and analyze the metrics, and set up alerts based on those metrics. It is also highly scalable and allows for easy integration with other tools and frameworks in the cloud-native ecosystem.

Grafana

Grafana is a powerful tool that allows you to visualize and analyze data in real time. It supports a wide variety of data sources and can be used to create highly customizable dashboards.

One of the key features of Grafana is that it supports Prometheus, a popular open source monitoring and alerting system. Prometheus allows you to collect time-series data from your cloud-native applications and infrastructure, and Grafana can be used to visualize this data in the form of graphs, tables, and other visualizations. This makes it easy to quickly identify trends, patterns, and anomalies in your data and can be used to monitor the health and performance of your systems.

In addition to its visualization capabilities, Grafana also allows you to set up alerts and notifications based on specific thresholds or conditions. For example, you can set up an alert to notify you if the CPU usage of a particular service exceeds a certain threshold, or if the response time of an API exceeds a certain limit. This can help you quickly identify and respond to potential issues before they become critical.

Another of its features is its ability to create a shared dashboard, which allows multiple users to access and interact with the same set of data and visualizations. This can be useful in a team or organization where multiple people are responsible for monitoring and troubleshooting different parts of the infrastructure.

Overall, Grafana is a powerful and flexible tool that can be used to monitor and troubleshoot cloud-native applications and infrastructure.

Logging and tracing

The logical next step after monitoring the deployments is to log the findings for code enhancements and perform trace analysis.

Fluentd

Fluentd is a popular open source data collection tool for the unified logging layer. It allows you to collect, parse, process, and forward logs and events from various sources to different destinations. Fluentd is designed to handle a large volume of data with low memory usage, making it suitable for use in high-scale distributed systems.

Fluentd has a flexible plugin system that allows for easy integration with a wide variety of data sources and outputs. Some common data sources include syslog, HTTP, and in-application logs, while common outputs include Elasticsearch, Kafka, and AWS S3. Fluentd also supports various message formats, such as JSON, MessagePack, and Apache2.

Fluentd can also filter and transform data as it is being collected, which allows you to do things such as drop unimportant events or add additional fields to the log.

It also has a built-in buffering mechanism that helps mitigate the impact of downstream outages and a robust error-handling mechanism that can automatically retry to send the logs in case of failure.

Fluentd’s ability to handle a wide variety of data sources and outputs, along with its ability to filter and transform data, makes it a powerful tool for managing and analyzing log data in large-scale distributed systems.

Elasticsearch

Elasticsearch is a distributed, open source search and analytics engine designed for handling large volumes of data. It is often used in cloud-native environments to provide full-text search capabilities and real-time analytics for applications.

One of the main benefits of Elasticsearch for cloud-native environments is its ability to scale horizontally. This means that as the volume of data or the number of users increases, additional nodes can be added to the cluster to handle the load, without requiring any downtime or reconfiguration. This allows Elasticsearch to handle large amounts of data, and still provide low-latency search and analytics capabilities.

Elasticsearch also has built-in support for distributed indexing and searching, which allows data to be partitioned across multiple nodes and searched in parallel, further increasing its ability to handle large volumes of data.

In addition to its scalability, Elasticsearch provides a rich set of features for indexing, searching, and analyzing data. It supports a wide variety of data types, including text, numerical, and date/time fields, and it allows you to perform complex search queries and analytics using its powerful query language, known as the Elasticsearch Query DSL.

Elasticsearch also provides a RESTful API for interacting with the data, making it easy to integrate with other systems and applications. Many popular programming languages have Elasticsearch client libraries that make it even easier to interact with the engine.

Finally, Elasticsearch has a built-in mechanism for handling data replication and sharding, which helps ensure that data is available and searchable even in the event of a node failure. This makes it suitable for use in cloud-native environments where high availability is a requirement.

Overall, Elasticsearch is a powerful tool for managing and analyzing large volumes of data in cloud-native environments, with features such as horizontal scalability, distributed indexing and searching, a rich set of features for indexing, searching, and analyzing data, and built-in support for data replication and sharding.

Kibana

Kibana is a data visualization tool that is commonly used in conjunction with Elasticsearch, a search and analytics engine, to explore, visualize, and analyze data stored in Elasticsearch indices.

In a cloud-native environment, Kibana can be used to visualize and analyze data from various sources, such as logs, metrics, and traces, which is collected and stored in a centralized Elasticsearch cluster. This allows for easy and efficient analysis of data across multiple services and environments in a cloud-based infrastructure.

Kibana can be deployed as a standalone application or as a part of the Elastic Stack, which also includes Elasticsearch and Logstash. It can be run on-premises or in the cloud and can easily be scaled horizontally to handle large amounts of data.

Kibana offers a variety of features for data visualization, such as creating and customizing dashboards, creating and saving visualizations, and creating and managing alerts. Additionally, it provides a user-friendly interface for searching, filtering, and analyzing data stored in Elasticsearch.

In a cloud-native environment, Kibana can easily be deployed as a containerized application using Kubernetes or other container orchestration platforms, allowing you to easily scale and manage the application.

Overall, Kibana is a powerful tool for exploring, visualizing, and analyzing data in a cloud-native environment and can be used to gain valuable insights from data collected from various sources.

Container registries

Within the cloud-native realm, each microservice is deployed within a container. Since they are frequently used within the production environment, it is critical to think about the container registry to be used, and how they’re going to be used.

Harbor

Harbor is an open source container registry project that provides a secure and scalable way to store, distribute, and manage container images. It is designed to be a private registry for enterprise usage but can also be used as a public registry. Harbor is built on top of the Docker Distribution open source project and extends it with additional features such as role-based access control (RBAC), vulnerability scanning, and image replication.

One of the key features of Harbor is its support for multiple projects, which allows you to organize and separate images based on their intended usage or ownership. Each project can have its own set of users and permissions, allowing for fine-grained control over who can access and manage images.

Another important feature of Harbor is its built-in vulnerability scanning capability, which scans images for known vulnerabilities and alerts administrators of any potential risks. This helps ensure that only secure images are deployed in production environments.

Harbor also supports image replication, which allows you to copy images between different Harbor instances, either within the same organization or across different organizations. This can be useful for organizations that have multiple locations or that want to share images with partners.

In terms of deployment, Harbor can be deployed on-premises or in the cloud and can be easily integrated with existing infrastructure and workflows. It also supports integration with other tools such as Kubernetes, Jenkins, and Ansible.

Overall, Harbor is a feature-rich container registry that provides a secure and scalable way to store, distribute, and manage container images and helps ensure the security and compliance of containerized applications.

Service meshes

A service mesh is a vital component in cloud-native environments that helps manage and secure communication between microservices. It provides visibility and control over service-to-service communication, simplifies the deployment of new services, and enhances application reliability and scalability. With a service mesh, organizations can focus on developing and deploying new features rather than worrying about managing network traffic.

Istio

Istio is an open source service mesh that provides a set of security features to secure communication between microservices in a distributed architecture. Some of the key security features of Istio include the following:

• Mutual TLS authentication: Istio enables mutual Transport Layer Security (TLS) authentication between service instances, which ensures that only authorized services can communicate with each other. This is achieved by automatically generating and managing X.509 certificates for each service instance and using these certificates for mutual authentication.
• Access control: Istio provides RBAC for services, which allows for fine-grained control over who can access and manage services. This can be used to enforce security policies based on the identity of the service or the end user.
• Authorization: Istio supports service-to-service and end user authentication and authorization using JSON Web Token (JWT) and OAuth2 standards. It integrates with external authentication providers such as Auth0, Google, and Microsoft Active Directory to authenticate end users.
• Auditing: Istio provides an audit log that records all the requests and responses flowing through the mesh. This can be useful for monitoring and troubleshooting security issues.
• Data protection: Istio provides the ability to encrypt payloads between services, as well as to encrypt and decrypt data at rest.
• Distributed tracing: Istio provides distributed tracing of service-to-service communication, which allows you to easily identify issues and perform troubleshooting in a distributed microservices architecture.
• Vulnerability management: Istio integrates with vulnerability scanners such as Aqua Security and Snyk to automatically detect and alert administrators of any vulnerabilities in the images used for the service.

Overall, Istio provides a comprehensive set of security features that can be used to secure communication between microservices in a distributed architecture. These features include mutual TLS authentication, access control, authorization, auditing, data protection, distributed tracing, and vulnerability management. These features can be easily configured and managed through Istio’s control plane, making it simple to secure a microservices environment.

Security

Security provisions have to be applied at multiple layers of the cloud environment, so it is also critical to understand each platform and tool available at our disposal.

Open Policy Agent

Open Policy Agent (OPA) is an open source, general-purpose policy engine that can be used to enforce fine-grained, context-aware access control policies across a variety of systems and platforms. It is especially well suited for use in cloud-native environments, where it can be used to secure and govern access to microservices and other distributed systems.

One of the key features of OPA is its ability to evaluate policies against arbitrary data sources. This allows it to make access control decisions based on a wide range of factors, including user identity, system state, and external data. This makes it an ideal tool for implementing complex, dynamic access control policies in cloud-native environments.

Another important feature of OPA is its ability to work with a variety of different policy languages. This makes it easy to integrate with existing systems and tools and allows developers to express policies in the language that best suits their needs.

OPA is often used in conjunction with service meshes and other service orchestration tools to provide fine-grained access control to microservices. It can also be used to secure Kubernetes clusters and other cloud-native infrastructure by enforcing policies at the network level.

In summary, OPA is a powerful and flexible policy engine that can be used to enforce fine-grained, context-aware access control policies across a variety of systems and platforms. It’s well suited for use in cloud-native environments, where it can be used to secure and govern access to microservices and other distributed systems.

Falco

Falco is an open source runtime security tool that is designed for use in cloud-native environments, such as Kubernetes clusters. It is used to detect and prevent abnormal behavior in containers, pods, and host systems, and can be integrated with other security tools to provide a comprehensive security solution.

Falco works by monitoring system calls and other kernel-level events in real time and comparing them against a set of predefined rules. These rules can be customized to match the specific requirements of an organization and can be used to detect a wide range of security issues, including privilege escalation, network communications, and file access.

One of the key features of Falco is its ability to detect malicious activity in containers and pods, even if they are running with elevated privileges. This is important in cloud-native environments, where containers and pods are often used to run critical applications and services, and where a security breach can have serious consequences.

Falco can also be used to detect and prevent abnormal behavior on the host system, such as unexpected changes to system files or attempts to access sensitive data. This makes it an effective tool for preventing malicious actors from gaining a foothold in a cloud-native environment.

Falco can be easily integrated with other security tools, such as firewalls, intrusion detection systems, and incident response platforms. It also supports alerting through various channels, such as syslog, email, slack, webhooks, and more.

In summary, Falco is an open source runtime security tool that is designed for use in cloud-native environments. It monitors system calls and other kernel-level events in real time and compares them against a set of predefined rules. This allows it to detect and prevent abnormal behavior in containers, pods, and host systems, making it an effective tool for securing cloud-native applications and services.

Calico

Calico is an open source networking and security solution that can be used to secure Kubernetes clusters. It is built on top of the Kubernetes API and provides a set of operators that can be used to manage and enforce network policies within a cluster.

One of the key security use cases for Calico is network segmentation. Calico allows administrators to create and enforce fine-grained network policies that segment a cluster into different security zones. This can be used to isolate sensitive workloads from less-trusted workloads and prevent unauthorized communication between different parts of a cluster.

Another security use case for Calico is the ability to control traffic flow within a cluster. Calico allows administrators to create and enforce policies that govern the flow of traffic between different pods and services. This can be used to implement micro-segmentation, which limits the attack surface of a cluster by restricting the communication between vulnerable workloads and the external environment.

Calico also provides a feature called Global Network Policy, which allows you to define network policies that span multiple clusters and namespaces, enabling you to secure your multi-cluster and multi-cloud deployments.

Calico also supports integration with various service meshes such as Istio, enabling you to secure your service-to-service communication in a more fine-grained way.

In summary, Calico is an open source networking and security solution that can be used to secure Kubernetes clusters. It provides a set of operators that can be used to manage and enforce network policies within a cluster, which can be used for network segmentation, traffic flow control, and securing multi-cluster and multi-cloud deployments. Additionally, it integrates with service meshes to provide more fine-grained service-to-service communication security.

Kyverno

Kyverno is an open source Kubernetes policy engine that allows administrators to define, validate, and enforce policies for their clusters. It provides a set of operators that can be used to manage and enforce policies for Kubernetes resources, such as pods, services, and namespaces.

One of the key security use cases for Kyverno is to enforce security best practices across a cluster. Kyverno allows administrators to define policies that ensure that all resources in a cluster comply with a set of security standards. This can be used to ensure that all pods, services, and namespaces are configured with the appropriate security settings, such as appropriate service accounts, resource limits, and labels.

Another security use case for Kyverno is to provide automated remediation of security issues. Kyverno allows administrators to define policies that automatically remediate security issues when they are detected. This can be used to automatically patch vulnerabilities, rotate secrets, and reconfigure resources so that they comply with security best practices.

Kyverno also provides a feature called Mutate, which allows you to make changes to the resource definition before the resource is created or updated. This feature can be used to automatically inject sidecar containers, add labels, and set environment variables.

Kyverno also supports integration with other security tools such as Falco, OPA, and Kube-Bench, allowing you to build a more comprehensive security strategy for your cluster.

In summary, Kyverno is an open source Kubernetes policy engine that allows administrators to define, validate, and enforce policies for their clusters. It provides a set of operators that can be used to manage and enforce policies for Kubernetes resources, such as pods, services, and namespaces. It can be used to enforce security best practices across a cluster, provide automated remediation of security issues, and integrate with other security tools to build a more comprehensive security strategy for a cluster.

There are multiple tools and platforms available at the disposal of every software engineer within the cloud-native realm. It is important to understand the use case and application of those platforms. When it comes to the model that the product is designed on, you should choose the most efficient and scalable platform.

In this chapter, we tried to provide a clear definition of what we would venture into in this book. I strongly encourage you to read the documentation of the platforms mentioned in this chapter as we will leverage them in this book further and learn about implementing security controls and solutions within any system and application.

With the rise of cloud-native architecture, more companies are adapting to this technique. With that, security engineers and security champions must update their skill sets based on recent updates. In the next chapter, we will be doing a deep dive into understanding secure code development and leveraging the cloud-native approach, as well as a few of the tools discussed in this chapter, to create security solutions for software development.

Answer the following questions to test your knowledge of this chapter:

• Why would you want to use cloud-native architecture?
• Why do we care about cloud-native security?
• What are a few components of cloud-native architecture?
• How would you advocate for adopting a cloud-native architecture for your project?

To learn more about the topics that were covered in this chapter, take a look at the following resources:

• https://landscape.cncf.io/
• https://community.cncf.io/cncf-cartografos-working-group/
• https://kubernetes.io/docs/home/
• https://www.redhat.com/en/topics/cloud-native-apps