DevSecOps

Deliver Faster Data Value with DataOps The world of data analytics is rapidly accelerating. To stay competitive and agile, organisations need to continually adapt and invest strategically in their data culture, processes, and data platforms to ensure there is strategic alignment to the needs of their business, while enabling better agility, improved time-to-insight & higher quality data delivered to end-users. By leveraging DataOps practices, organisations can deliver faster data value in a cost-effective manner, enabling businesses to adapt and uncover insights with agility. DataOps is a lifecycle practice and collection of workflows, standards, and architecture patterns that drive agility and innovation to orchestrate data movement from data producers to data consumers, enabling the output of high-quality data with improved security. The Key Objectives of DataOps The primary objectives of DataOps (Data Operations) are to streamline and improve the overall management and delivery of data within an organisation. There are many benefits that can be reaped from leveraging DataOps practices which are summarised below: The building blocks of DataOps practices To reap the full benefits of DataOps practices requires strategic planning & investment into the organisation’s data culture. The following are a few building blocks and steps that can be taken to fully embrace DataOps practices: Conclusion: DataOps aims to enhance the overall effectiveness, efficiency, and value of data operations within an organisation, ultimately driving better business outcomes and data-driven decision-making. As the market of data analytics is rapidly accelerating, the adoption of DataOps practices is continuing to gain momentum. Organisations that wholeheartedly embrace DataOps practices and invest in driving and fostering a data-driven culture will be ideally positioned to deliver faster data value to identify opportunities and challenges and make faster decisions with confidence.

Navigating the Future of Software Development The world of software development is rapidly changing. To stay competitive, organisations need to not only keep up with the changes but also strategically adopt methods that improve agility, security, and dependability. The emergence of cloud computing, microservices, and containers has given rise to an innovative approach to creating and deploying software in a cloud-native way. Cloud-native applications are designed to be scalable, resilient, and secure, and they are often delivered through DevOps or DevSecOps methodologies. The markets for cloud-native development, platform engineering, and DevSecOps are all witnessing substantial growth, fuelled by the growing demand for streamlined software development practices and heightened security protocols. This article will explore how the intersection of cloud-native development, platform engineering, and DevSecOps is reshaping the landscape of software development.  Cloud Native Development: Building for the Future Cloud-native development represents a significant transformation in the approach to designing and deploying software. It revolves around crafting applications specifically tailored for cloud environments. These applications are usually constructed from microservices, which are compact, self-contained units collaborating to provide the application’s features. This architectural approach endows cloud-native applications with superior scalability and resilience when compared to conventional monolithic applications.  Key Benefits of Cloud Native Development:  Platform Engineering: The Glue that Holds It Together  Platform engineering is the bridge between development and operations. It is about providing the tools and infrastructure that developers need to build, test, and deploy their applications seamlessly. Think of it as an internal developer platform, offering a standardised environment for building and running software.  Why Platform Engineering Matters:  DevSecOps: Weaving Security into the Fabric  DevSecOps extends the DevOps philosophy by emphasising the integration of security into every phase of the software development lifecycle. It shifts security from being an afterthought to an initiative-taking and continuous process.  The Importance of DevSecOps:  Embarking on the Cloud Native, Platform Engineering, and DevSecOps Odyssey  While there exist various avenues for implementing cloud-native, platform engineering, and DevSecOps practices, the optimal approach hinges on an organisation’s unique requirements. Nevertheless, some overarching steps that organisations can consider include:  In summation, cloud-native development, platform engineering, and DevSecOps are not mere buzzwords; they are strategic mandates for organisations aiming to flourish in the digital era. These practices pave the way for heightened agility, cost-effectiveness, security, and reliability in software development.  Conclusion: As market intelligence attests, the adoption of these practices is not decelerating; it is gaining momentum. Organisations that wholeheartedly embrace cloud-native development, invest in platform engineering, and prioritise DevSecOps will be ideally positioned to navigate the challenges and seize the opportunities of tomorrow. The moment to embark on this transformative journey is now, ensuring that your software development processes are not just future-ready but also primed to deliver value at an unprecedented velocity and with unwavering security. 

The State of Observability 2023: Unlocking the Power of Observability The State of Observability 2023 study, recently released by Splunk, provides insights into the crucial role observability plays in minimising costs related to unforeseen disruptions in digital systems. In the fast-paced and intricate digital landscapes of today, observability has emerged as a beacon of light, illuminating the path towards efficient monitoring and oversight. Gone are the days of relying solely on traditional monitoring methods; observability offers a holistic perspective of complex systems by gathering and analysing data from diverse sources across the entire technology stack. With its comprehensive approach, observability has become an indispensable tool for comprehending the inner workings of digital ecosystems.  While DevOps and cloud-native architectures have become cornerstones of digital transformation, they also introduce a host of intricate observability challenges. The hurdles faced by organisations when implementing observability and security in Kubernetes were brought into focus in this year’s State of Observability survey conducted by Splunk. Respondents acknowledged the difficulties of effectively monitoring Kubernetes itself, which serves as a significant obstacle to achieving complete observability in their environments.  Now, let us explore some of the main findings uncovered in this report.  Main discoveries from this survey Observability leaders outshine beginners: Those who have embraced observability as a core practice outperform their counterparts in various aspects. These leaders experience a staggering 7.9 times higher return on investment (ROI) with observability tools, showing 3.9 times more confidence in meeting requirements, and resolving downtime or service issues four times faster.  The expanding observability ecosystem: The study reveals that the observability landscape has witnessed a recent surge in the adoption of observability tools and capabilities. An impressive 81% of respondents reported using an increasing number of observability tools, with 32% noting a significant rise. However, managing multiple vendors and tools presents a challenge when it comes to achieving a unified view for IT professionals.  Changing expectations around cloud-native apps: While the percentage of respondents expecting a larger portion of internally developed apps to be cloud-native has declined (from 67% to 58%), there has been an increase in those anticipating the same proportion (from 32% to 40%). A small percentage (2%) expects a decrease. This shift highlights the evolving landscape of application development and the growing importance of cloud-native technologies.  The convergence of observability and security monitoring: Organisations are recognising the benefits of merging observability and security monitoring disciplines. By combining these practices, enhanced visibility and faster incident resolution can be achieved, ensuring the overall robustness of digital systems.  Harnessing the power of AI and ML: AI and ML have become integral components of observability practices, with 66% of respondents already incorporating them into their workflows. An additional 26% are in the process of implementing these advanced technologies, leveraging their capabilities to gain deeper insights and drive proactive monitoring.  Centralised teams and talent challenges: Organisations are increasingly consolidating their observability experts into centralised teams equipped with standardised tools (58%), rather than embedding them within application development teams (42%). However, recruiting observability talent remains a significant challenge, with difficulties in hiring ITOps team members (85%), SRE (86%), and DevOps engineers (86%) being highlighted.  Conclusion In conclusion, observability has become an indispensable force in today’s hypercomplex digital environments. By providing complete visibility and context across the full stack, observability empowers organisations to ensure digital health, reliability, resilience, and high performance. Building a centralised observability capability enables proactive monitoring, issue detection and diagnosis, performance optimisation, and enhanced customer experiences. This goes beyond simply adopting tools into a more strategic approach that involves rolling out standardised practices across the full stack in which both platform teams and application teams participate to build and consume. As digital ecosystems continue to evolve, harnessing the power of observability will be key to unlocking the full potential of modern technologies and achieving digital transformation goals.

Kubernetes container design patterns Kubernetes is a robust container orchestration tool, but deploying and managing containerised applications can be complex. Fortunately, Kubernetes container design patterns can help simplify the process by segregating concerns, enhancing scalability and resilience, and streamlining management. In this blog post, we will delve into five popular Kubernetes container design patterns, showcasing real-world examples of how they can be employed to create powerful and effective containerised applications. Additionally, we’ll provide valuable insights and tool recommendations to help you implement these patterns with ease. Sidecar Pattern: The first design pattern we’ll discuss is the sidecar pattern. The sidecar pattern involves deploying a secondary container alongside the primary application container to provide additional functionality. For example, you can deploy a logging sidecar container to collect and store logs generated by the application container. This improves the scalability and resiliency of your application and simplifies its management. Similarly, you can deploy a monitoring sidecar container to collect metrics and monitor the health of the application container. The sidecar pattern is a popular design pattern for Kubernetes, with many open-source tools available to simplify implementation. For example, Istio is a popular service mesh that provides sidecar proxies to handle traffic routing, load balancing, and other networking concerns. Ambassador Pattern: The ambassador pattern is another popular Kubernetes container design pattern. This pattern involves using a proxy container to decouple the application container from its external dependencies. For example, you can use an API gateway as an ambassador container to handle authentication, rate limiting, and other API-related concerns. This simplifies the management of your application and improves its scalability and reliability. Similarly, you can use a caching sidecar container to cache responses from external APIs and reduce latency and improve performance. This ensures that the application is properly configured and ready to run when the primary container runs. The ambassador pattern is commonly used for API management in Kubernetes. Tools like Nginx,Kong and Traefik provide API gateways that can be deployed as ambassador containers to handle authentication, rate limiting, and other API-related concerns. Adapter Pattern: The adapter pattern is another powerful Kubernetes container design pattern. This pattern involves using a container to modify an existing application to make it compatible with Kubernetes. For example, you can use an adapter container to add health checks, liveness probes, or readiness checks to an application that was not originally designed to run in a containerised environment. This can help ensure the availability and reliability of your application when running in Kubernetes. Similarly, you can use an adapter container to modify an application to work with Kubernetes secrets, environment variables, or other Kubernetes-specific features. The adapter pattern is often used to migrate legacy applications to Kubernetes. Tools like Kubernetes inlets and kompose provide an easy way to convert Docker Compose files to Kubernetes YAML and make the migration process smoother Sidecar injector Pattern: The sidecar injector pattern is another useful Kubernetes container design pattern. This pattern involves dynamically injecting a sidecar container into a primary application container at runtime. For example, you can inject a container that performs security checks and monitoring functions into an existing application container. This can help improve the security and reliability of your application without having to modify the application container’s code or configuration. Similarly, you can inject a sidecar container that provides additional functionality such as authentication, rate limiting, or caching. The Sidecar Injector pattern is a dynamic method of injecting sidecar containers into Kubernetes applications during runtime. By utilizing the Kubernetes admission controller webhook, the injection process can be automated to guarantee that the sidecar container is always present when the primary container initiates. An excellent instance of the Sidecar Injector pattern is the HashiCorp Vault Injector, which enables the injection of secrets into pods. Init container pattern: Finally, the init container pattern is a valuable Kubernetes container design pattern. This pattern involves using a separate container to perform initialization tasks before the primary application container starts. For example, you can use an init container to perform database migrations, configuration file generation, or application setup. This ensures that the application is properly configured and ready to run when the primary container. In conclusion, Kubernetes container design patterns are essential for building robust and efficient containerised applications. By using these patterns, you can simplify the deployment, management, and scaling of your applications. The patterns we discussed in this blog are just a few examples of the many design patterns available for Kubernetes, and they can help you build powerful and reliable containerised applications that meet the demands of modern cloud computing. Whether you’re a seasoned Kubernetes user or just starting out, these container design patterns are sure to help you streamline your containerised applications and take your development to the next level.

Maximising ROI and Minimising OPEX with Kubernetes At TL Consulting, we offer specialised services in managing Kubernetes instances, including AKS, EKS, and GKE, as well as bare metal setups and VMWare Tanzu on private cloud. Our Kubernetes consulting services are tailored to help businesses optimise their IT costs and improve their ROI, enabling them to leverage the full potential of Kubernetes. We streamline operations, optimise resource utilisation, and reduce infrastructure expenses, ensuring that our clients get the most out of their Kubernetes deployments. Thus ensuring that your teams are maximising Kubernetes ROI while minimising IT costs. With our expertise, we can work with organisations to assess their current infrastructure and identify areas where Kubernetes can be implemented to achieve better ROI. Our services cover advisory, design and architecture, engineering, and operations. We guide organisations on containerisation, scalability, and automation best practices to optimise their use of Kubernetes. We provide customised Kubernetes solutions and ensure seamless implementation, management, and maintenance. With our help, businesses can streamline operations, enhance resource utilisation, and reduce infrastructure costs. We do not just provide one-off Kubernetes solutions. We’re committed to ongoing management and support, staying up to date with the latest innovations and best practices in Kubernetes. By collaborating with us, organisations can stay ahead of the curve and continue to optimise their IT costs and improve their ROI over time. Our partnership ensures that businesses can adapt and thrive in an ever-changing technological landscape, confidently leveraging Kubernetes’ full potential. Additionally, we offer a cloud-agnostic approach to Kubernetes, enabling businesses to choose the cloud platform that best fits their requirements. Our team provides guidance on cloud platform selection, deployment, and optimisation to ensure that clients can maximise their investments in the cloud. We specialise in multi-cloud approaches, making it seamless for organisations to manage Kubernetes across various cloud providers.

What can we expect for Kubernetes in 2023? As Kubernetes approaches the eighth anniversary of its first version launch, we look into the areas of significant change. So what does the Kubernetes ecosystem look like and What can we expect for Kubernetes in 2023? In short, is huge and continues to grow. As more businesses, teams, and people use it as a platform for innovation, more new applications will be created and old ones will be scaled more quickly than ever before, fuelling its continual development. The State of Kubernetes 2022 study from VMware Tanzu and the most recent Annual Cloud Native Computing Foundation (CNCF) Survey both indicate that Kubernetes is widely adopted and continues to grow in popularity as a platform for container orchestration. These studies suggest that Kubernetes has become a de facto standard in the industry and its adoption will likely continue to increase in the coming years. Anticipated Shift towards Kubernetes on multi cloud As we move forward into 2023, it’s becoming increasingly common for businesses to utilize multiple cloud providers for their Kubernetes deployments. This trend, known as multi-cloud/hybrid deployments, often involves the use of container orchestration and federated development and deployment strategies. While there are already tools available for deploying and managing containers across a variety of cloud providers and on-premises platforms, we can expect to see even more advancements in this area. Specifically, there will likely be an increase in technology that makes it easier to create and deploy multi-cloud systems using native cloud services that work seamlessly across different providers. Multi-cloud adoption allows businesses to take advantage of the strengths of different cloud providers, such as leveraging the best database solutions from one provider and the best serverless offerings from another. This approach can also increase flexibility, reduce vendor lock-in, and provide redundancy and disaster recovery options. Additionally, it can allow for cost optimization by taking advantage of different pricing models and promotions offered by different providers. Continual Evolution of DevOps and Platform Teams: To survive in this digital age, businesses need to have a diverse set of skills and knowledge areas within their workforce. Close collaboration between different departments and disciplines is essential for leveraging new technologies like Kubernetes and other cloud platforms. However, these technologies can be difficult to learn and maintain, and teams may struggle to gain in-depth understanding of them. Businesses should focus on automation and acceleration, but also invest in training and development programs to help their teams acquire the necessary skills to effectively use these technologies. Companies of all sizes should think about where they want to develop their Kubernetes knowledge base. Many businesses choose a platform team to develop and implement this knowledge. Multiple DevOps teams can be supported by a single platform team. This separation allows DevOps teams to continue concentrating on creating and running business applications while the platform team looks after a solid and dependable underpinning platform. Improved Stateful Application Management: Containers were originally intended to be a means of operating stateless applications. However, the value of running stateful workloads in containers has been recognised by the community over the last few years, and the newer versions of Kubernetes have added the required functionalities. Now there are better ways to deploy stateful applications, but the outcome is far from ideal and inconsistent. By including a controller in the cluster, K8s operators can resolve this difficulty. Reconciliation loops are controller loops that monitor differences between the current and intended states and adjust return the current state to the desired state. Maturity in Policy-as-Code for Kubernetes The goal has been to give teams more autonomy when delivering applications to Kubernetes for several years. In many businesses today, creating pipelines that can quickly send out apps is standard procedure. Although having autonomy is a great advantage, maintaining some manual control still requires finding the proper balance. The transition to everything as a code has opened a plethora of opportunities. Following accepted engineering principles will make it simple to validate and review policies defined as-code. As a result, the importance of policy frameworks will increase. Within the CNCF, Open Policy Agent (OPA) is the most common policy framework. Practices like this will advance concurrently with the adoption of Kubernetes and autonomous teams to enable continual growth while preserving or even gaining more control. Adoption enables you to control how Kubernetes is used by a wide range of teams. Enhanced Observability and Troubleshooting capabilities: Troubleshooting applications running on a Kubernetes cluster at scale can be challenging due to the complexity of Kubernetes and the relationships between different elements. Providing teams with effective troubleshooting solutions can give an organization a competitive advantage. The Four elements (Events, Logs, Traces, Metrics) are important in understanding the performance and behaviour of a system. They provide different perspectives and details on system activity, and when combined, give a more complete picture of the issue. Solutions that integrate these four elements can aid in faster troubleshooting and problem resolution and can also help in identifying and preventing future issues. Vendors and open-source frameworks will continue to drive this trend. Focus on supply chain security: Software supply chain security has been in laser sights for a while now, as most software rooted from other software. The necessity of ensuring Kubernetes’ strength has increased along with its importance as it becomes more widely adopted, it is important to ensure its security as it is a critical component of the software supply chain. This includes securing the infrastructure on which it runs, as well as securing the containerized applications that are deployed on it. The “4C’s of cloud native security” model is a good place to start thinking about the security of the different layers of a cloud native application: Cloud, Clusters, Containers, and Code. Each layer of the Cloud Native security model builds upon the next outermost layer, and they are equally important when considering security practices and tools. This can be done through a variety of methods, such as using secure configurations, implementing network

Progressive Delivery (the GitOps way) with Kubernetes: One of the biggest challenges organisations faces, especially when running microservices, is managing application deployments. Having a proper deployment strategy is necessary. For instance, in a production environment, it is always a change management process requirement to ensure that the downtime impact on the end-user is minimised and maintenance windows need to be planned to cater for any changes that will cause an outage. It is also mandated that in case of any issues when deploying the change, a rollback plan must be ready for execution to recover from any failures. These challenges amplify with the increase of the number of microservices and makes it more difficult to assess the result of the deployment and execute the rollback if required. Enter progressive delivery. Thankfully, cloud native architectures using Kubernetes running microservices addresses this problem by offering increased flexibility, allowing teams to publish more useful updates more frequently and progressively. The use of release techniques like Canary, Blue-Green, and Feature flagging as part of progressive delivery enables teams to maximise an enterprise’s software delivery. It is predicated on the notion that consumers desire to test features prior to completion to enhance the user experience. In Kubernetes, there are different ways to release an application. It is necessary to choose the right strategy to make the infrastructure reliable and more resilient during an application deployment or update. The out of the box Kubernetes Deployment Object supports the Rolling Update strategy which comes as a standard and provides a basic set of safety guarantees (aka. readiness probes) during an update. When deploying into a development/staging environment, standard Kubernetes deployment strategies such as a recreate or rolling deployment might be a good option. However, the rolling update strategy faces may limitations such as controlling the speed and flow of the rollout. in large scale high-volume production environments, a rolling update is often considered too risky of an update procedure since it provides no control over the blast radius, may rollout too aggressively, and provides no automated rollback upon failures. In production environments, more advanced deployment strategies are much needed to satisfy the business requirements. These advanced strategies are called “Progressive Deployments”. An example of these deployment strategies is the Blue/Green deployment which allows for a quick transition between the old version and the new version by deploying them side by side and then switching to the new version when testing has been successful. This testing of the platform needs to be thorough to avoid having to rollback frequently. If unsure of the platform’s stability or the potential effects of releasing a new software version, a canary deployment offers a smaller scale next version of the release running side by side the current version in production. By doing so, the new release is rolled-out to a small subset of users to test the application and provide their feedback. Once the change is accepted, it is rolled out to the rest of the users. Benefits of Progressive Delivery: Progressive delivery lowers the risk of releasing new features, as well as identifying and resolving possible issues with those additions. It also offers early feedback on any version of your application. Before a feature is fully deployed, the developer can test out various changes on the product to see how the application behaves. The idea is that the developer can alter the release strategy if the modifications are unfavourable to prevent end users from experiencing any glitches. Secondly, improved release frequency results from sequential delivery. While the primary goal of progressive delivery is to provide end users with safer, more dependable releases, you as the DevOps team will benefit from being able to deploy new versions in smaller parts and hence release more frequently. You can work on each feature separately and release it in tiny sprints. The time to market is shortened, and any DevOps team can now deploy better software more quickly. Finally, and this is something that is frequently ignored, progressive delivery leads to improved segregation of duties between the development and operations teams. This segregation of duties works better with progressive delivery since developers concentrate on creating new features while operations concentrate on rolling out the new features gradually in a strategy that suits the operational needs of the platform. Progressive delivery is best achieved with GitOps: This demand for progressive delivery in a cloud native manner can be achieved with GitOps. The objective behind GitOps is to define and declare everything in Git including operational tasks. Git is already used by developers to generate and collaborate on code. GitOps simply expands this concept to include the creation and setup of infrastructure as well. Git becomes the control plane for operations and deployments because everything is declared as code in Git. GitOps is being enabled by open-source tooling such as ArgoCD, Flux and Flagger, which automatically checks Git repositories for any new changes, and if it detects a change, it automatically deploys it to production. With progressive delivery, these automated deployments need to be done in phases and to multiple target Kubernetes clusters. These tools offer full control of the software delivery pipeline, rollback strategies, test executions, feature releases, and scaling of infrastructure resources. In conclusion, there are various methods for deploying an application to cater for applications with varying complexities, teams with different demands, and environments with different operational requirements and compliance levels. Selecting the right strategy or strategies and having full control over these strategies in code when combined by the right tools is an extremely powerful feature of cloud native platforms that greatly simplifies change management, release management, and operations of the applications. It completely disrupts the way operations teams traditionally thought of these processes as rigid and extremely sensitive with a dramatic business impact in case anything went wrong, into simplified processes and tasks that can be executed every day in the background without having an impact on the end-user.

Transforming the Way We Manage Data Data is the backbone of today’s digital economy. With the ever-increasing volume of data being generated every day, the need for efficient, scalable, and robust data management solutions is more pressing than ever. Enter Kubernetes, the revolutionary open-source platform that’s changing the game of data management. Market research suggests that the demand for Kubernetes in data management is growing at a rapid pace, with a projected compound annual growth rate of over 30% by 2023. There is an increase in demand for Kubernetes. With its ability to automate deployment, scaling and management of containerized applications, is providing organisations with a new way to approach data management. By leveraging its container orchestration capabilities, Kubernetes is making it possible to handle complex data management tasks with ease and efficiency. Stateful applications, such as databases and data pipelines, are the backbone of any data management strategy. Traditionally, managing these applications has been a complex and time-consuming task. But with Kubernetes, stateful applications can be managed with ease, thanks to its Persistent Volumes and Persistent Volume Claims. Data pipelines, the critical component of data management, are transforming the way organizations process, transform and store data. Kubernetes makes it possible to run data pipelines as containers, simplifying their deployment, scaling, and management. With Kubernetes in-built jobs support, these workflows can run as a scheduled or triggered jobs that are orchestrated by the Kubernetes engine. This enables organizations to ensure the reliability and efficiency of their data pipelines, even as the volume of data grows. Scalability is a major challenge in data management, but with Kubernetes, it is by design. Its ability to horizontally scale the number of nodes in a cluster makes it possible to easily handle the growing volume of data. This ensures that data management solutions remain robust and scalable, even as data volumes increase. Resilience in another key requirement in data management. Traditionally, a single point of failure can bring down the entire system. But with Kubernetes, failures are handled gracefully, with failed containers automatically rescheduled on healthy nodes. This provides peace of mind, knowing that data management solutions remain available even in the event of failures. Kubernetes also offers zero downtime deployment in the form of rolling updates. This also applies to databases where the administrator can upgrade the database version without any impact to the service by rolling the update to one workload at a time until all replicas are upgraded. To complement the resilience features, operations such as memory or CPU upgrades which, in the past, were considered destructive changes that required planning and careful change and release management. Today, since Kubernetes relies on declarative management of its objects, this change is just a single line of code. This change can be deployed similar to any code change that progresses to the different environments using CI/CD pipelines. Conclusion In conclusion, Kubernetes is transforming data management. Gone are the days of regarding Kubernetes as a platform suitable only for stateless workloads leaving databases running on traditional VMs. Many initiatives took place to adapt stateful workloads to run efficiently and reliably in Kubernetes from releasing the StatefulSets API and Storage CSI, to building Kubernetes operators that will ensure databases can run securely in the cluster with massive resilience and scalability. With these operators being released for common database systems such as Postgres and mySQL to name a few, daunting database operations such as automatic backups, rolling updates, high availability and failover are simplified and taken care of in the background transparent to the end user. Today, with more database vendors either releasing or endorsing Kubernetes operators for their database systems, and enterprises running databases in Kubernetes production environments successfully, there is no reason to think that it lacks the necessary features to run production enterprise database systems. The future of data management is looking bright, and we excitedly await what lies ahead thanks to the Kubernetes community’s constant drive for innovation and the expansion of the possibilities. To learn more about Kubernetes and our service offering here.