Cloud Architecture

KubeVirt offers a bridge between virtual machines and containerized environments. As an open-source project, its standout feature is the ability to run VMs and containers side by side. But while the concept is promising, several caveats remain for organizations that need to support critical at-scale VM workloads. The CNCF project also reflects how containers are not going to replace VMs, while the reverse may be true in the long term for many use cases. Gartner estimates that through 2028, technical and operational limitations will restrict adoption of KubeVirt to less than 10% of on-premises production virtual workloads in enterprise environments. KubeVirt’s lackluster adoption forecast by Gartner is largely due to how its functionality has been shown to properly support only a limited number of virtual machines running alongside containers. Most of the management and security features that today’s VM infrastructure provides are not available in KubeVirt, in favor of bringing VMs under the Kubernetes management control plan. For organizations seeking to port a significant or large number of VMs and integrate them with Kubernetes, the project lacks the necessary support in several key areas. Backed by industry players such as NVIDIA, Red Hat, and others, the project has the kind of support that could drive the improvements it still needs. However, it will likely be over a decade before that potential is fully realized, while existing virtual machine infrastructure providers already offer the capacity to run VMs alongside containers today and likely into the extended future. The Works KubeVirt remains in the incubation stage after entering the CNCF as a sandbox project in 2019 and advancing to the incubation maturity level in 2022. Designed for organizations that have virtual machine workloads and want to adopt a Kubernetes control plane, KubeVirt serves as a platform that allows for both to run side by side. The KubeVirt Kubernetes Virtualization API and runtime are designed to define and manage virtual machines. However, additional functionality is required that another tool provides: Libvirt is used to integrate VMs with a KVM hypervisor so they can be launched and managed within Kubernetes pods. According to the project’s documentation, KubeVirt is largely limited to very basic declarative usages, such as: Creating a predefined VM.Scheduling a VM on a Kubernetes cluster.Launching a VM.Stopping a VM.Deleting a VM. According to Gartner, KubeVirt’s use cases include Infrastructure provisioning to create and destroy short-lived non-production virtual environments (including development and lab environments. Once KubeVirt is successfully implemented, its functionality is significantly limited compared to established VM-management offerings, especially those that can extend to Kubernetes, allowing applications running on containers or VMs to be managed on a single platform. Functionality Want KubeVirt provides very basic hypervisor admin features and is limited in use. For integrating a limited number of VMs, the relative lack of functionality and advanced operational capabilities, such as storage-management integration, does not support advanced features expected in VM management. Since VM-based infrastructure has been in use for several decades, VMs benefit from extensive industry understanding and ongoing innovation of management in this space. Ideally, VMs’ advanced capabilities must be available with containers in a common infrastructure. Since KubeVirt was created for Kubernetes-executing VMs, this means organizations using KubeVirt remain reliant on storage vendors that support the Container Storage Interface (CSI). According to Gartner, as of January 2025, among the listed CSI drivers: 54% do not support snapshots.49% do not support read/write to multiple pods.57% do not support expansion. CSI requirements can disrupt many storage environments that use many common features like snapshots and expansion. This stands in stark contrast to traditional storage solutions for virtual environments, whether based on external or software-defined storage. Proven, de facto APIs have enabled storage vendors to consistently offload storage functions for virtual workloads. Examples include cloning, migration, provisioning, reclamation, and access control. According to Michael Warrilow, a vice president and Gartner analyst, “Most enterprises are likely to find that re-virtualization of existing production virtual workloads will be the most technically challenging, risky, and difficult to justify through at least 2028.” Integration with storage systems is not a default implementation feature of KubeVirt. Without a standardized storage element that attaches to or is standard for KubeVirt, different storage vendors may or may not easily work with or offer support for it. While organizations can manage existing VMs with KubeVirt, the management capabilities are minimal and limited to basic hypervisor administration. VMs continue to exist within the infrastructure, but KubeVirt lacks many of the advanced operational and life cycle management features that enterprise-grade virtualization platforms provide. Skill Ups Organizations continue to rely on virtual machines because they offer operational simplicity, proven efficiency, and a lower total cost of ownership (TCO). Furthermore, the existing talent pool is better aligned with VM management; hiring skilled professionals in traditional VM environments remains considerably easier than sourcing equivalent expertise in containerized or Kubernetes-native infrastructures. In comparison to traditional VM platforms, Kubernetes introduces significantly more operational complexity. This architectural shift imposes substantial friction, as it requires organizations to move away from established VM management practices and toward a Kubernetes-centric approach. While Kubernetes offers powerful orchestration capabilities, it also demands a completely new skill set. Virtual infrastructure administrators must be retrained, and organizations must reskill entire teams to manage existing VM workloads through a new control plane such as KubeVirt. Today’s VM operations are supported by mature orchestration tools, advanced features, and hardened security and compliance frameworks. Introducing virtual machines into a Kubernetes-native environment via KubeVirt would require organizations to overhaul their tooling and significantly reskill technical teams — an effort that carries considerable cost and operational risk. As such, the rationale for running VMs inside Kubernetes clusters using KubeVirt remains limited to highly specific use cases and is not recommended for general enterprise adoption. For organizations in regulated or mission-critical sectors, such as banking, federal, state, and local governments, utilities, or retail, the risks associated with adopting such a nascent technology like KubeVirt are considerable. Unlike technology leaders such as NVIDIA, Google, or Meta, which have internal engineering capabilities to customize and support open source tools, most enterprises are not equipped to manage this level of technical complexity independently. KubeVirt is purpose-built for Kubernetes environments, yet the requirements for enterprise adoption remain considerable. The platform operates on the assumption that all workloads will eventually be migrated to Kubernetes. However, such transitions are rarely immediate; even upgrades between Kubernetes versions can take several months — or, in some cases, years. “This is an evident gap in skills that will need to be overcome to successfully adopt KubeVirt, whether for production or not. Many existing I&O personnel will lack proficiency and experience in modern, cloud-native tools and methodologies,” Warrilow writes. “Implementing DevOps requires significant investment in both technology and training, which has proven to be a barrier to widespread adoption. KubeVirt will force this change.” This perspective is consistent with observations made by Gartner, which has noted that vendors such as Red Hat, with its OpenShift platform, have adopted KubeVirt as a strategic entry into the VM management market. Developing a mature, enterprise-grade VM management platform typically requires many years, if not decades, of sustained engineering and operational refinement, regardless of whether the solution is open source or proprietary. Conclusion KubeVirt is a very well-supported project and can serve as a solid sandbox project for those organizations exploring different virtualization options for VMs and Kubernetes. The healthy number of stars and forks notwithstanding, very few organizations with significant resources can afford to rely on such a young project (Version 1.0 of KubeVirt was released in July 2023) to integrate VMs with Kubernetes and container infrastructure. It is recommended that for the foreseeable future, its use remains limited to the management of small numbers of VMs — think tens of VMs for a sandbox project, and not a thousand VMs that support critical operations. As we have mentioned above, we advise against porting large-scale VM deployments — over 100 VMs — to Kubernetes using KubeVirt. This kind of re-virtualization implies that you are likely taking a managed VM infrastructure and forfeiting several key security, advanced features, storage, and management benefits for the sake of running Kubernetes or containers and VMs side by side within a cluster environment. Achieving ROI would be a challenge. Conversely, platforms exist that have been tried and tested to run both VMs and Kubernetes on a single platform, while preserving all the benefits of a well-managed VM and Kubernetes infrastructure.

By Chris Ward CORE

Legacy systems in financial institutions often lead to scalability bottlenecks, security risks, and poor resiliency. In the modern digital economy, banks, payment providers, and fintech firms need infrastructure that is cost-effective, agile, and resilient. Cloud-based microservices have emerged as a modern approach to address these needs. By breaking monolithic systems into modular services, financial firms can accelerate innovation, reduce downtime. By achieving these, they can meet rising expectations for secure and real-time digital transactions. Understanding Cloud-Based Microservices in Fintech What are Microservices? Microservices are an architectural style where an application is built through a modular approach instead of a large applications. Financial services are broken into small, independent services that communicate among themselves securely. This ensures that the Financial system is resilient, which means if one service fails, the rest are running. It provides scalability, which implies each service can be scaled based on the needs without impacting others. It also ensures secure systems where each service follows its strict security guidelines. Why Financial Institutions Need Them? Traditional banking and payment systems are built with monolithic architecture and struggle to meet the current demand, which also slows down innovation. Adopting cloud-based microservices can overcome most of the challenges and have substantial benefits. It increases performance by scaling seamlessly during peak transaction volumes (e.g., Black Friday, stock market surges). It processes the transaction faster and reduces downtime from cyberattacks. Industry Drivers for Modernization Digital Payments Acceleration: According to McKinsey’s 2023 Global Payments Report [1], digital payments surpassed $9 trillion globally, with real-time transactions accounting for a significant share. Financial institutions must modernize to meet growing demands for instant processing and 24/7 service availability. Regulatory Compliance Pressure: Financial services face increasingly complex regulatory frameworks such as PCI DSS for payment security, GDPR for data privacy, and local requirements like the U.S. SEC and OCC guidelines. Meeting these mandatory requirements calls for secure, auditable, and resilient cloud-native infrastructures [2]. Rising Cost of Legacy Systems: A 2022 Accenture report [3] found that maintaining legacy IT systems costs financial institutions 60% more annually compared to cloud-native counterparts, driven by infrastructure, licensing, and operational overhead. Evolving Fraud Threats: With fraud attacks becoming more sophisticated (LexisNexis Risk Solutions reported global fraud costs reaching $42 billion in 2022 [4]), banks require real-time, scalable detection systems that can integrate AI models and process large volumes of transaction data without delays. Key Benefits of Cloud-Based Microservices in Financial Systems Cloud-based microservices provide many benefits to financial institutions across operational efficiency, security, and technology modernization. Economically, these architectures enable faster transaction processing by reducing latency and optimizing resource allocation. They also lower infrastructure expenses by replacing monolithic legacy systems with modular, scalable services that are easier to maintain and operate. Furthermore, the shift to cloud technologies increases demand for specialized roles in cloud operations and cybersecurity. In security operations, microservices support zero-trust architectures and data encryption to reduce the risk of fraud and unauthorized access. Cloud platforms also enhance resilience by offering built-in redundancy and disaster recovery capabilities, which help ensure continuous service and maintain data integrity in the event of outages or cyber incidents. From a technology perspective, microservices improve system flexibility and scalability, allowing financial firms to more easily adopt emerging technologies as needed. By decoupling services and following cloud-first principles, organizations reduce dependence on legacy infrastructure while positioning themselves for agile and sustainable modernization. Technical Implementation in Financial Systems To build secure and scalable financial microservices, there are a few key technology stacks needed. They include Docker and Kubernetes containerization for managing multiple microservices, and cloud functions for serverless computing, which will be used to run calculations on demand. API Gateways will ensure that there is secure communication between services and Kafka for real-time data monitoring and streaming. Let's take a simple example and analyze a fraud detection scenario using microservices. To prevent unauthorized transactions, financial systems need real-time fraud detection mechanisms to be in place. A microservices-based fraud detection application can analyze transactions instantly and block them due to suspicious activity. Here's a sample Kafka code in Step 1 that deploys Kafka streaming transactions: YAML version: '3' services: zookeeper: image: wurstmeister/zookeeper ports: - "2181:2181" kafka: image: wurstmeister/kafka ports: - "9092:9092" environment: KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181 KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092 In Step 2, we implemented a Fraud Detection Microservice: Python from kafka import KafkaConsumer import json def detect_fraud(): consumer = KafkaConsumer('transactions', bootstrap_servers='kafka:9092') for msg in consumer: transaction = json.loads(msg.value) if transaction['amount'] > 10000: print(f"Fraud Alert! Suspicious Transaction Detected: {transaction}") # Trigger an alert or block the transaction detect_fraud() In Step 3, we automated the Cloud deployment using Serverless Functions. We used the Google Cloud Platform ( GCP ) example: Plain Text gcloud functions deploy fraud-detection \ --runtime python39 \ --trigger-http \ --allow-unauthenticated Result: When a transaction exceeds the $10,000 threshold, a trigger is initiated to send alerts instantly. The fraud detection service runs in real time with minimal infrastructure cost. This scalable model also handles high transaction volumes effectively. Fintech Case Study: Banking Modernization Let's take a banking system that struggled with an outdated monolithic code base. This code base caused frequent downtimes, slowed transactions, and raised operational costs. The solution that the bank incorporated was migrating to a cloud-based microservices architecture, implementing autoscaling, serverless functions, and API gateways. The results were outstanding as shown below: Metric Before Migration After Migration System uptime 85% 99 Operational Cost High 30% Reduction Recovery Time (RTO) 2 hours 40% Faster Cybersecurity Risk High Reduced by 50% Conclusion Financial institutions globally face pressures from regulatory requirements, customer expectations, and operational risks. Cloud-based microservices provide a clear path forward to reduce costs by eliminating legacy infrastructure. Improve resiliency with fault-tolerant design. Enhance security using micro-isolation and zero-trust principles. Enable innovation through agile and scalable architectures. By adopting microservices, banks and fintech companies can stay competitive, meet regulatory demands, and deliver superior experiences to customers securely and efficiently.

Jenkins is an open-source CI/CD tool written in Java that is used for organising the CI/CD pipelines. Currently, at the time of writing this blog, it has 24k stars and 9.1k forks on GitHub. With over 2000 plugin support, Jenkins is a well-known tool in the DevOps world. The following are multiple ways to install and set up Jenkins: Using the Jenkins Installer package for WindowsUsing Homebrew for macOSUsing the Generic Java Package (war)Using DockerUsing KubernetesUsing apt for Ubuntu/Debian Linux OS In this tutorial blog, I will cover the step-by-step process to install and setup Jenkins using Docker Compose for an efficient and seamless CI/CD experience. Using Dockerwith Jenkins allows users to set up a Jenkins instance quickly with minimal manual configuration. It ensures portability and scalability, as with Docker Compose, users can easily set up Jenkins and its required services, such as volumes and networks, using a single YAML file. This allows the users to easily manage and replicate the setup in different environments. Installing Jenkins Using Docker Compose Installing Jenkins with Docker Compose makes the setup process simple and efficient, and allows us to define configurations in a single file. This approach removes the complexity and difficulty faced while installing Jenkins manually and ensures easy deployment, portability, and quick scaling. Prerequisite As a prerequisite, Docker Desktop needs to be installed, up and running on the local machine. Docker Compose is included in Docker Desktop along with Docker Engine and Docker CLI. Jenkins With Docker Compose Jenkins could be instantly set up by running the following docker-compose command using the terminal: Plain Text docker compose up -d This docker-compose command could be run by navigating to the folder where the Docker Compose file is placed. So, let’s create a new folder jenkins-demo and inside this folder, let’s create another new folder jenkins-configuration and a new file docker-compose.yaml. The following is the folder structure: Plain Text jenkins-demo/ ├── jenkins-configuration/ └── docker-compose.yaml The following content should be added to the docker-compose.yaml file. YAML # docker-compose.yaml version: '3.8' services: jenkins: image: jenkins/jenkins:lts privileged: true user: root ports: - 8080:8080 - 50000:50000 container_name: jenkins volumes: - /Users/faisalkhatri/jenkins-demo/jenkins-configuration:/var/jenkins_home - /var/run/docker.sock:/var/run/docker.sock Decoding the Docker Compose File The first line in the file is a comment. The services block starts from the second line, which includes the details of the Jenkins service. The Jenkins service block contains the image, user, and port details. The Jenkins service will run the latest Jenkins image with root privileges and name the container as jenkins. The ports are responsible for mapping container ports to the host machine. The details of these ports are as follows: 8080:8080:This will map the port 8080 inside the container to the port 8080 on the host machine. It is important, as it is required for accessing the Jenkins web interface. It will help us in accessing Jenkins in the browser by navigating to http://localhost:808050000:50000:This will map the port 50000 inside the container to port 50000 on the host machine. It is the JNLP (Java Network Launch Protocol) agent port, which is used for connecting Jenkins build agents to the Jenkins Controller instance. It is important, as we would be using distributed Jenkins setups, where remote build agents connect to the Jenkins Controller instance. The privileged: true setting will grant the container full access to the host system and allow running the process as the root user on the host machine. This will enable the container to perform the following actions : Access all the host devicesModify the system configurationsMount file systemsManage network interfacesPerform admin tasks that a regular container cannot perform These actions are important, as Jenkins may require permissions to run specific tasks while interacting with the host system, like managing Docker containers, executing system commands, or modifying files outside the container. Any data stored inside the container is lost when the container stops or is removed. To overcome this issue, Volumes are used in Docker to persist data beyond the container’s lifecycle. We will use Docker Volumes to keep the Jenkins data intact, as it is needed every time we start Jenkins. Jenkins data would be stored in the jenkins-configuration folder on the local machine. The /Users/faisalkhatri/jenkins-demo/jenkins-configuration on the host is mapped to /var/jenkins_home in the container. The changes made inside the container in the respective folder will reflect on the folder on the host machine and vice versa. This line /var/run/docker.sock:/var/run/docker.sock, mounts the Docker socket from the host into the container, allowing the Jenkins container to directly communicate with the Docker daemon running on the host machine. This enables Jenkins, which is running inside the container, to manage and run Docker commands on the host, allowing it to build and run other Docker containers as a part of CI/CD pipelines. Installing Jenkins With Docker Compose Let’s run the installation process step by step as follows: Step 1 — Running Jenkins Setup Open a terminal, navigate to the jenkins-demo folder, and run the following command: Plain Text docker compose up -d After the command is successfully executed, open any browser on your machine and navigate to https://localhost:8080, you should be able to find the Unlock Jenkins screen as shown in the screenshot below: Step 2 — Finding the Jenkins Password From the Docker Container The password to unlock Jenkins could be found by navigating to the jenkins container (remember we had given the name jenkins to the container in the Docker Compose file) and checking out its logs by running the following command on the terminal: Plain Text docker logs jenkins Copy the password from the logs, paste it in the Administrator password field on the Unlock Jenkins screen in the browser, and click on the Continue button. Step 3 — Setting up Jenkins The “Getting Started” screen will be displayed next, which will prompt us to install plugins to set up Jenkins. Select the Install suggested plugins and proceed with the installation. It will take some time for the installations to complete. Step 4 — Creating Jenkins user After the installation is complete, Jenkins will show the next screen to update the user details. It is recommended to update the user details with a password and click on Save and Continue. This username and password can then be used to log in to Jenkins. Step 5 — Instance Configuration In this window, we can update the Jenkins accessible link so it can be further used to navigate and run Jenkins. However, we can leave it as it is now — http://localhost:8080. Click on the Save and Finish button to complete the set up. With this, the Jenkins installation and set up are complete; we are now ready to use Jenkins. Summary Docker is the go-to tool for instantly spinning up a Jenkins instance. Using Docker Compose, we installed Jenkins successfully in just 5 simple steps. Once Jenkins is up and started, we can install the required plugin and set up CI/CD workflows as required. Using Docker Volumes allows us to use Jenkins seamlessly, as it saves the instance data between restarts. In the next tutorial, we will learn about installing and setting up Jenkins agents that will help us run the Jenkins jobs.

By Faisal Khatri CORE

Application performance, scalability, and resilience are critical for ensuring a seamless user experience. Apache JMeter is a powerful open-source tool for load testing, but running it on a single machine limits scalability, automation, and distributed execution. This blog presents a Kubernetes-powered JMeter setup on Azure Kubernetes Service (AKS), which can also be deployed on other cloud platforms like AWS EKS and Google GKE, integrated with CI/CD pipelines in Azure DevOps. This approach enables dynamic scaling, automated test execution, real-time performance monitoring, and automated reporting and alerting. Key Benefits of JMeter on AKS Run large-scale distributed load tests efficientlyAuto-scale worker nodes dynamically based on trafficAutomate test execution and result storage with CI/CDMonitor performance in real-time using InfluxDB & GrafanaGenerate automated reports and notify teams via email This guide follows a Kubernetes-native approach, leveraging: ConfigMaps for configuration managementDeployments for master and worker nodesServices for inter-node communicationHorizontal Pod Autoscaler (HPA) for dynamic scaling While this guide uses Azure DevOps as an example, the same approach can be applied to other CI/CD tools like Jenkins, GitHub Actions, or any automation framework with minimal modifications. For CI/CD integration, the same setup can be adapted for Jenkins, GitHub Actions, or any other CI/CD tool. Additionally, this JMeter setup is multi-cloud compatible, meaning it can be deployed on AWS EKS, Google GKE, or any Kubernetes environment. To fully automate the JMeter load simulation process, we integrate it with CI/CD pipelines, ensuring tests can be triggered on every code change, scheduled runs, or manually, while also enabling automated reporting and alerting to notify stakeholders of test results. What This Guide Covers Service Connection Setup – Authenticate AKS using Azure Service Principal.CI Pipeline Setup – Validate JMeter test scripts upon code commits.CD Pipeline Setup – Deploy and execute JMeter tests in a scalable environment.Performance Monitoring – Using InfluxDB and Grafana for real-time observability.Automated Reporting & Alerts – Convert JTL reports into HTML, extract key metrics, and send email notifications.Best Practices – Managing secrets securely and optimizing resource usage. If your system fails under heavy traffic, it could mean revenue loss, poor user experience, or even security risks. Traditional performance testing tools work well for small-scale tests, but what if you need to simulate thousands of concurrent users across multiple locations? This is where Kubernetes-powered JMeter comes in! By deploying JMeter on Azure Kubernetes Service (AKS) and integrating it with CI/CD Pipelines, we can: Run large-scale distributed tests efficientlyScale worker nodes dynamically based on loadAutomate the entire process, from deployment to reporting and result analysis Key Challenges with Traditional JMeter Execution Limitations of Running JMeter on a Single Machine Resource bottlenecks – Can’t simulate real-world distributed loads.Manual execution – No automation or CI/CD integration.Scalability issues – Hard to scale up or down dynamically.Data management – Handling large test datasets is cumbersome. Challenge JMeter on Local Machine JMeter on AKS Scalability Limited by CPU/memory Auto-scales with HPA Automation Manual test execution CI/CD pipelines for automation Parallel Execution Hard to distribute Kubernetes distributes the load Observability No centralized monitoring Grafana + InfluxDB integration Cost Efficiency Wasted resources On-demand scaling By deploying JMeter on AKS, we eliminate bottlenecks and achieve scalability, automation, and observability. JMeter Architecture on AKS A distributed JMeter deployment consists of: JMeter Master Pod – Orchestrates test execution.JMeter Worker Pods (Slaves) – Generate the actual load.JMeter Service – Enables inter-pod communication.InfluxDB – Stores real-time performance metrics.Grafana – Visualizes test execution.Azure File Storage – Stores test logs and results.Horizontal Pod Autoscaler (HPA) – Adjusts worker count based on CPU utilization. Figure 1: JMeter Distributed Load Testing Architecture on Azure Kubernetes Service (AKS), showing how the Master node orchestrates tests, Worker Pods generate load, and InfluxDB/Grafana monitor performance. Adding Real-World Use Cases To make the blog more relatable, let’s add examples of industries that benefit from scalable performance testing. E-commerce & Retail: Load testing before Black Friday & holiday sales.Banking & FinTech: Ensuring secure, high-performance online banking.Streaming Platforms: Handling millions of concurrent video streams.Healthcare Apps: Load-testing telemedicine platforms during peak hours.Gaming & Metaverse: Performance testing multiplayer online games. Optimizing Costs When Running JMeter on AKS Running JMeter on Azure Kubernetes Service (AKS) is powerful, but without optimization, it can get expensive. Let’s add a section on cost-saving strategies: Use Spot Instances for Non-Critical TestsAuto-Scale JMeter Worker Nodes Based on LoadSchedule Tests During Non-Peak Hours to Save CostsMonitor and Delete Unused Resources After Test ExecutionOptimize Log Storage – Avoid Keeping Large Log Files on AKS Deploying JMeter on AKS Prerequisites Ensure you have: Azure subscription with AKS configured. kubectl and helm installed. JMeter Docker images for master and worker nodes. JMX test plans and CSV datasets for load execution. Azure Service Principal for CI/CD automation. Creating JMeter Docker Images Your setup requires different Dockerfiles for the JMeter Master and JMeter Worker (Slave) nodes. Dockerfile - JMeter Master Shell FROM ubuntu:latest RUN apt-get update && apt-get install -y openjdk-11-jdk wget unzip WORKDIR /jmeter RUN wget https://downloads.apache.org//jmeter/binaries/apache-jmeter-5.5.tgz && \ tar -xzf apache-jmeter-5.5.tgz && rm apache-jmeter-5.5.tgz CMD ["/jmeter/apache-jmeter-5.5/bin/jmeter"] Dockerfile - JMeter Worker (Slave) Shell FROM ubuntu:latest RUN apt-get update && apt-get install -y openjdk-11-jdk wget unzip WORKDIR /jmeter RUN wget https://downloads.apache.org//jmeter/binaries/apache-jmeter-5.5.tgz && \ tar -xzf apache-jmeter-5.5.tgz && rm apache-jmeter-5.5.tgz CMD ["/bin/bash"] Once built and pushed to Azure Container Registry, these images will be used in Kubernetes deployments. Deploying InfluxDB for Performance Monitoring To capture real-time test results, deploy InfluxDB, which stores metrics from JMeter. File: jmeter_influxdb_configmap.yaml YAML apiVersion: v1 kind: ConfigMap metadata: name: influxdb-config labels: app: influxdb-jmeter data: influxdb.conf: | [meta] dir = "/var/lib/influxdb/meta" [data] dir = "/var/lib/influxdb/data" engine = "tsm1" wal-dir = "/var/lib/influxdb/wal" [[graphite]] enabled = true bind-address = ":2003" database = "jmeter" File: jmeter_influxdb_deploy.yaml YAML apiVersion: apps/v1 kind: Deployment metadata: name: influxdb-jmeter labels: app: influxdb-jmeter spec: replicas: 1 selector: matchLabels: app: influxdb-jmeter template: metadata: labels: app: influxdb-jmeter spec: containers: - image: influxdb name: influxdb volumeMounts: - name: config-volume mountPath: /etc/influxdb ports: - containerPort: 8086 volumes: - name: config-volume configMap: name: influxdb-config File: jmeter_influxdb_svc.yaml YAML apiVersion: v1 kind: Service metadata: name: jmeter-influxdb labels: app: influxdb-jmeter spec: ports: - port: 8086 name: api targetPort: 8086 selector: app: influxdb-jmeter Deployment Command Shell kubectl apply -f jmeter_influxdb_configmap.yaml kubectl apply -f jmeter_influxdb_deploy.yaml kubectl apply -f jmeter_influxdb_svc.yaml Verify InfluxDB Shell kubectl get pods -n <namespace-name> | grep influxdb Deploying Jmeter Master and Worker Nodes with Autoscaling Creating ConfigMap for JMeter Master - A ConfigMap is used to configure the JMeter master node. File: jmeter_master_configmap.yaml YAML apiVersion: v1 kind: ConfigMap metadata: name: jmeter-load-test labels: app: jmeter data: load_test: | #!/bin/bash /jmeter/apache-jmeter-*/bin/jmeter -n -t $1 -Dserver.rmi.ssl.disable=true -R $(getent ahostsv4 jmeter-slaves-svc | awk '{print $1}' | paste -sd ",") This script: Runs JMeter in non-GUI mode (-n).Disables RMI SSL for inter-pod communication.Dynamically resolves JMeter slave IPs. Deploying JMeter Master Nodes File: jmeter_master_deploy.yaml YAML apiVersion: apps/v1 kind: Deployment metadata: name: jmeter-master labels: app: jmeter-master spec: replicas: 1 selector: matchLabels: app: jmeter-master template: metadata: labels: app: jmeter-master spec: serviceAccountName: <Service Account Name> containers: - name: jmeter-master image: <your-jmeter-master-image> imagePullPolicy: IfNotPresent command: [ "/bin/bash", "-c", "--" ] args: [ "while true; do sleep 30; done;" ] volumeMounts: - name: loadtest mountPath: /jmeter/load_test subPath: "load_test" - name: azure mountPath: /mnt/azure/jmeterresults ports: - containerPort: 60000 volumes: - name: loadtest configMap: name: jmeter-load-test defaultMode: 0777 - name: azure azureFile: secretName: files-secret shareName: jmeterresults readOnly: false This ensures: ConfigMap-based test executionPersistent storage for test resultsThe master node is always available Deploying JMeter Worker Nodes File: jmeter_slaves_deploy.yaml YAML apiVersion: apps/v1 kind: Deployment metadata: name: jmeter-slaves labels: app: jmeter-slave spec: replicas: 2 # Initial count, will be auto-scaled selector: matchLabels: app: jmeter-slave template: metadata: labels: app: jmeter-slave spec: serviceAccountName: <Service Account Name> containers: - name: jmeter-slave image: <your-jmeter-worker-image> imagePullPolicy: IfNotPresent volumeMounts: - name: azure mountPath: /mnt/azure/jmeterresults ports: - containerPort: 1099 - containerPort: 50000 volumes: - name: azure azureFile: secretName: files-secret shareName: jmeterresults readOnly: false Worker pods dynamically join the JMeter master and execute tests. Creating JMeter Worker Service File: jmeter_slaves_svc.yaml YAML apiVersion: v1 kind: Service metadata: name: jmeter-slaves-svc labels: app: jmeter-slave spec: clusterIP: None # Headless service for inter-pod communication ports: - port: 1099 targetPort: 1099 - port: 50000 targetPort: 50000 selector: app: jmeter-slave This enables JMeter master to discover worker nodes dynamically. Enabling Auto-Scaling for JMeter Workers File: jmeter_hpa.yaml YAML apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: jmeter-slaves-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: jmeter-slaves minReplicas: 2 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 Deploying All Components command Run the following command to deploy all components: Shell kubectl apply -f jmeter_master_configmap.yaml kubectl apply -f jmeter_master_deploy.yaml kubectl apply -f jmeter_slaves_deploy.yaml kubectl apply -f jmeter_slaves_svc.yaml kubectl apply -f jmeter_hpa.yaml To verify deployment: Shell kubectl get all -n <namespace-name> kubectl get hpa -n <namespace-name> kubectl get cm -n <namespace-name> Adding More Depth to Monitoring & Observability Performance testing is not just about running the tests—it’s about analyzing the results effectively. Using InfluxDB for Test Data StorageCreating Grafana Dashboards to Visualize TrendsIntegrating Azure Monitor & Log Analytics for Deeper Insights Example: Grafana Metrics for JMeter Performance Metric Description Response Time Measures how fast the system responds Throughput Requests per second handled Error Rate Percentage of failed requests CPU & Memory Usage Tracks AKS node utilization Deploying Grafana for Visualizing Test Results Once InfluxDB is running, configure Grafana to visualize the data. File: dashboard.sh Shell #!/usr/bin/env bash working_dir=`pwd` tenant=`awk '{print $NF}' $working_dir/tenant_export` grafana_pod=`kubectl get po -n $tenant | grep jmeter-grafana | awk '{print $1}'` kubectl exec -ti -n $tenant $grafana_pod -- curl 'http://admin:[email protected]:3000/api/datasources' -X POST -H 'Content-Type: application/json;charset=UTF-8' --data-binary '{"name":"jmeterdb","type":"influxdb","url":"http://jmeter-influxdb:8086","access":"proxy","isDefault":true,"database":"jmeter","user":"admin","password":"admin"}' Run Dashboard Script Shell chmod +x dashboard.sh ./dashboard.sh Automating Cluster Cleanup Once tests are complete, automate cleanup to free up resources. File: jmeter_cluster_delete.sh Shell #!/usr/bin/env bash clustername=$1 tenant=<namespace-name> echo "Deleting ConfigMaps" kubectl delete -n $tenant configmap jmeter-${clustername}-load-test echo "Deleting Jmeter Slaves" kubectl delete deployment.apps/jmeter-${clustername}-slaves kubectl delete service/jmeter-${clustername}-slaves-svc echo "Deleting Jmeter Master" kubectl delete deployment.apps/jmeter-${clustername}-master kubectl get -n $tenant all Run Cleanup Shell chmod +x jmeter_cluster_delete.sh ./jmeter_cluster_delete.sh <clustername> Running JMeter Tests Run a JMeter load test by executing the following in the master pod: Shell kubectl exec -ti jmeter-master -- /jmeter/load_test /mnt/azure/testplans/test.jmx -Gusers=100 -Gramp=10 This runs the test with: 100 concurrent users10-second ramp-up period Monitor Performance in Grafana Open Grafana UI (http://<Grafana-IP>:3000).View real-time results under the JMeter Dashboard. Stopping the JMeter Test To stop an active test: Shell kubectl exec -ti jmeter-master -- /jmeter/apache-jmeter-5.5/bin/stoptest.sh Automating JMeter Load Testing Using CI/CD Pipeline in Azure DevOps Figure 2: The CI/CD pipeline in Azure DevOps for automating JMeter execution, validating scripts, deploying to AKS, and storing results in Azure Blob Storage. Prerequisites for CI/CD in Azure DevOps Before creating the pipelines, ensure: Service Connection for AKS is set up using Azure App Registration / Service Principal with permissions to interact with AKS.Azure DevOps Agent (Self-hosted or Microsoft-hosted) is available to run the pipeline.Variable Groups & Key Vault Integration are configured for secure secrets management. Setting up Service Connection for AKS Create a Service Principal in Azure:az ad sp create-for-rbac --name "aks-service-connection" --role Contributor --scopes /subscriptions/<subscription-id> Go to Azure DevOps → Project Settings → Service Connections.Add a new Kubernetes Service Connection and authenticate using the Service Principal. Verify access using:az aks get-credentials --resource-group <resource-group> --name <aks-cluster> Setting Up CI/CD Pipelines for JMeter in Azure DevOps We will create two pipelines: CI Pipeline (Continuous Integration): Triggers when a commit happens and validates JMeter scripts.CD Pipeline (Continuous Deployment): Deploys JMeter to AKS and executes tests. Implementing the CI Pipeline (Validate JMeter Test Scripts) The CI pipeline will: Validate JMeter test scripts (.jmx)Check syntax and correctness Created File: azure-pipelines-ci.yml YAML trigger: branches: include: - main pool: vmImage: 'ubuntu-latest' steps: - checkout: self - task: UsePythonVersion@0 inputs: versionSpec: '3.x' - script: | echo "Validating JMeter Test Scripts" jmeter -n -t test_plan.jmx -l test_log.jtl displayName: "Validate JMeter Test Plan" Pipeline Execution: Saves logs (test_log.jtl) for debugging.Ensures no syntax errors before running tests in the CD pipeline. Implementing the CD Pipeline (Deploy & Execute JMeter Tests on AKS) The CD pipeline: Pulls the validated JMeter scripts.Deploys JMeter to AKS.Scales up worker nodes dynamically.Executes JMeter tests in distributed mode.Generates test reports and stores them in Azure Storage. Create File: azure-pipelines-cd.yml YAML trigger: - main pool: name: 'Self-hosted-agent' # Or use 'ubuntu-latest' for Microsoft-hosted agents variables: - group: "jmeter-variable-group" # Fetch secrets from Azure DevOps Variable Group stages: - stage: Deploy_JMeter displayName: "Deploy JMeter on AKS" jobs: - job: Deploy steps: - checkout: self - task: AzureCLI@2 displayName: "Login to Azure and Set AKS Context" inputs: azureSubscription: "$(azureServiceConnection)" scriptType: bash scriptLocation: inlineScript inlineScript: | az aks get-credentials --resource-group $(aksResourceGroup) --name $(aksClusterName) kubectl config use-context $(aksClusterName) - script: | echo "Deploying JMeter Master and Worker Nodes" kubectl apply -f jmeter_master_deploy.yaml kubectl apply -f jmeter_slaves_deploy.yaml kubectl apply -f jmeter_influxdb_deploy.yaml displayName: "Deploy JMeter to AKS" - script: | echo "Scaling Worker Nodes for Load Test" kubectl scale deployment jmeter-slaves --replicas=5 displayName: "Scale JMeter Workers" - stage: Execute_Load_Test displayName: "Run JMeter Load Tests" dependsOn: Deploy_JMeter jobs: - job: RunTest steps: - script: | echo "Executing JMeter Test Plan" kubectl exec -ti jmeter-master -- /jmeter/load_test /mnt/azure/testplans/test.jmx -Gusers=100 -Gramp=10 displayName: "Run JMeter Load Test" - script: | echo "Fetching JMeter Test Results" kubectl cp jmeter-master:/mnt/azure/jmeterresults/results test-results displayName: "Copy Test Results" - task: PublishPipelineArtifact@1 inputs: targetPath: "test-results" artifact: "JMeterTestResults" publishLocation: "pipeline" displayName: "Publish JMeter Test Results" Understanding the CD Pipeline Breakdown Step 1: Deploy JMeter on AKS Uses AzureCLI@2 to authenticate and set AKS context.Deploys JMeter Master, Worker nodes, and InfluxDB using YAML files. Step 2: Scale Worker Nodes Dynamically Uses kubectl scale to scale JMeter Worker pods based on test load. Step 3: Execute JMeter Load Test Runs the test using:kubectl exec -ti jmeter-master -- /jmeter/load_test /mnt/azure/testplans/test.jmx -Gusers=100 -Gramp=10 This triggers distributed execution. Step 4: Fetch & Publish Results Copies test results from JMeter Master pod.Publish the results as an artifact in Azure DevOps. Managing Secrets & Variables Securely To prevent exposing credentials: Use Variable Groups to store AKS names, resource groups, and secrets.Azure Key Vault Integration for storing sensitive information. YAML variables: - group: "jmeter-variable-group" Or directly use: YAML - task: AzureKeyVault@1 inputs: azureSubscription: "$(azureServiceConnection)" KeyVaultName: "my-keyvault" SecretsFilter: "*" Security Considerations in CI/CD Pipelines When integrating JMeter tests in Azure DevOps CI/CD Pipelines, security should be a priority. Use Azure Key Vault for Storing Secrets YAML - task: AzureKeyVault@1 inputs: azureSubscription: "$(azureServiceConnection)" KeyVaultName: "my-keyvault" SecretsFilter: "*" Limit AKS Access Using RBAC PoliciesEncrypt Test Data and CredentialsMonitor Pipeline Activities with Azure Security Center Automating Test Cleanup After Execution To free up AKS resources, the pipeline should scale down workers' post-test. Modify azure-pipelines-cd.yml YAML - script: | echo "Scaling Down JMeter Workers" kubectl scale deployment jmeter-slaves --replicas=1 displayName: "Scale Down Workers After Test" Best Practices for JMeter on AKS and CI/CD in Azure DevOps 1. Optimizing Performance and Scaling Optimize Auto-Scaling – Use HPA (Horizontal Pod Autoscaler) to dynamically adjust JMeter worker nodes.Optimize Worker Pods – Assign proper CPU and memory limits to avoid resource exhaustion.Store Results in Azure Storage – Prevent overload by saving JMeter logs in Azure Blob Storage.Automate Cleanup – Scale down JMeter workers post-test to save costs. Figure 3: Auto-Scaling of JMeter Worker Nodes using Horizontal Pod Autoscaler (HPA) in Azure Kubernetes Service (AKS), dynamically adjusting pod count based on CPU usage. 2. Monitoring and Observability Monitor Performance – Use InfluxDB + Grafana for real-time analysis.Use Azure Monitor & Log Analytics – Track AKS cluster health and performance.Integrate Grafana & Prometheus – (Optional) Provides visualization for live metrics.Automate Grafana Setup – Ensure seamless test monitoring and reporting.JMeter Logs & Metrics Collection – View live test logs using: kubectl logs -f jmeter-master 3. Best Practices for CI/CD Automation Use Self-hosted Agents – Provides better control over pipeline execution.Leverage HPA for CI/CD Workloads – Automatically adjust pod count during load test execution.Automate Deployment – Use Helm charts or Terraform for consistent infrastructure setup.Use CI/CD Pipelines – Automate test execution in Azure DevOps Pipelines.Optimize Cluster Cleanup – Prevent unnecessary costs by cleaning up resources after execution. 4. Automating Failure Handling & Alerts Set Up Alerting for Test Failures – Automatically detect failures in JMeter tests and trigger alerts.Send Notifications to Slack, Teams, or Email when a test fails. Example: Automated Failure Alerting YAML - script: | if grep -q "Assertion failed" test_log.jtl; then echo "Test failed! Sending alert..." curl -X POST -H "Content-Type: application/json" -d '{"text": "JMeter Test Failed! Check logs."}' <Slack_Webhook_URL> fi displayName: "Monitor & Alert for Failures" Figure 4: Automated failure detection and alerting mechanism for JMeter tests in Azure DevOps, utilizing Azure Monitor & Log Analytics for failure handling. 5. Steps for Automating JMeter Test Reporting & Email Notifications for JMeter Results Once the CI/CD pipeline generates the JTL file, we can convert it into an HTML report. Generate an HTML report from JTL: jmeter -g results.jtl -o report/ This will create a detailed performance report inside the report/ directory. Convert JTL to CSV (Optional): awk -F, '{print $1, $2, $3, $4}' results.jtl > results.csv This extracts key columns from results.jtl and saves them in results.csv. Extracting Key Metrics from JTL To summarize test results and send an email, extract key metrics like response time, error rate, and throughput. Python script to parse results.jtl and summarize key stats: Python import pandas as pd def summarize_jtl_results(jtl_file): df = pd.read_csv(jtl_file) total_requests = len(df) avg_response_time = df["elapsed"].mean() error_count = df[df["success"] == False].shape[0] error_rate = (error_count / total_requests) * 100 summary = f""" **JMeter Test Summary** --------------------------------- Total Requests: {total_requests} Avg Response Time: {avg_response_time:.2f} ms Error Count: {error_count} Error Rate: {error_rate:.2f} % --------------------------------- """ return summary # Example usage: report = summarize_jtl_results("results.jtl") print(report) Sending JMeter Reports via Email Once the report is generated, automate sending an email with the results. Python script to send JMeter reports via email: Python import smtplib import os from email.message import EmailMessage def send_email(report_file, recipient): msg = EmailMessage() msg["Subject"] = "JMeter Test Report" msg["From"] = "[email protected]" msg["To"] = recipient msg.set_content("Hi,\n\nPlease find attached the JMeter test report.\n\nBest,\nPerformance Team") with open(report_file, "rb") as file: msg.add_attachment(file.read(), maintype="application", subtype="octet-stream", filename=os.path.basename(report_file)) with smtplib.SMTP("smtp.example.com", 587) as server: server.starttls() server.login("[email protected]", "your-password") server.send_message(msg) # Example usage: send_email("report/index.html", "[email protected]") Automating the Process in CI/CD Pipeline Modify the azure-pipelines-cd.yml to Include Reporting & Emailing YAML - script: | echo "Generating JMeter Report" jmeter -g results.jtl -o report/ displayName: "Generate JMeter HTML Report" - script: | echo "Sending JMeter Report via Email" python send_email.py report/index.html [email protected] displayName: "Email JMeter Report" This ensures: The JMeter test report is generated post-execution.The report is automatically emailed to stakeholders. Conclusion By leveraging JMeter on Kubernetes and CI/CD automation with Azure DevOps (or other CI/CD tools like Jenkins, GitHub Actions, etc.), you can ensure your applications are scalable, resilient, and cost-effective. This guide covers the deployment and execution of JMeter on AKS, enabling distributed load testing at scale. By leveraging Kubernetes auto-scaling capabilities, this setup ensures efficient resource utilization and supports continuous performance testing with automated reporting and alerting. This Kubernetes-native JMeter setup allows for scalable, cost-effective, and automated performance testing on Azure Kubernetes Service (AKS) but can also be deployed on AWS EKS, Google GKE, or any other Kubernetes environment. It integrates JMeter, Kubernetes, InfluxDB, and Grafana for scalable, automated, and observable performance testing, with automated email notifications and report generation. Benefits of Automating JMeter Load Testing with CI/CD Pipelines End-to-end automation – From test execution to result storage and reporting.Scalability – JMeter runs are distributed across AKS worker nodes (or any Kubernetes cluster).Observability – Monitored via InfluxDB & Grafana with real-time insights.Automated Reporting – JTL test results are converted into HTML reports and sent via email notifications. "With modern applications handling massive traffic, performance testing is no longer optional—it's a necessity. By leveraging JMeter on Kubernetes and CI/CD automation with Azure DevOps (or any CI/CD tool), you can ensure your applications are scalable, resilient, and cost-effective." Key Takeaways: Automate Load Testing with Azure DevOps Pipelines (or Jenkins, GitHub Actions, etc.).Scale JMeter dynamically using Kubernetes & HPA across multi-cloud environments.Monitor & Analyze results with InfluxDB + Grafana in real time.Optimize Costs by using auto-scaling and scheduled tests.Enable Automated Reporting by sending test results via email notifications. Next Step: Expanding Reporting & Alerting Mechanisms in CI/CD Pipelines, including AI-based anomaly detection for performance testing and predictive failure analysis. Stay tuned for advanced insights! Take Action Today! Implement this setup in your environment—whether in Azure AKS, AWS EKS, or Google GKE—and share your feedback! References Apache JMeter - Apache JMeterTM. (n.d.). https://jmeter.apache.org/Apache JMeter - User’s Manual: Best Practices. (n.d.). https://jmeter.apache.org/usermanual/best-practices.htmlKubernetes documentation. (n.d.). Kubernetes. https://kubernetes.io/docs/Nickomang. (n.d.). Azure Kubernetes Service (AKS) documentation. Microsoft Learn. https://learn.microsoft.com/en-us/azure/aks/Chcomley. (n.d.). Azure DevOps documentation. Microsoft Learn. https://learn.microsoft.com/en-us/azure/devops/?view=azure-devopsInfluxData. (2021, December 10). InfluxDB: Open Source Time Series Database | InfluxData. https://www.influxdata.com/products/influxdb/Grafana OSS and Enterprise | Grafana documentation. (n.d.). Grafana Labs. https://grafana.com/docs/grafana/latest/Apache JMeter - User’s Manual: Generating Dashboard Report. (n.d.). https://jmeter.apache.org/usermanual/generating-dashboard.html

In Terraform, you will often need to convert a list to a string when passing values to configurations that require a string format, such as resource names, cloud instance metadata, or labels. Terraform uses HCL (HashiCorp Configuration Language), so handling lists requires functions like join() or format(), depending on the context. How to Convert a List to a String in Terraform The join() function is the most effective way to convert a list into a string in Terraform. This concatenates list elements using a specified delimiter, making it especially useful when formatting data for use in resource names, cloud tags, or dynamically generated scripts. The join(", ", var.list_variable) function, where list_variable is the name of your list variable, merges the list elements with ", " as the separator. Here’s a simple example: Shell variable "tags" { default = ["dev", "staging", "prod"] } output "tag_list" { value = join(", ", var.tags) } The output would be: Shell "dev, staging, prod" Example 1: Formatting a Command-Line Alias for Multiple Commands In DevOps and development workflows, it’s common to run multiple commands sequentially, such as updating repositories, installing dependencies, and deploying infrastructure. Using Terraform, you can dynamically generate a shell alias that combines these commands into a single, easy-to-use shortcut. Shell variable "commands" { default = ["git pull", "npm install", "terraform apply -auto-approve"] } output "alias_command" { value = "alias deploy='${join(" && ", var.commands)}'" } Output: Shell "alias deploy='git pull && npm install && terraform apply -auto-approve'" Example 2: Creating an AWS Security Group Description Imagine you need to generate a security group rule description listing allowed ports dynamically: Shell variable "allowed_ports" { default = [22, 80, 443] } resource "aws_security_group" "example" { name = "example_sg" description = "Allowed ports: ${join(", ", [for p in var.allowed_ports : tostring(p)])}" dynamic "ingress" { for_each = var.allowed_ports content { from_port = ingress.value to_port = ingress.value protocol = "tcp" cidr_blocks = ["0.0.0.0/0"] } } } The join() function, combined with a list comprehension, generates a dynamic description like "Allowed ports: 22, 80, 443". This ensures the security group documentation remains in sync with the actual rules. Alternative Methods For most use cases, the join() function is the best choice for converting a list into a string in Terraform, but the format() and jsonencode() functions can also be useful in specific scenarios. 1. Using format() for Custom Formatting The format() function helps control the output structure while joining list items. It does not directly convert lists to strings, but it can be used in combination with join() to achieve custom formatting. Shell variable "ports" { default = [22, 80, 443] } output "formatted_ports" { value = format("Allowed ports: %s", join(" | ", var.ports)) } Output: Shell "Allowed ports: 22 | 80 | 443" 2. Using jsonencode() for JSON Output When passing structured data to APIs or Terraform modules, you can use the jsonencode() function, which converts a list into a JSON-formatted string. Shell variable "tags" { default = ["dev", "staging", "prod"] } output "json_encoded" { value = jsonencode(var.tags) } Output: Shell "["dev", "staging", "prod"]" Unlike join(), this format retains the structured array representation, which is useful for JSON-based configurations. Creating a Literal String Representation in Terraform Sometimes you need to convert a list into a literal string representation, meaning the output should preserve the exact structure as a string (e.g., including brackets, quotes, and commas like a JSON array). This is useful when passing data to APIs, logging structured information, or generating configuration files. For most cases, jsonencode() is the best option due to its structured formatting and reliability in API-related use cases. However, if you need a simple comma-separated string without additional formatting, join() is the better choice. Common Scenarios for List-to-String Conversion in Terraform Converting a list to a string in Terraform is useful in multiple scenarios where Terraform requires string values instead of lists. Here are some common use cases: Naming resources dynamically: When creating resources with names that incorporate multiple dynamic elements, such as environment, application name, and region, these components are often stored as a list for modularity. Converting them into a single string allows for consistent and descriptive naming conventions that comply with provider or organizational naming standards.Tagging infrastructure with meaningful identifiers: Tags are often key-value pairs where the value needs to be a string. If you’re tagging resources based on a list of attributes (like team names, cost centers, or project phases), converting the list into a single delimited string ensures compatibility with tagging schemas and improves downstream usability in cost analysis or inventory tools.Improving documentation via descriptions in security rules: Security groups, firewall rules, and IAM policies sometimes allow for free-form text descriptions. Providing a readable summary of a rule’s purpose, derived from a list of source services or intended users, can help operators quickly understand the intent behind the configuration without digging into implementation details.Passing variables to scripts (e.g., user_data in EC2 instances): When injecting dynamic values into startup scripts or configuration files (such as a shell script passed via user_data), you often need to convert structured data like lists into strings. This ensures the script interprets the input correctly, particularly when using loops or configuration variables derived from Terraform resources.Logging and monitoring, ensuring human-readable outputs: Terraform output values are often used for diagnostics or integration with logging/monitoring systems. Presenting a list as a human-readable string improves clarity in logs or dashboards, making it easier to audit deployments and troubleshoot issues by conveying aggregated information in a concise format. Key Points Converting lists to strings in Terraform is crucial for dynamically naming resources, structuring security group descriptions, formatting user data scripts, and generating readable logs. Using join() for readable concatenation, format() for creating formatted strings, and jsonencode() for structured output ensures clarity and consistency in Terraform configurations.

As software development continues to evolve, companies are reimagining how teams collaborate to build and ship applications. The emergence of cloud development environments (CDEs) has been a major catalyst in this change, offering self‐service platforms that make it easier for developers to spin up resources on demand. Coupled with platform engineering and internal developer portals, these self‐service solutions are fundamentally altering the software development culture by reducing cognitive load and boosting productivity. The Shift Toward Self‐Service Traditionally, developers had to navigate complex layers of approvals and processes to get the infrastructure they needed. Each new environment or tool often meant waiting for separate teams, such as ops or security, to provision resources. This created bottlenecks, slowed innovation, and increased context-switching. Self‐service platforms turn this model on its head. By consolidating essential development and deployment workflows into a single interface or a set of automated processes, these platforms give developers immediate access to environments, services, and tooling. The result is a more agile workflow, where waiting times are dramatically reduced. Teams can prototype, experiment, and test ideas more quickly because they no longer depend on external gatekeepers to configure each piece of the puzzle. The Role of Platform Engineering Platform engineering is a discipline focused on delivering a frictionless developer experience through curated technology stacks, standard tooling, and common infrastructure services. A platform engineering team acts as an internal service provider, continuously refining and maintaining the platform in response to developer needs. Standardized Infrastructure By abstracting away the gritty details of provisioning, configuration, and deployments, platform engineers enable developers to focus on writing code and delivering features. This standardization ensures consistency, security, and compliance across diverse projects and teams. Automated Toolchains Automated pipelines for building, testing, and deploying code speed up the feedback loop. With continuous integration and continuous delivery (CI/CD) systems integrated into the platform, developers gain a seamless path from commit to production, reducing manual tasks and potential errors. Empowered Decision-Making A self‐service platform often includes catalogs of pre-approved services (databases, message queues, etc.) that developers can spin up. This fosters autonomy while ensuring security policies and best practices are enforced behind the scenes. Internal Developer Portals: A Single Pane of Glass To further reduce cognitive load, companies are implementing internal developer portals — often a web-based interface that aggregates information, tools, and metrics related to the entire development lifecycle. These portals serve as a single pane of glass, making it easier for developers to: Discover available APIs, microservices, and libraries.Provision environments and test data.Track observability metrics and logs for troubleshooting.Access documentation, design guidelines, and best practices. By centralizing these components, internal developer portals streamline how teams interact with the broader technology ecosystem. Instead of juggling multiple logins, consoles, or spreadsheets, developers can navigate through a consistent interface that encapsulates everything they need to get work done. Reducing Cognitive Load One of the most cited benefits of self‐service platforms is the reduction of cognitive load on developers. When developers don’t have to worry about how infrastructure is spun up or how to configure their CI/CD pipelines, they can focus on writing clean, maintainable code. This reduction in mental overhead translates to better software quality, faster time to market, and less burnout. Key elements that ease cognitive load include: Automation: Removing manual steps from testing, deployment, and monitoring.Documentation: Providing clear, concise, and centralized instructions for common tasks.Standardization: Establishing default configurations and recommended toolsets so developers spend less time making boilerplate decisions. Impact on Dev Culture The real magic of self‐service platforms lies in their cultural implications. By empowering developers, these platforms also drive collaboration and cross-team alignment. Here’s how: Greater Autonomy, Better Morale: Developers who have the freedom to create and iterate without red tape tend to be more motivated and engaged. This autonomy fosters creativity and a sense of ownership.Inter-Team Collaboration: Self‐service platforms don’t just help developers; they also help platform and ops teams. The traditional tension between “dev” and “ops” teams softens when developers can take care of routine tasks themselves and ops can concentrate on platform improvements and strategic initiatives.Faster Feedback Loop: Shorter lead times and rapid prototyping are keys to innovation. When developers can deploy features and get feedback in hours rather than days or weeks, organizations can adapt to market needs more effectively.Continuous Improvement: Platform engineering is an ongoing effort. As technology shifts, the platform evolves, adopting new frameworks or cloud services. This continuous improvement mindset trickles down to developers, encouraging a culture of experimentation and learning. Challenges and Best Practices While self‐service platforms offer enormous benefits, adopting them is not without challenges: Security and Compliance: Automating environment provisioning can introduce security risks if not handled properly. Platform engineers must enforce policies, role-based access, and guardrails.Complexity Under the Hood: Abstraction is great for ease of use, but someone still has to manage the underlying complexity. Make sure the platform team has a clear roadmap and well-defined processes for maintenance and scaling.Organizational Alignment: Implementing a self‐service culture requires buy-in from leadership, particularly around budget, headcount for platform teams, and training initiatives.Clear Documentation: Without accessible docs, even the best platform can become a maze. Documentation should be treated as a first-class asset. Conclusion Self‐service platforms and cloud development environments are enabling a new wave of developer empowerment. By reducing cognitive load, fostering autonomy, and streamlining workflows, these platforms reshape dev culture toward greater innovation and collaboration. As technology continues to evolve, organizations that invest in robust platform engineering and intuitive internal developer portals will be best positioned to attract top talent, deliver value more rapidly, and stay ahead of the competition.

Protecting database access through strong password policies is a cornerstone of security in any environment. When deploying Oracle databases on AWS RDS, enforcing password complexity is essential, but the approach differs slightly from on-premises Oracle environments. AWS provides two primary ways to enforce password complexity in RDS Oracle: using the standard ORA_STIG_VERIFY_FUNCTION or a custom user-defined verification function. This article provides a detailed, step-by-step guide for implementing both methods to help secure Oracle database passwords in AWS RDS. Why Password Verification Matters Password verification functions ensure that users adhere to organization-defined security policies for password creation. These functions typically enforce: Minimum password lengthInclusion of uppercase/lowercase charactersUse of numbers and special charactersPrevention of dictionary words or username-based passwords On AWS RDS for Oracle, password verification must be registered using the rdsadmin package, unlike on-prem Oracle where direct creation is allowed. Option 1: Use the AWS-Provided Standard Verification Function AWS RDS for Oracle includes a built-in password verification function named ORA_STIG_VERIFY_FUNCTION, which aligns with the U.S. Department of Defense STIG standards. Steps Create a Profile Using the Built-in Function PLSQL CREATE PROFILE stig_profile LIMIT PASSWORD_LIFE_TIME 60 PASSWORD_REUSE_TIME 365 PASSWORD_REUSE_MAX 10 FAILED_LOGIN_ATTEMPTS 5 PASSWORD_VERIFY_FUNCTION ORA_STIG_VERIFY_FUNCTION; Assign the Profile to a User PLSQL ALTER USER db_user PROFILE stig_profile; Test Password Complexity Try altering the password with a weak password to verify that complexity enforcement works: SQL ALTER USER db_user IDENTIFIED BY "simple"; -- This should fail due to policy violation. Option 2: Create a Custom Password Verification Function If your organization requires custom password rules, you can define a function and register it via the AWS rdsadmin package. A Step-by-Step Guide Define the Function Using the rdsadmin Utility Use the rdsadmin.rdsadmin_password_verify.create_verify_function procedure to register a custom function. The following example creates a function named CUSTOM_PASSWORD_FUNCTION. It enforces the following rules: The password must be at least 12 characters long.It ust contain at least 2 uppercase characters.It must include at least 1 digit and 1 special character.It must not contain the @ character. PLSQL BEGIN rdsadmin.rdsadmin_password_verify.create_verify_function(p_verify_function_name => 'CUSTOM_PASSWORD_FUNCTION', p_min_length => 12, p_min_uppercase => 2, p_min_digits => 1, p_min_special => 1, p_disallow_at_sign => true); END; / More parameter details: Parameter nameData typeDefaultRequiredDescription p_verify_function_name varchar2 — Yes The name for your custom function. This function is created for you in the SYS schema. You assign this function to user profiles. p_min_length number 8 No The minimum number of characters required. p_max_length number 256 No The maximum number of characters allowed. p_min_letters number 1 No The minimum number of letters required. p_min_uppercase number 0 No The minimum number of uppercase letters required. p_min_lowercase number 0 No The minimum number of lowercase letters required. p_min_digits number 1 No The minimum number of digits required. p_min_special number 0 No The minimum number of special characters required. p_min_different_chars number 3 No The minimum number of different characters required between the old and new passwords. p_disallow_username boolean true No Set to true to disallow the user name in the password. p_disallow_reverse boolean true No Set to true to disallow the reverse of the user name in the password. p_disallow_db_name boolean true No Set to true to disallow the database or server name in the password. p_disallow_simple_strings boolean true No Set to true to disallow simple strings as the password. p_disallow_whitespace boolean false No Set to true to disallow white space characters in the password. p_disallow_at_sign boolean false No Set to true to disallow the @ character in the password. To see the text of your verification function, run the following: PLSQL COL TEXT FORMAT a150 SELECT TEXT FROM DBA_SOURCE WHERE OWNER = 'SYS' AND NAME = 'CUSTOM_PASSWORD_FUNCTION' ORDER BY LINE; Step 2: Associate the Function With a User Profile Assign your custom function to the DEFAULT or another user-defined profile: PLSQL ALTER PROFILE DEFAULT LIMIT PASSWORD_VERIFY_FUNCTION CUSTOM_PASSWORD_FUNCTION; To view which user profiles are linked to the custom function: SQL SELECT * FROM DBA_PROFILES WHERE RESOURCE_NAME = 'PASSWORD' AND LIMIT = 'CUSTOM_PASSWORD_FUNCTION'; To list all profiles and the password verification functions they are associated with: PLSQL SELECT * FROM DBA_PROFILES WHERE RESOURCE_NAME = 'PASSWORD_VERIFY_FUNCTION'; Sample output: SQL PROFILE RESOURCE_NAME RESOURCE LIMIT --------- ------------------------- -------- -------------------------- DEFAULT PASSWORD_VERIFY_FUNCTION PASSWORD CUSTOM_PASSWORD_FUNCTION RDSADMIN PASSWORD_VERIFY_FUNCTION PASSWORD NULL Step 3: Assign the Profile to a User PLSQL ALTER USER example_user PROFILE DEFAULT; This step ensures that the user is now governed by the rules defined in the custom password verification function. Best Practices for Password Management in AWS RDS Oracle Avoid default users: Do not use admin, system, or sys for application access.Use IAM and Secrets Manager: Integrate AWS Secrets Manager for secure password storage and rotation.Audit logs: Enable CloudTrail and CloudWatch for tracking login attempts and failed access.Enforce expiry and lock policies: Use parameters like PASSWORD_LIFE_TIME and FAILED_LOGIN_ATTEMPTS.Rotate passwords automatically: Leverage automation tools or AWS Lambda for periodic password changes. Conclusion Securing database access in the cloud requires thoughtful implementation of password management policies. With AWS RDS for Oracle, you have the flexibility to use either AWS-provided STIG-compliant password checks or create tailored password validation functions. Remember, while the concepts may mirror on-premises Oracle, the implementation differs in AWS and requires using rdsadmin utilities. By following these practices, you ensure a more secure and compliant cloud database environment.

By arvind toorpu CORE

The cloud may be fast…but it nearly slowed us down. When we launched Hathora in 2022, we knew the infrastructure behind multiplayer games was long overdue for reinvention. Studios like EA and Blizzard had built their own complex systems to host game servers, but for most multiplayer game studios, that approach was out of reach. Our goal was to eliminate the barrier with a platform-as-a-service built specifically for multiplayer game workloads (low-latency, stateful servers ready to handle millions of connections without the overhead of managing infrastructure). We launched on AWS, using EKS for Kubernetes orchestration. Adoption came fast. In our first six months, more than a hundred studios signed on. That early success validated our bet on developer demand, but it also revealed a hidden cost structure that threatened the model entirely. For larger studios in particular, we saw that the cloud’s egress charges were spiraling far beyond compute. Because multiplayer games rely on high-frequency state updates (we’re talking hundreds of updates per second, per connected player), the bandwidth cost of constantly pushing those updates to clients became astronomical. In some cases, egress costs were more than four times higher than compute. We also realized that much of what public cloud platforms offer wasn’t particularly relevant to our use case. Our workloads were ephemeral, memory-intensive, and didn’t need databases, message queues, or serverless components. Game sessions spin up and tear down constantly, and each instance behaves identically. What we did need was fast, efficient compute close to users, but without all the extras that cloud platforms bake into their pricing. On top of that, the performance characteristics of EKS weren’t where we wanted them to be. For latency-sensitive gaming workloads, we were hitting limits we couldn’t tune past. Between the cost and the performance tradeoffs, it became clear that our infrastructure model had to evolve. Why We Stepped off the Cloud-Only Path To keep serving independent developers and win the trust of larger studios, we had to rethink our platform’s foundations. We needed to bring infrastructure costs down dramatically without compromising scale, availability, or player experience. And we needed to offer consistent performance globally, even during unpredictable traffic spikes tied to new releases or live events. That all led us to an approach many would consider unconventional in 2025: bare metal. By defaulting to dedicated hardware for our base workloads and reserving cloud capacity only for on-demand bursts, we could finally break the cost-performance tradeoff. We started building out this hybrid model, using bare metal servers for baseline compute and reserving cloud capacity for elasticity. But that decision introduced its own complexity. Managing orchestration across bare metal and multiple cloud providers wasn’t something our existing setup could support. The Orchestration Layer Had to Evolve, Too EKS had helped us get off the ground, but it wasn’t built for orchestrating containers across a mix of hardware and cloud providers. We needed something more minimal, portable, and vendor-agnostic. Something purpose-built for distributed environments, not just AWS. That search brought us to Talos Linux, an open source operating system designed specifically for Kubernetes. Talos had a stripped-down, API-driven model with no SSH layer, making it simple to manage while improving our security. It ran just as well on bare metal as it did in virtualized cloud environments, and it was already powering large-scale production clusters. After a successful proof of concept, we took it a step further and adopted Omni, Sidero Labs’ Kubernetes management platform. Our strategy with Omni gave us unified control across every node, no matter where it lived (bare metal, AWS, or GCP). With it, our small team could operate a distributed, multi-cloud fleet with the confidence of a much larger organization. As we expanded into new regions and brought on new providers, Talos and Omni helped us scale without fracturing our infrastructure model. We’re now operating infrastructure that manages 30,000+ cores across 14 global regions, spanning two bare metal vendors and multiple public cloud platforms. Everything is orchestrated as one cohesive system. The Hybrid Model That Transformed Our Platform With bare metal deployed via Omni now handling 80% of our compute and cloud filling in the gaps during spikes, we’ve achieved a level of efficiency and flexibility that simply wasn’t possible with a cloud-only architecture. For game studios, this has translated into significantly lower costs (especially for those with large or persistent game worlds) and consistently low latency regardless of global traffic patterns. Talos Linux (connected to the bare metal Omni) gives us the freedom to onboard new edge nodes quickly, no matter the provider. That flexibility makes it easy to expand into new regions or test out new hardware options without locking into a single vendor. We can move fast, stay lean, and keep our infrastructure tuned precisely for the unique demands of multiplayer gaming. What started as a shift to improve economics has turned into a long-term advantage. The hybrid model didn’t just save us money, but also control, performance, and the confidence to grow globally without compromise. From Infrastructure Burden to Competitive Advantage From the beginning, we set out to make it easier for game studios to build multiplayer games without worrying about infrastructure. That vision hasn’t changed. What has changed is how we deliver on it. By really leaning into bare metal, refining our orchestration strategy, and using the cloud where it actually makes sense, we’ve built an infrastructure that matches the real-world needs of our customers. Studios can scale instantly, launch in new regions overnight, and trust that player experience won’t suffer when their games take off. We don’t believe infrastructure should be a bottleneck, or a budget killer, for studios focused on creativity and gameplay. Thanks to this hybrid foundation with bare metal and no lock-in, it no longer has to be.

Imagine you're building a skyscraper—not just quickly, but with precision. You rely on blueprints to make sure every beam and every bolt is exactly where it should be. That’s what Infrastructure as Code (IaC) is for today’s cloud-native organizations—a blueprint for the cloud. As businesses race to innovate faster, IaC helps them automate and standardize how cloud resources are built. But here’s the catch: speed without security is like skipping the safety checks on that skyscraper. One misconfigured setting, an exposed secret, or a non-compliant resource can bring the whole thing down—or at least cause serious trouble in production. That’s why the shift-left approach to secure IaC matters more than ever. What Does “Shift-Left” Mean in IaC? Shifting left refers to moving security and compliance checks earlier in the development process. Rather than waiting until deployment or runtime to detect issues, teams validate security policies, compliance rules, and access controls as code is written—enabling faster feedback, reduced rework, and stronger cloud governance. For IaC, this means, Scanning Terraform templates and other configuration files for vulnerabilities and misconfigurations before they are deployed.Validating against cloud-specific best practices.Integrating policy-as-code and security tools into CI/CD pipelines. Why Secure IaC Matters? IaC has completely changed the game when it comes to managing cloud environments. It’s like having a fast-forward button for provisioning—making it quicker, more consistent, and easier to repeat across teams and projects. But while IaC helps solve a lot of the troubles around manual operations, it’s not without its own set of risks. The truth is, one small mistake—just a single misconfigured line in a Terraform script—can have massive consequences. It could unintentionally expose sensitive data, leave the door open for unauthorized access, or cause your setup to drift away from compliance standards. And because everything’s automated, those risks scale just as fast as your infrastructure. In cloud environments like IBM Cloud, where IaC tools like Terraform and Schematics automate the creation of virtual servers, networks, storage, and IAM policies, a security oversight can result in- Publicly exposed resources (e.g., Cloud Object Storage buckets or VPC subnets).Over-permissive IAM roles granting broader access than intended.Missing encryption for data at rest or in transit.Hard-coded secrets and keys within configuration files.Non-compliance with regulatory standards like GDPR, HIPAA, or ISO 27001. These risks can lead to data breaches, service disruptions, and audit failures—especially if they go unnoticed until after deployment. Secure IaC ensures that security and compliance are not afterthoughts but are baked into the development process. It enables: Early detection of mis-configurations and policy violations.Automated remediation before deployment.Audit-ready infrastructure, with traceable and versioned security policies.Shift-left security, empowering developers to code safely without slowing down innovation. When done right, Secure IaC acts as a first line of defense, helping teams deploy confidently while reducing the cost and impact of security fixes later in the lifecycle. Components of Secure IaC Framework The Secure IaC Framework is structured into layered components that guide organizations in embedding security throughout the IaC lifecycle. Building Blocks of IaC (Core foundation for all other layers)—These are the fundamental practices required to enable any Infrastructure as Code approach. Use declarative configuration (e.g. Terraform, YAML, JSON).Embrace version control (e.g. Git) for all infrastructure code.Define idempotent and modular code for reusable infrastructure.Enable automation pipelines (CI/CD) for repeatable deployments.Follow consistent naming conventions, tagging policies, and code linting.Build Secure Infrastructure- Focuses on embedding secure design and architectural patterns into the infrastructure baseline. Use secure-by-default modules (e.g. encryption, private subnets).Establish network segmentation, IAM boundaries, and resource isolation.Configure monitoring, logging, and default denial policies.Choose secure providers and verified module sources.Automate Controls - Empowers shift-left security by embedding controls into the development and delivery pipelines. Run static code analysis (e.g. Trivy, Checkov) pre-commit and in CI.Enforce policy-as-code using OPA or Sentinel for approvals and denials.Integrate configuration management and IaC test frameworks (e.g. Terratest).Detect & Respond - Supports runtime security through visibility, alerting, and remediation.Enable drift detection tools to track deviations from IaC definitions.Use runtime compliance monitoring.Integrate with SOAR platforms or incident playbooks.Generate security alerts for real-time remediation and Root Cause Analysis (RCA).Detect & Respond - Supports runtime security through visibility, alerting, and remediation. Enable drift detection tools to track deviations from IaC definitions.Use runtime compliance monitoring (e.g., IBM Cloud SCC).Integrate with SOAR platforms or incident playbooks.Generate security alerts for real-time remediation and RCA.Design Governance—Establishes repeatable, scalable security practices across the enterprise. Promote immutable infrastructure for consistent and tamper-proof environments.Use golden modules or signed templates with organizational guardrails.Implement change management via GitOps, PR workflows, and approval gates.Align with compliance standards (e.g., CIS, NIST, ISO 27001) and produce audit reports. Anatomy of Secure IaC Creating a secure IaC environment involves incorporating several best practices and tools to ensure that the infrastructure is resilient, compliant, and protected against potential threats. These practices are implemented and tracked at various phases of IaC environment lifecycle. Design phase of IaC involves not just identifying the IaC script design and tools decision but also includes the design of incorporating organizational policies into the IaC scripts.Development phase of IaC involves the coding best practices, implementing IaC scripts and policies involved, and also the pre-commit checks that the developer can run before committing. These checks help a clean code check-in and detect the code smells upfront.Build phase of IaC involves all the code security checks and policy verification. This is a quality gate in the pipeline that stops the deployment on any failures.Deployment phase of IaC supports deployment to various environments along with their respective configurations.Maintenance phase of IaC is also a crucial phase, as threat detection, vulnerability detection, and monitoring play a key role. Key Pillars of Secure IaC Below is a list of key pillars of Secure IaC, incorporating all the essential tools and services. These pillars align with cloud-native capabilities to enforce a secure-by-design, shift-left approach for Infrastructure as Code: Reference templates like Deployable Architectures or AWS Terraform Modules. Reusable, templatized infrastructure blueprints designed for security, compliance, and scalability.Promotes consistency across environments (dev/test/prod).Often include pre-approved Terraform templates.Managed IaC platformsllike IBM Cloud Schematics or AWS CloudFormation Enables secure execution of Terraform code in isolated workspaces.Supports: Role-Based Access Control (RBAC)Encrypted variablesApproval workflows (via GitOps or manual)Versioned infrastructure plansLifecycle resource management using IBM Cloud Projects or Azure Blueprints Logical grouping of cloud resources tied to governance and compliance requirements.Simplifies multi-environment deployments (e.g. dev, QA, prod).Integrates with IaC deployment and CI/CD for isolated, secure automation pipelines.Secrets Management Centralized secrets vault to manage: API keysCertificatesIAM credentialsProvides dynamic secrets, automatic rotation, access logging, and fine-grained access policies.Key Management Solutions (KMS/HSM) Protect sensitive data at rest or in transit Manages encryption keys with full customer control and auditability.KMS-backed encryption is critical for storage, databases, and secrets.Compliance Posture Management Provides posture management and continuous compliance monitoring.Enables: Policy-as-Code checks on IaC deploymentsCustom rules enforcementCompliance posture dashboards (CIS, NIST, GDPR)Introduce Continuous Compliance (CC) pipelines as part of the CI/CD pipelines for shift-left enforcement.CI/CD Pipelines (DevSecOps) Integrate security scans and controls into delivery pipelines using GitHub Actions, Tekton, Jenkins, or IBM Cloud Continuous DeliveryPipeline stages include: Terraform lintingStatic analysis (Checkov, tfsec)Secrets scanningCompliance policy validationChange approval gates before Schematics applyPolicy-as-Code Use tools like OPA (Open Policy Agent) policies to: Block insecure resource configurationsRequire tagging, encryption, and access policiesAutomate compliance enforcement during plan and applyIAM & Resource Access Governance Apply least privilege IAM roles for projects, and API keys.Use resource groups to scope access boundaries.Enforce fine-grained access to Secrets Manager, KMS, and Logs.Audit and Logging Integrate with Cloud Logs to: Monitor infrastructure changesAudit access to secrets, projects, and deploymentsDetect anomalies in provisioning behaviorMonitoring and Drift Detection Use monitoring tools like IBM Instana, Drift Detection, or custom Terraform state validation to: Continuously monitor deployed infrastructureCompare live state to defined IaCRemediate unauthorized changes Checklist: Secure IaC 1. Code Validation and Static Analysis Integrate static analysis tools (e.g., Checkov, TFSec) into your development workflow. Scan Terraform templates for misconfigurations and security vulnerabilities. Ensure compliance with best practices and CIS benchmarks. 2. Policy-as-Code Enforcement Define security policies using Open Policy Agent (OPA) or other equivalent tools. Enforce policies during the CI/CD pipeline to prevent non-compliant deployments. Regularly update and audit policies to adapt to evolving security requirements. 3. Secrets and Credential Management Store sensitive information in Secrets Manager. Avoid hardcoding secrets in IaC templates. Implement automated secret rotation and access controls. 4. Immutable Infrastructure and Version Control Maintain all IaC templates in a version-controlled repository (e.g., Git). Implement pull request workflows with mandatory code reviews. Tag and document releases for traceability and rollback capabilities. 5. CI/CD Integration with Security Gates Incorporate security scans and compliance checks into the CI/CD pipeline. Set up approval gates to halt deployments on policy violations. Automate testing and validation of IaC changes before deployment. 6. Secure Execution Environment Utilize IBM Cloud Schematics or AWS Cloud Formation or any equivalent tool for executing Terraform templates in isolated environments. Restrict access to execution environments using IAM roles and policies. Monitor and log all execution activities for auditing purposes. 7. Drift Detection and Continuous Monitoring Implement tools to detect configuration drift between deployed resources and IaC templates. Regularly scan deployed resources for compliance. Set up alerts for unauthorized changes or policy violations. Benefits of Shift-Left Secure IaC Here are the key benefits of adopting Shift-Left Secure IaC, tailored for cloud-native teams focused on automation, compliance, and developer enablement: Early Risk Detection and RemediationFaster, More Secure DeploymentsAutomated Compliance EnforcementReduced Human Error and Configuration DriftImproved Developer ExperienceEnhanced Auditability and TraceabilityReduced Cost of Security FixesStronger Governance with IAM and RBACContinuous Posture Assurance Conclusion Adopting a shift-left approach to secure IaC in cloud platforms isn’t just about preventing mis-configurations—it’s about building smarter from the start. When security is treated as a core part of the development process rather than an afterthought, teams can move faster with fewer surprises down the line. With cloud services like Schematics, Projects, Secrets Manager, Key Management, Cloud Formation, and Azure Blueprints, organizations have all the tools they need to catch issues early, stay compliant, and automate guardrails. However, the true benefit extends beyond security—it establishes the foundation for platform engineering. By baking secure, reusable infrastructure patterns into internal developer platforms, teams create a friction-less, self-service experience that helps developers ship faster without compromising governance.

By Josephine Eskaline Joyce CORE

Modern software development is inherently global. Distributed engineering teams collaborate across time zones to build, test, and deploy applications at scale. DevOps, the practice of combining software development (Dev) and IT operations (Ops), is essential to achieving these goals efficiently. One of the primary challenges in this setting is simplifying the Continuous Integration and Continuous Delivery (CI/CD) pipeline in the cloud, enabling global teams to collaborate seamlessly. Challenges of Managing Multinational Teams Operating in multiple countries offers significant opportunities, but it also comes with challenges, particularly for multinational software teams. Time zone differences: Delays in communication and handoffs.Regulatory compliance: Adhering to local data laws (e.g., GDPR, HIPAA).Communication barriers: Language and cultural differences.Legal and financial complexities: Tax, residency, and operational scaling. Just as containerization standardizes deployment environments, a well-architected CI/CD pipeline acts as the "universal runtime" for global software delivery. Additionally, legal and financial complexities make scaling global operations even harder. Interestingly, the concept of the second passport comes into play here for executives and technical leaders who work across borders. A second passport allows individuals to manage travel, residency, and tax considerations more flexibly, easing the process of leading multinational teams without the hindrance of jurisdictional restrictions. Similarly, a streamlined CI/CD pipeline can act as a technological passport, enabling developers and engineers to move code efficiently across the globe. CI/CD Pipelines: The Backbone of DevOps The CI/CD pipeline is the backbone of any successful DevOps strategy, ensuring that code is tested, integrated, and deployed automatically, allowing teams to focus on innovation rather than manual processes. For multinational teams, it’s especially critical that these pipelines must be, Reliable: Minimizing failures in distributed environments.Fast: Reducing delays caused by time zone differences.Scalable: Supporting a geographically diverse workforce. In a DevOps ecosystem, seamless CI/CD pipelines prevent bottlenecks, enabling teams to develop, test, and deploy features or bug fixes quickly, regardless of their location. CI/CD: The Nervous System of Modern Software Delivery For engineering teams shipping code internationally, CI/CD pipelines must be: Deterministic: Same commit, same result in any regionLow-latency: Fast feedback loops despite geographical dispersionObservable: Granular metrics on build times, test flakes, deployment success Example: A team in Berlin merges a hotfix that automatically triggers: Parallelized testing in AWS us-east-1 and ap-southeast-1Compliance checks for data residency requirementsCanary deployment to Tokyo edge locations Some of the leading Cloud Providers for DevOps are listed below, along with their key strengths. Platform DevOps Advantage Ideal For AWS Broad DevOps tools, global data centers, strong compliance Teams needing global redundancy Azure Seamless integration for Microsoft-based enterprises Microsoft ecosystem shops Google Cloud Superior Kubernetes & container orchestration Kubernetes-native organizations Building a Unified DevOps Culture Across Borders DevOps isn’t just a set of tools and processes - it’s a culture that promotes collaboration, continuous improvement, and innovation. For multinational teams, building a unified DevOps culture is critical to ensuring that everyone is aligned toward the same objectives. This begins with focusing on open communication and collaboration tools that work seamlessly across different time zones and languages. To create a cohesive culture, organizations need to adopt common workflows, coding standards, and development philosophies. Encouraging transparency and responsibility will help team members from various nations work more effectively together. Furthermore, supporting this alignment are frequent team sync-ups, cross-border information sharing, and a feedback culture. Automation in CI/CD Pipelines: A Global Necessity Manual interventions in distributed teams lead to delays. Automation eliminates these bottlenecks by: Automated testing: Ensuring code quality before deployment.Automated deployments: Enabling 24/7 releases across time zones.Consistent standards: Reducing human error. In global teams, time zone differences can result in significant delays caused by manual interventions. By automating key stages of the pipeline, teams can push code updates and new features around the clock, ensuring that business never stops, no matter where developers are located. Automated tools like Jenkins: Open-source automation server.Travis CI: Cloud-based CI for GitHub projects.CircleCI: Fast, scalable pipeline automation. Collaboration Tools for Multinational DevOps Teams DevOps processes rely on effective teamwork, especially when team members are distributed globally. Many tools support project management and continuous communication, enabling teams to remain in agreement even across great distances. Slack: Instant messaging and integrations.Jira: Agile project management and issue tracking.GitHub/GitLab: Code collaboration and version control. Cloud-Native CI/CD Solutions for Global Scalability As global teams grow, scalability becomes a key concern. Cloud-native CI/CD solutions, such as Kubernetes, Docker, and Terraform, are ideal for multinational organizations looking to scale their operations without sacrificing efficiency. These tools enable teams to deploy applications in any region, leveraging cloud infrastructure to manage containers, orchestrate workloads, and ensure uptime across multiple time zones. Using cloud-native technologies enables international teams to quickly meet evolving corporate needs and deliver benefits to consumers worldwide. Kubernetes, in particular, offers seamless orchestration for containerized applications, allowing teams to manage their CI/CD pipelines more effectively. Managing Compliance and Security in Multinational CI/CD Pipelines Security and regulatory compliance are major concerns for global teams, especially when operating in countries with stringent data protection laws. CI/CD pipelines must be designed to ensure that code complies with local regulations, including GDPR in Europe or HIPAA in the United States. Multinational teams must incorporate security best practices into their development pipelines, including automated vulnerability scanning and secure deployment practices. Additionally, ensuring that data is stored and processed in compliance with local laws is crucial for avoiding potential legal issues in the future. Monitoring and Optimizing Global DevOps Performance Real-time insights help teams maintain efficiency: Prometheus: Metrics monitoring.Grafana: Visualization and analytics.Datadog: Full-stack observability. Tracking deployment frequency, lead time, and failure rates helps optimize performance. Real-World Case Studies: Successful Global DevOps Implementations To better understand the benefits of streamlining CI/CD pipelines for multinational teams, it’s useful to look at real-world examples. Companies like Netflix, Amazon, and Spotify have successfully implemented global DevOps strategies that leverage cloud infrastructure and automation to streamline their workflows. These companies have adopted cloud-native technologies and automated their CI/CD pipelines, allowing them to scale quickly and deploy updates to users worldwide. By following their example, other multinational teams can achieve similar success. Future-Proofing Your CI/CD Pipeline for Global Growth As global collaboration becomes more common, it’s crucial for organizations to streamline their CI/CD pipelines in the cloud to support multinational teams. Businesses can future-proof their CI/CD pipelines and guarantee they are ready for worldwide expansion by using cloud-native tools, automating important operations, and creating a consistent DevOps culture. Streamlining DevOps in the cloud is not only about efficiency but also about enabling teams to collaborate seamlessly across borders. Through automation, security best practices, or real-time monitoring, a well-optimized CI/CD pipeline will determine the course of global software development.