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Dev
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HOW TO USE AN AZURE CUSTOM TEMPLATE TO DEPLOY A VIRTUAL MACHINE.
- Deploying a custom template in Azure refers to the process of using an Azure Resource Manager (ARM) template to provision and configure resources in your Azure environment programmatically.
- Deploying a custom template in Azure means using a JSON file to define and automate the creation of resources like storage accounts or virtual machines.
- There are different ways or options of creating a virtual machine in Azure, including Azure portal, Azure PowerShell, Azure Mobile app, Command Line Interface (CLI), Azure REST API, Azure Cloud Shell, and Azure Resource Manager Templates.
- This article explains how to use an Azure custom template to deploy a virtual machine.
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The New Stack
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Streamlining Kubernetes Implementation With GitOps: Best Practices
- GitOps is a method for deploying, managing and running applications within the Kubernetes ecosystem.
- GitOps is based on three main principles: declarative configuration, version control, and automated deployment.
- Real-world benefits of GitOps include improved developer productivity, enhanced collaboration, consistent environments, auditability and compliance, and scalability and flexibility.
- Best practices for implementing GitOps include starting small, using a GitOps operator, embracing CI/CD, and monitoring and observing.
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Solarwinds
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5 Traffic Analysis Functions Your Observability Solution Should Have
- Robust traffic analysis capabilities are essential for optimizing bandwidth in your environment.
- Monitor and analyze network traffic patterns using various flow formats.
- Ensure granular traffic analysis by dissecting network traffic in detail.
- Create effective network-wide visualizations to identify patterns and anomalies.
- Identify top bandwidth consumers and deliver data-driven insights for intelligent resource allocation.
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Dev
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DevOps vs. SRE: Bridging the Gap, Not Building Walls
- DevOps and SRE (Site Reliability Engineering) approach reliable and efficient software delivery from different angles.
- DevOps is a cultural philosophy that emphasizes collaboration, automation, and continuous improvement.
- SRE focuses on the implementation of reliability by applying software engineering principles to operations.
- To bridge the gap, organizations should define clear roles, adopt SLOs and error budgets, embrace automation, promote collaboration, learn from failures, focus on measurable metrics, and iterate for improvement.
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Medium
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Setting Up AWS CodeBuild for macOS Application Builds
- 1. Sign in to AWS Console: Navigate to the AWS CodeBuild service.
- 2. Click on “Create Build Project” and provide a project name and description.
- 3. Choose Source Repository: Select your source provider (GitHub, CodeCommit, Bitbucket, or S3) and provide the repository details.
- 4. Choose Environment:
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Dev
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Creating an Azure Compute Gallery and Capturing the Image of a Virtual Machine (VM)
- Azure Compute Gallery in Azure allows users to manage and share VM images effectively across subscriptions and regions.
- The gallery serves as a central repository for storing images and streamlines consistent environment deployment.
- Prerequisites for creating an Azure Compute Gallery include an active Azure subscription and understanding of VMs.
- Step 1 involves creating the Azure Compute Gallery by providing necessary details like subscription, resource group, and region.
- Step 2 focuses on preparing the VM for image capture, ensuring it is in the correct state with required configurations.
- The specialized state of a VM involves unique configurations, applications, and data, necessary for specific deployments.
- Specialized VMs are tailored for environments requiring specific settings, while generalized VMs remove unique configurations for reuse.
- Step 3 covers capturing the specialized VM image by naming it, selecting the Compute Gallery, and confirming its specialized state.
- Generalizing a VM is recommended for creating reusable, standardized images for scaling purposes.
- Step 4 enables users to use the captured image from Azure Compute Gallery to deploy new VMs, either in the same region or across regions.
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Dev
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Everything* as code
- Coding has been used to solve problems for decades and has evolved to be a vital part of our lives.
- The term "code" originated even before computers, from the practice of encoding information.
- Modern software engineering follows the principle of structuring information for efficient retrieval, automation, and tracking.
- Software encapsulates logic, following a structured and predictable approach for reliable results.
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Dev
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Choosing the tech stack for your SaaS
- Choosing the right technology stack for your SaaS business is crucial for success, impacting scalability, performance, and costs.
- Key aspects covered in the article include backend and frontend tech selection, cloud infrastructure impact, DevOps tools, and cost optimization strategies.
- Decision makers in tech stack selection bring unique priorities, aiming to align technical, business, and financial needs for long-term success.
- Real-world example: A FinTech startup adopts a hybrid approach with Kubernetes for core services and serverless for event-driven workloads.
- Frontend tech choices directly impact user engagement, SEO, and development efficiency, with recommendations for different scales of SaaS apps.
- Backend technology choices affect performance, scalability, and cost-efficiency, with considerations like scalability, cost, maintenance, and performance.
- Cloud infrastructure selection is crucial for scalability, reliability, and cost-efficiency, with considerations for scalability, cost efficiency, service integration, and global reach.
- Best practices for deployment models include single cloud for small to mid-sized SaaS, multi-cloud for global SaaS, and hybrid cloud for enterprise SaaS with legacy integration.
- Infrastructure as code (IaC) automates server provisioning and scaling, enhancing reliability and repeatability in SaaS applications.
- Performance optimization techniques like CDN usage, database indexing, API response caching, and edge computing help improve user experience and reduce costs.
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Hackernoon
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Turbocharging AI Sentiment Analysis: How We Hit 50K RPS with GPU Micro-services
- The article discusses how a sentiment analysis pipeline faced challenges due to high traffic, leading to the shift to microservices leveraging technologies like Kubernetes, GPU autoscaling, and a streaming ETL pipeline.
- Initially a monolithic design was used, but as traffic increased, over-provisioning every component became unsustainable, prompting the transition to microservices.
- Key components of the new microservices architecture include API Gateway, Text Cleanup & Tokenization, GPU-Based Sentiment Service, Data Storage & Logs, and Monitoring.
- Containerization was a pivotal step, with a Dockerfile for the GPU-enabled inference service enabling GPU acceleration and efficient orchestration via Kubernetes.
- Kubernetes allowed for GPU autoscaling based on utilization, ensuring optimal performance and cost efficiency by spinning up additional pods as needed.
- Batch inference and async I/O were utilized to boost performance, with multiple requests being batched together for efficient GPU resource utilization.
- Real-time ETL was achieved using Kafka for streaming, Spark for transformation, and Redshift for storage to handle high-volume social data ingestion.
- Challenges included initial memory issues and the importance of tuning batch sizes, balancing between analytics and end-user request latency.
- The integration of microservices, GPU acceleration, and a streaming-first ETL architecture led to a high-performance sentiment analysis pipeline capable of handling 50K RPS.
- The approach ensures minimal resource waste and scalability, allowing for adaptability to surging data volumes in real time.
- By embracing microservices and real-time data flows, the system can efficiently scale, ensuring reliability and readiness for any future traffic spikes.
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Dev
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How Containerization and Kubernetes Revolutionize Software Deployment Efficiency
- Containerization and Kubernetes are pivotal technologies that enhance software deployment by offering benefits like scalability, resilience, and consistency across different environments.
- Containerization packages applications and dependencies into lightweight, isolated containers, ensuring portability, faster startup times, and resource optimization.
- Containers provide consistency across environments, reducing the risks of errors due to version mismatches or configuration differences.
- Portability is a key advantage of containers, allowing consistent deployment across various environments supported by container technologies like Docker.
- Kubernetes automates container orchestration, providing benefits such as automated scaling, self-healing, and streamlined updates with rolling deployments.
- Kubernetes ensures efficient resource management, cost optimization, and seamless communication between containers through built-in networking features.
- Declarative configuration in Kubernetes simplifies infrastructure management by defining the desired state using configuration files.
- The integration of containerization and Kubernetes in DevOps practices enables faster application delivery, continuous testing, and improved software quality.
- Overall, containerization and Kubernetes revolutionize software deployment, delivering consistency, efficiency, and automation to modern software development practices.
- As organizations adopt microservices and cloud-native architectures, containerization and Kubernetes will continue to play a significant role in shaping the future of software deployment.
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The New Stack
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Breaking the Chains of Kube-Proxy With Cilium
- Kubernetes, the standard for container orchestration, faces limitations with default settings like kube-proxy as clusters scale and workloads become more demanding.
- Default configurations in Kubernetes can be limiting due to the platform's modular nature, leading to complexity as organizations push the platform's limits.
- Cilium, a CNCF-graduated CNI, addresses challenges posed by kube-proxy by leveraging modern networking capabilities built on eBPF.
- Kube-proxy's challenges include performance overheads due to managing forwarding rules, operational complexity in updating rules, and limits on load balancing efficiency.
- Cilium, by eliminating kube-proxy, provides an eBPF-based approach for better networking in modern cloud native environments, improving efficiency, security, and observability.
- Efficiency gains with Cilium include reduced latency and improved throughput by handling packets in the kernel, enhancing security policies and real-time traffic insights.
- Success stories with Cilium showcase tangible benefits for organizations across industries by leveraging eBPF-based networking in Kubernetes environments.
- Cilium's 'kube-proxy replacement' mode simplifies traffic load balancing using eBPF, leading to performance gains, simplification, and enhanced security benefits.
- Cilium offers a path to drop kube-proxy, improving networking efficiency and security with eBPF-based load balancing directly in the kernel.
- By eliminating heavy rule-based overhead, Cilium speeds up traffic, provides deeper visibility, and demonstrates maturity to become the de facto standard for cloud native networking.
- For more information on Cilium and cloud native networking, refer to the official documentation or join the Cilium Slack channels for real-world advice and support.
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Silicon
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Utah Passes App Store Age Verification Bill
- The state of Utah has passed legislation that requires app stores to verify the ages of users who download apps.
- The new law also mandates parental consent for minors to download apps on their devices.
- This move is part of a global effort to protect children from the impact of social media and online platforms.
- Similar bills have been introduced in at least eight other U.S. states, with Meta Platforms supporting the responsibility of app stores in age verification.
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Hashicorp
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HashiCorp and Red Hat, better together
- HashiCorp and Red Hat recently announced their collaboration under IBM, aiming to provide a 'better together' experience for users and customers.
- The focus is on integration of HashiCorp Terraform with Red Hat Ansible Automation Platform and HashiCorp Vault with Red Hat OpenShift for enhanced solutions.
- HashiCorp Terraform's infrastructure automation capabilities complement Red Hat Ansible's operational management platform for efficient configuration and execution of tasks.
- Plans include dynamic inventory generation, official Terraform modules for Ansible, and supporting Ansible provider for Terraform.
- The goal is seamless automation across infrastructure lifecycle with improved mechanisms for invoking Ansible Playbooks.
- HashiCorp Vault's security features align with Red Hat OpenShift's application platform for secrets management, certificate automation, and enhanced data encryption.
- The collaboration aims to provide a 'push-button' experience for securing the application platform by default, reducing configuration points and enhancing ease of use.
- Opportunities for deeper collaboration extend to broader IBM technologies, including bringing FinOps capability to Terraform with Apptio and extending zero trust approach to various solutions.
- Future integrations may involve IBM Guardium, IBM Z systems, and additional synergies within the IBM portfolio.
- The collaborative efforts seek to shape a more integrated and efficient experience for the community and customers, with ongoing developments to be revealed.
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The New Stack
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Transferring Trillions of Dollars Daily Using Kubernetes With No Internet
- Swift operates financial market infrastructure handling trillions of dollars daily transactions in a highly secure offline environment.
- Kubernetes clusters deployed by Swift are disconnected from the internet and managed using GitOps for updates and patches.
- Using a GitOps model, Swift ensures secure and up-to-date Kubernetes cluster deployment globally.
- Red Hat Advanced Cluster Management and Kubernetes are utilized to manage clusters at scale with zero-privilege implementation.
- Swift's architecture includes Hub and App clusters, managed centrally for consistent versioning and security.
- Argo CD automates changes to clusters based on Git repository updates, ensuring consistency and efficiency.
- Swift has reduced cluster deploy and test time to under 90 minutes per cluster, enhancing operational efficiency.
- Security and regulatory requirements are met by tracking operations performed on clusters and ensuring cluster health before updates.
- The system enables Swift to manage cluster deployments globally, control upgrades, reduce costs, and improve developer velocity.
- Swift's approach facilitates secure transactions while maintaining strict control over cluster management and updates.
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Amazon
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A lightning fast, new agentic coding experience within the Amazon Q Developer CLI
- Amazon Q Developer announced an enhanced CLI agent within the Amazon Q CLI, providing a more dynamic, interactive coding experience.
- The CLI agent leverages the power of Amazon Bedrock and features Claude 3.7 Sonnet step-by-step reasoning.
- It utilizes tools installed on the system, such as compilers and package managers, and supports multi-turn conversations.
- One example demonstrated how the CLI agent efficiently scaffolded a new application, utilized Git commands, and provided summaries of completed work.
- The enhanced CLI agent can read/write local files and suggest next steps, enhancing developer productivity.
- The CLI agent can also interact with AWS resources, install necessary SDKs, and update application files efficiently.
- Developers can work collaboratively with the CLI agent through natural language prompts and receive intelligent responses.
- Support for multi-turn conversations allows seamless interaction and faster completion of tasks without leaving the CLI environment.
- The CLI agent transforms software development by providing AI assistance directly in the command-line environment, speeding up complex tasks.
- By following the Amazon Q Developer User Guide, developers can install the CLI and start benefiting from the new agent capabilities for enhanced workflow.
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