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Devops News
Dev
2w
240
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Why I Stopped Doing Everything Manually in DevOps
- Manual work in DevOps can lead to inefficiencies, errors, and inconsistencies in deployments.
- Automation tools like Ansible offer a more efficient way to handle tasks like server configuration.
- Automation allows for consistent provisioning, updates, and frees up time for higher-value work.
- Shifts in mindset include prioritizing automation, reducing manual intervention, and designing for scale and resilience.
- The key question in DevOps is always: 'Can this task be automated?'
- Automation aims to enhance human ingenuity by eliminating repetitive tasks.
- The article suggests starting small with automation to experience time and reliability benefits.
- Automation is not about replacing humans but about enabling them to focus on complex problems and innovation.
- The importance of sharing automation stories and learning from others in the field is highlighted.
- The article also promotes an e-book on DevOps interview questions for comprehensive preparation.
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Amazon
3w
1.2k
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Continue to take control over your code with Amazon Q Developer’s new context features
- Amazon Q Developer has introduced new features in VS Code to offer more control over coding conversations and context management.
- These enhancements include conversation persistence, chat history search, and conversation export capabilities.
- With conversation persistence, users can continue where they left off across IDE sessions without losing context.
- The chat history search feature allows users to quickly find and review previous conversations with Amazon Q Developer.
- Users can now export chat sessions as markdown files for offline use or collaboration purposes.
- Amazon Q Developer also provides improved context control by allowing users to specify folders, files, functions, and variables to include in conversations.
- By utilizing symbols like '@', users can ensure that the relevant code components are considered for more precise responses.
- These new features aim to enhance developers' efficiency and help them maintain a smooth development flow even across longer periods.
- Amazon Q Developer continues to evolve to empower developers with tools that streamline their coding experience.
- Users can access these features in VS Code by referring to the Amazon Q Developer Getting Started guide.
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8 Likes
Dev
3w
314
Image Credit: Dev
End-to-End DevOps: Running a .NET App in Kubernetes with Docker, ACR, and AKS
- The article demonstrates the process of deploying a .NET application in Kubernetes using Docker, Azure Container Registry (ACR), and Azure Kubernetes Service (AKS).
- The tutorial starts with building a sample web application in dotnet core using Visual Studio Code and testing it in a local Kubernetes cluster.
- The infrastructure creation involves cloning a repository, checking for the .Net SDK, and building the application.
- After testing locally, the process moves on to building a Docker image, containerizing the application, and running it on a local Kubernetes cluster.
- The deployment to Azure Kubernetes Service (AKS) is detailed, including creating an AKS cluster, connecting via Azure CLI, and pushing the Docker image to Docker Hub and Azure Container Registry.
- Scaling the application using Kubernetes commands and pushing the image to Azure Container Registry for AKS integration are explained in the article.
- The tutorial covers creating deployments, exposing services, checking statuses, accessing deployments outside the cluster, and scaling the application.
- The article provides a comprehensive guide for developers looking to deploy .NET applications on Kubernetes using Docker, ACR, and AKS.
- It emphasizes steps such as testing applications locally, building Docker images, and configuring Kubernetes deployments on Azure.
- The tutorial also includes instructions on pushing images to Docker Hub and Azure Container Registry, as well as scaling deployments in Kubernetes.
- The processes of connecting to AKS via Azure CLI, verifying node information, and pushing images to ACR for AKS deployments are well-documented.
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18 Likes
The New Stack
3w
327
Image Credit: The New Stack
7 Major Gaps in Today’s GitOps Tools
- GitOps has transformed infrastructure management with Git as the single source of truth, replacing manual processes with automation.
- Tools like Argo CD and Flux CD have been pivotal in Kubernetes deployments, embracing GitOps principles.
- GitOps tools face challenges in security, scalability, and operational practices as cloud native environments evolve.
- Challenges in GitOps tools include multicluster management complexity, policy-based deployments, and SLO-driven rollbacks.
- Current GitOps tools lack native support for GitOps promotion and application-aware functionalities.
- The next-gen GitOps platforms require flexible rollback capabilities, advanced deployment strategies, and simplified multicluster deployments.
- Built-in observability, role-based access control, and approval policies are essential for a modern GitOps platform.
- Devtron aims to enhance GitOps with features like flexible rollbacks, advanced deployment strategies, and strong observability.
- Devtron provides built-in approval gates, fine-grained RBAC, and robust observability, emphasizing security and transparency in deployments.
- The platform offers full control to teams for smarter and safer deployments, supporting software delivery acceleration without compromising quality.
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19 Likes
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Dev
3w
126
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5 developer tools to really step up your workflow
- Lazygit is a TUI for git management, allowing easy changes, commits, and branch operations.
- LazyDocker is a TUI manager for Docker, providing insights into running containers, available images, logs, metrics, and volumes.
- Vim keybindings or using Neovim can enhance coding efficiency, especially with extensions in IDEs like VSCode for Vim support.
- Obsidian is a note-taking tool with plugin support and note graph visualization, suitable for all users, and Tmux enables terminal multiplexing for efficient multitasking.
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7 Likes
Dev
3w
170
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Creating Reports to Find Missing Data During the Normalization Process in SQL
- Creating reports in SQL to find missing data during the normalization process is crucial for maintaining data integrity.
- Common SQL reporting patterns like NOT EXISTS, IS NULL, CASE, and boolean-style flags can help detect and quantify data anomalies.
- Examples include identifying orphaned records, detecting null values in critical fields, and flagging inconsistent data types or logic.
- By automating the reporting pipeline, outputting results to dashboards, and following best practices, SQL-based data integrity reports can be made more actionable and accessible.
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10 Likes
Kubernetes
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139
Kubernetes 1.33: Job's SuccessPolicy Goes GA
- Kubernetes 1.33 release includes Job success policy graduating to General Availability (GA).
- Job success policy allows specifying early exit criteria for Kubernetes Jobs to mark them as succeeded based on rules set.
- This feature is beneficial for batch workloads like scientific simulations, AI/ML, and High-Performance Computing (HPC).
- Users can define custom success policies for Jobs, allowing flexibility in handling experiment outcomes and terminating Pods accordingly.
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8 Likes
Dev
3w
428
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Kubernetes Security in 2025: Best Practices for Protecting Your Clusters
- Implement Role-Based Access Control (RBAC) to control access to Kubernetes resources.
- Define network policies to restrict communication between pods for increased security.
- Enable audit logging to monitor API requests and detect suspicious activities in your cluster.
- Regularly update Kubernetes to apply the latest security patches and mitigate vulnerabilities.
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25 Likes
Dev
3w
314
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Ping! Pop! Pow! Real-Time Security with Suricata, StackStorm & Slack.
- The blog discusses combining Suricata, an IDS tool, with StackStorm, an event-driven automation engine, to enhance real-time security monitoring.
- The setup aims to streamline security operations by automatically alerting suspicious network activity detected by Suricata via StackStorm triggers sent to Slack.
- Key components used include Suricata for network intrusion detection, StackStorm for automation, and Slack for receiving alerts in a user-friendly interface.
- The process involves setting up Suricata alerts, configuring StackStorm sensors, defining rules for handling triggers, and integrating with Slack for immediate alert notifications.
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18 Likes
The New Stack
3w
433
Image Credit: The New Stack
Manage Complex Kubernetes Workflows With an AI Solution
- Late-night incidents in complex cloud native environments can be challenging to debug and resolve, often requiring thorough investigation of pod statuses, events, logs, resource utilization, and deployment histories.
- The article discusses a systematic approach to debugging system issues, emphasizing the importance of methodical investigation to uncover root causes effectively.
- Skyflo.ai is introduced as an AI-driven solution that automates operational tasks in cloud native environments, offering Planner, Executor, and Validator Agents to streamline troubleshooting processes.
- With Skyflo.ai, users can describe issues in natural language, allowing the AI agents to collect diagnostics, propose solutions, and execute changes, reducing the manual effort and speeding up incident resolution.
- AI-powered automation, like Skyflo.ai, transforms cloud native operations by enhancing speed, efficiency, accessibility, reducing the risk of human error, and facilitating continuous learning for improved troubleshooting.
- The platform's ability to handle repetitive tasks and provide intelligent suggestions empowers users to focus on higher-level decision-making and system design rather than tedious firefighting.
- Skyflo.ai's AI-driven approach offers benefits for both experienced cloud architects and junior developers, enabling faster insights, automated tasks, and decreased incident response times.
- By contributing to and supporting the Skyflo.ai project on GitHub, users can help shape the future of AI-assisted cloud native operations and participate in the evolution of intelligent automation in DevOps.
- Skyflo.ai's AI agents work together to orchestrate cloud native tools, interpret commands, execute tasks, and validate changes, offering a comprehensive and efficient approach to managing Kubernetes workflows.
- The article concludes by highlighting the transformative potential of AI-powered solutions like Skyflo.ai in revolutionizing how complex cloud native challenges are approached and resolved.
- For those interested in AI-driven cloud native workflows, the Skyflo.ai project on GitHub provides a platform for collaboration, feedback, and innovation to advance AI-assisted operations in the DevOps landscape.
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26 Likes
Dev
3w
236
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Speed Up Your CI/CD: ARM 64 Runners for GitHub Actions
- GitHub has introduced Arm64 support for runners in GitHub Actions, enhancing multi-architecture development workflows.
- Arm64 runners offer significant speed improvements with some customers witnessing a reduction in build times from 3 hours to just 20 minutes.
- Developers can efficiently create multi-architecture Docker images and contribute to reducing carbon emissions as Arm processors consume 30-40% less power in data centers.
- Arm64 runners are free for public repositories with unlimited minutes, allowing for faster and more efficient workflows across diverse architectures.
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14 Likes
Dev
3w
288
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Instantly Export Live GCP Infrastructure to Pulumi – No Import, No Terraformer
- Pulumi users facing challenges in migrating or starting Infrastructure as Code (IaC) in Google Cloud Platform (GCP) projects.
- Introducing StackForge, a system that connects to GCP projects and instantly generates Pulumi Main.yaml, Pulumi state, and offers optional GCS upload without deployment requirements.
- Use cases include agencies managing multiple GCP clients, internal platform teams onboarding cloud projects to Pulumi, and large organizations aiming for repeatable IaC in GCP.
- Early access available for DevOps and platform teams to try out StackForge, provide feedback, and shape future developments.
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17 Likes
Testim.io
3w
144
Test Automation, Optimization, and Management
- Tricentis integrates Testim with SeaLights and qTest to streamline test automation, optimization, and management, enhancing application quality and resource utilization.
- Testim's AI-powered test creation, smart locators, and self-healing tests allow teams to automate complex workflows efficiently.
- SeaLights, a continuous quality optimization platform, utilizes AI to provide insights into test coverage, aiding teams in focusing testing efforts effectively.
- SeaLights offers comprehensive code coverage analysis, quality gates, and test optimization to enhance overall software quality and stability.
- The integration of Testim and SeaLights ensures optimized test execution, identifies coverage gaps, and enforces quality gates for high-quality releases.
- qTest's centralized test management, enhanced reporting, and interactive dashboards improve collaboration and visibility in test processes.
- The integration of qTest and Testim enhances test visibility, efficiency, and collaboration, providing a comprehensive testing solution.
- Utilizing qTest and Testim together simplifies test management and execution, fostering collaboration and informed decision-making.
- The integration of Testim with SeaLights and qTest offers benefits such as improved efficiency, faster time to market, and reduced risk in software development.
- By combining test automation, optimization, and management, Tricentis enables teams to deliver high-quality applications efficiently and effectively.
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8 Likes
Dev
3w
363
Image Credit: Dev
🐾 Setting Up the Raspberry Pi-Based IoT Cat Feeder (Day-27)
- Day-27 guide covers setting up an IoT-based pet feeder using a Raspberry Pi with a servo motor controlled by Python and cron jobs.
- Objective: Establish an automated pet feeder with a Raspberry Pi running a Python script to feed your cat daily.
- Steps mentioned include flashing Raspberry Pi OS to a 32GB microSD card, enabling SSH access, configuring Wi-Fi for headless boot, and booting the Pi.
- Despite successful OS flashing and SSH enablement, issues with SSH connection, network detection, and hostname access were encountered, setting up for the next steps in Day-28.
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21 Likes
Medium
3w
381
Image Credit: Medium
Building GenAI solutions on Amazon EKS
- Self-hosting AI solutions on Amazon EKS provides more control over sensitive data and hybrid deployment, enhancing quality and productivity for teams through integration of existing data sources.
- Customizing models through self-hosting allows tuning to specific needs, resulting in competitive performance with cost efficiency.
- Hosting models on the same platform as applications reduces latency, ensuring better user experience, particularly for applications with strict latency requirements.
- Deploying generative AI applications in production requires complementary components like prompt caching, guardrails for safe usage, observability tools, and model evaluation capabilities.
- A self-hosting stack requires multiple components, leveraging open-source software to self-host models in a cost-efficient manner with Amazon EKS.
- Key components include Model Gateway Service, Model Hosting Services, Model Evaluation Services, and Model Observability Services, all running as containers using Amazon EKS.
- Model Gateway enables centralized access to models, hybrid inferencing, and secure model access, while Model Hosting Services expose model weights as APIs with efficient inferencing capabilities.
- Model Evaluation Services assist in choosing the right model through rigorous testing, real-time evaluation, and continuous optimization of models and prompts.
- Model Observability Services capture essential metrics like latency and payload data, enabling better model assessment and optimization for changing business needs.
- Open-source frameworks such as LiteLLM, vLLM, LangFuse, and others fulfill the roles of key components for self-hosting AI solutions on Amazon EKS.
- LangFuse offers observability by tracking various data points, while also assisting in model evaluation through benchmarking and automated evaluation methods.
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22 Likes
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