Devops latest news and discussion articles on Techminis

A naukri.com initiative

New

Home

Devops News

Unite

179

Image Credit: Unite

Speed Without the Stress: How AI Is Rewriting DevOps

The demand for faster software development has put pressure on DevOps teams, leading to burnout and inefficiencies in observability tools.
Observability is about understanding the story behind data, not just collecting vast amounts of it.
AI plays a crucial role in simplifying observability by providing contextual analysis and actionable insights.
DevOps teams face burnout due to being overloaded with multiple responsibilities and constant firefighting.
AI can automate tasks like alert resolution, anomaly detection, and log correlation, reducing the burden on DevOps engineers.
By offering real-time anomaly detection, predictive failure modeling, and automated resolution, AI lightens the workload of DevOps engineers.
AI amplifies the work of engineers, allowing them to focus on designing systems rather than mundane tasks.
Increasing developer velocity requires removing friction and integrating security throughout the development process with the help of AI.
AI enables faster deployments without compromising quality, ensuring that speed and stability work in harmony.
With AI, DevOps becomes a sustainable journey towards smarter systems, allowing teams to embrace speed without stress.

Read Full Article

10 Likes

Dev

339

Image Credit: Dev

Securing Redis with ACLs and Integrating Redis Insight in Docker

Securing Redis deployment with ACLs is essential for various use cases such as caching, messaging, and data storage.
Redis 6 introduced an Access Control List (ACL) system to restrict commands and keys per user.
Enabling ACLs can pose challenges for monitoring tools like Redis Insight, which may try to connect without credentials.
Redis Insight does not connect by default when ACLs are enabled, as the default user lacks permissions.
Writing a valid users.acl file is essential for defining user permissions accurately in Redis ACL configurations.
Ensure correct syntax for each user, including username, status (on/off), password, key pattern, and command/category permissions.
Examples of valid ACL lines include disabling default user, creating users for Insight and application, and specifying full permissions.
Deploying Redis with ACLs in Docker involves creating a separate container for Redis with ACLs enabled and another for Redis Insight.
Verify ACLs are loading properly in Docker containers by checking Redis logs for any errors and testing connections with redis-cli.
Deploying Redis Insight in Docker includes removing old containers, running Redis Insight on the same network, and verifying its functionality.

Read Full Article

20 Likes

Dev

343

Image Credit: Dev

Navigating the DevOps Career Path: From Junior to Principal Engineer

The DevOps career path offers opportunities for growth, innovation, and continuous learning.
Progressing from a Junior DevOps Engineer to a Principal DevOps Engineer involves mastering skills, taking on more responsibilities, and gaining relevant experience.
Each level in the DevOps career ladder, including Junior, Intermediate, Senior, Staff, and Principal Engineers, has specific roles, responsibilities, and required expertise.
Real-world DevOps career journeys may vary based on company culture and individual growth, highlighting the importance of continuous learning and adaptation.

Read Full Article

20 Likes

Dev

17h

Image Credit: Dev

🚀 Amazon EKS Now Supports Kubernetes 1.33 — What’s New and Why It Matters

Amazon EKS and EKS Distro now support the latest Kubernetes 1.33 release.
Kubernetes 1.33 brings improvements in scalability, observability, and developer flexibility.
Notable features in Kubernetes 1.33 include stable support for sidecar containers, topology-aware routing and distribution, user namespaces for Linux pods, and more.
Users can upgrade existing EKS clusters to 1.33 via EKS Console, eksctl, Terraform, CloudFormation, or CDK with regional availability in all AWS Regions.

Read Full Article

4 Likes

Dev

17h

Image Credit: Dev

🚀 Automatically Mount S3 Buckets at Boot with Mountpoint for Amazon S3 + fstab

Amazon has introduced automatic mounting of S3 buckets on EC2 instances using fstab and Mountpoint for Amazon S3.
Previously, manual remounting of S3 buckets after every reboot was required, but now with fstab integration, automatic mounting is possible.
The step-by-step process involves creating a mount directory, testing manual mount, adding an entry to /etc/fstab, and testing the setup.
This enhancement simplifies infrastructure management, providing persistent S3 access, better automation and scripting options, and usefulness for containers, EC2 fleets, and bootstrapping.

Read Full Article

1 Like

Scrum-Master-Toolbox

22h

179

BONUS: Martti Kuldma shares how to transform century-old organizations through product-driven agile transformation

Martti Kuldma, CEO of Omniva, discusses the agile transformation of the century-old company.
Omniva transitioned from traditional postal services to modern logistics through product-driven agile practices.
The company shifted focus to software as a revenue center, investing in innovative logistics solutions.
Martti's strategic vision involves treating physical mailing as the next IP network for adaptable delivery.
The transformation involved breaking down waterfall processes, moving towards agile product teams.
Omniva created cross-functional product teams, empowering them with accountability for business outcomes.
Martti emphasized the evolution of product managers to be directly accountable for business results.
Leadership in transformation required storytelling skills for effective communication and alignment.
Real-time feedback philosophy was endorsed for immediate and impactful professional development.
Key transformation practices included compelling narratives, distributed decision-making, and business accountability for engineers.

Read Full Article

10 Likes

Dev

Image Credit: Dev

DevOps Interview Questions & Answers: The Ultimate Guide for 2025

The article provides a comprehensive guide for DevOps interview preparation in 2025, featuring 100+ real-world interview questions and answers.
It covers foundational DevOps concepts, including the key goals, lifecycle stages, differences from Agile, and core principles.
The guide delves into DevOps tools, such as Git, CI/CD, automation, Docker, Kubernetes, observability, monitoring, and Infrastructure as Code (IaC).
Key concepts like Pipeline-as-Code, Docker vs. Podman, Kubernetes, and observability are discussed in detail.
It also compares tools like Terraform vs. Ansible, discusses system design, Blue-Green Deployment, Canary Release, and Shift-Left Security.
The article emphasizes the importance of explaining thought processes, tailoring responses to tech stacks, practicing system design, and knowing tools for DevOps interviews.
Final tips include mastering technical and problem-solving skills and utilizing recommended learning resources like courses, certifications, and blogs.
DevOps interviews in 2025 aim to assess both technical proficiency and problem-solving abilities, requiring candidates to understand principles and think critically.
The advice emphasizes continual curiosity, skill development, and good luck in upcoming DevOps interviews.

Read Full Article

5 Likes

Amazon

362

Image Credit: Amazon

New and improved Amazon Q Developer experience in the AWS Management Console

Amazon Q Developer has introduced a new agentic experience in the AWS Management Console, enhancing user insights into AWS resources.
The tool now operates as a resource analysis and operational troubleshooting assistant across multiple information sources.
Users can ask questions about AWS services, with Amazon Q Developer selecting the appropriate tools from AWS APIs to provide detailed answers.
It can analyze relationships between resources, configurations, and respond to complex queries efficiently.
The new features facilitate deeper resource introspection analysis and increased operational troubleshooting efficiency.
Builders can gain comprehensive insights into AWS environments using natural language prompts.
Amazon Q Developer streamlines the process of understanding resource configurations and relationships.
The tool can troubleshoot operational issues efficiently by correlating information with logs and metrics.
Amazon Q Developer simplifies the process of diagnosing and resolving issues, enabling quick troubleshooting.
Overall, Amazon Q Developer provides comprehensive answers with fewer steps, aiding in better resource management.

Read Full Article

21 Likes

Medium

325

Image Credit: Medium

10 Must-Have Tools to Supercharge Your Linux Workflow (Including a Zen Bash Toolkit You’ve Never…

This post introduces 10 powerful tools and enhancements to improve your Linux workflow and boost productivity.
Tools like Tilda and Guake provide drop-down terminals for quick access, fzf allows lightning-speed searching, and htop offers real-time process views.
rg is a faster alternative to grep, tmux enables terminal splitting, bat enhances file previews, and ncdu helps free up disk space.
Monk Bash Kit is a unique, handcrafted toolkit designed for the terminal, offering clarity and productivity for just 5 USDT.

Read Full Article

19 Likes

Dev

180

Image Credit: Dev

Fedora CoreOS Setup Guide – Ignition Files Without the Frustration

The article provides a guide on setting up Fedora CoreOS using Ignition files without facing frustrations.
CoreOS setup challenges often stem from the initial entry point rather than complexity.
The guide simplifies the process of creating an Ignition file for a functional Fedora CoreOS system.
The Ignition file is crucial, defining the system configuration pre-installation.
A YAML file is created first, specifying SSH access for the default user, and then converted to JSON using Butane.
The JSON Ignition file is what CoreOS reads during the initial boot to configure the system.
The article details the process of making the Ignition file available to CoreOS during installation, often a stumbling block for users.
One method is hosting the file on a local HTTP server, easily set up using Python.
Alternative methods to provide the Ignition file include using a USB stick, cloud providers, or network boot.
The installation process involves booting from the Fedora CoreOS ISO and executing coreos-installer with the Ignition file URL.

Read Full Article

10 Likes

Dev

163

Image Credit: Dev

Confessions of a Cloudaholic

The author shares their experience of overspending on cloud services and provides tips on cost optimization.
Strategies include stopping hoarding cloud resources, embracing reserved instances, tagging resources for visibility and accountability, and right-sizing infrastructure.
Using spot instances for non-critical workloads, automating cost governance, and educating the team on cloud costs are also recommended.
The article emphasizes the importance of proactive cost management as a continuous culture shift in cloud operations.

Read Full Article

9 Likes

Dev

237

Image Credit: Dev

Optimizing PHP Applications on ClawCloud Run: A Comprehensive Template Guide

A seasoned PHP developer created an optimized PHP template solution on ClawCloud Run due to limited PHP options available in the App Store.
The template features the webdevops/php-apache Docker image that supports PHP 8.0-8.4 and plans for future templates like php-nginx and php-openlitespeed.
The deployment guide includes steps to locate the template, choose PHP version, configure access, and upload the application for successful deployment.
Advanced configuration tips involve fixing permission issues by setting ownership and adding write permissions if needed.

Read Full Article

14 Likes

Dev

342

Image Credit: Dev

Data Scientists vs DevOps: Building Balanced Tech Teams

Building successful tech teams requires understanding the relationship between data scientists and DevOps engineers as a critical partnership in modern tech organizations.
Balanced data science and DevOps capabilities lead to 40% faster project deployment times, highlighting the importance of collaboration between these roles.
Data scientists focus on extracting insights from data using statistical analysis and machine learning models, while DevOps engineers ensure smooth system operations and scalability.
Skills such as proficiency in cloud platforms, version control systems, and business understanding are essential for modern data scientists to succeed.
DevOps engineers specialize in infrastructure management, automation, and ensuring seamless transition of data science models from research to production environments.
Professionals with expertise in both data science and DevOps command significant salary premiums due to their ability to bridge critical organizational gaps.
Successful tech teams establish clear communication channels, collaborative workflows, and shared responsibility models between data scientists and DevOps engineers.
Technology stack integration and practices like MLOps represent the convergence of data science and DevOps, facilitating continuous integration and deployment of models.
Balanced teams require metrics that reflect both analytical accuracy and operational performance to align with business objectives and measure success effectively.
Recruitment strategies should focus on hiring data scientists and DevOps engineers simultaneously to foster better team chemistry, collaboration, and project velocity.

Read Full Article

20 Likes

HRKatha

140

Image Credit: HRKatha

LinkedIn lets go about 280 in California

LinkedIn has laid off about 280 employees across multiple cities in California.
This round of layoffs has impacted employees in various locations, including Mountain View, San Francisco, Sunnyvale, Carpinteria, and remote workers in California.
The job cuts affected software engineers, machine-learning specialists, devops and systems infrastructure employees, product managers, designers, and others.
Microsoft, the owner of LinkedIn, has been restructuring to streamline operations, which may result in around 6,000 job losses globally, with LinkedIn facing its biggest round of job cuts in over two years.

Read Full Article

8 Likes

Analyticsindiamag

341

Image Credit: Analyticsindiamag

Kubernetes Native llm-d Could Be a ‘Turning Point in Enterprise AI’ for Inferencing

Red Hat AI introduced llm-d, a Kubernetes-native distributed inference framework to address challenges in deploying AI models in production-ready environments.
Developed in collaboration with tech giants like Google Cloud, IBM Research, NVIDIA, and others, llm-d optimizes AI model serving in demanding environments with multiple GPUs.
llm-d's architecture includes techniques like Prefill and Decode Disaggregation and KV Cache Offloading to boost efficiency and reduce memory usage on GPUs.
With Kubernetes-powered clusters and controllers, llm-d achieved significantly faster response times and higher throughput compared to baselines in NVIDIA H100 clusters.
Google Cloud reported 2x improvements in time-to-first-token with llm-d for use cases like code completion, enhancing application responsiveness.
llm-d features AI-aware network routing, supports various hardware like NVIDIA, Google TPU, AMD, and Intel, and aids in efficient scaling of AI inference.
Industry experts believe llm-d by Red Hat could mark a turning point in Enterprise AI by enhancing production-grade serving patterns using Kubernetes and vLLM.
Companies focus on scaling AI inference solutions, with efforts from hardware providers like Cerebras, Groq, and SambaNova aiming to accelerate AI inference in data centers.
Recent research efforts have also been made in software frameworks and architectures to optimize AI inference, with advancements in reducing pre-fill compute and improving serving throughput.
A study by Huawei Cloud and Soochow University reviewed efficient LLM inference serving methods at the instance level and cluster level, addressing various optimization techniques.
vLLM introduced a 'Production Stack' for Kubernetes native deployment, focusing on distributed KV Cache sharing and intelligent autoscaling to reduce costs and improve response times.

Read Full Article

20 Likes

For uninterrupted reading, download the app

Devops News

Discover more