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Azure DevOps Testing: How to Create Azure DevOps Test Plans

Azure DevOps is a tool that helps streamline the planning, development, testing, and delivery of software, ensuring software quality and reliability.
Azure Test Plans, an integrated test management module, enables teams to manage test plans, test suites, and test cases throughout the Software Development Life Cycle (SDLC).
Azure Test Plans streamlines testing workflows and ensures comprehensive test coverage along with a robust set of tools that helps teams manage testing activities effectively.
Azure Test Plans simplify test management with three core artifacts, Test Plan, Test Suite, and Test Case. Shared Steps and Shared Parameters lets you manage common test procedures and variable test data.
Creating test plans in Azure DevOps involves several steps, from defining test cases, assigning testers, and organizing test suites to ensure complete test coverage.
Teams can perform efficient execution of test cases, ensuring comprehensive test coverage, by running Azure DevOps testing in two ways: manual testing and automated testing.
Test results can be analyzed to assess application quality and identify areas for improvement. Test reports, progress reports, test analytics, and configuring permissions can be helpful in effectively handling permissions, test data, and test flakiness.
Integrating Azure DevOps Pipelines with cloud-based platforms helps improve test efficiency, enhance collaboration, and reduce time-to-market.
Azure DevOps Test Plans ensure effective testing and successful software releases by defining clear test objectives and scope, establishing comprehensive test environments, and integrating the test plan seamlessly with your Agile development process.
As part of a broader movement toward continuous improvement in testing practices, Azure Test Plans is positioned as a vital tool for teams aiming to enhance their software delivery processes, ensuring compliance with business requirements, and fostering a culture of collaboration and feedback across all stages of development.

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Dev

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How to Create a Windows Server in Microsoft Azure and Install IIS Server using a powershell

Microsoft Azure provides a platform for deploying Windows Server instances.
This article explains how to create a Windows Server in Azure and install IIS.
The steps include logging into Azure Portal, creating a VM, connecting to the server, and installing IIS using PowerShell.
Once installed, verify the installation and access the default IIS Welcome Page.

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Dev

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Atlas v0.31: pgvector support for your LLM-based apps, custom schema rules, and more

Custom rules can now be defined for database schema in Atlas v0.31.
Atlas provides native support for managing schemas for projects using the pgvector extension.
Drift detection is simplified to alert for any manual schema changes causing issues.
Multi-project ER Diagrams allow combining schema objects from multiple Atlas projects.

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Alvinashcraft

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Dew Drop – February 6, 2025 (#4356)

Docker Desktop 4.38 brings several new features including an AI agent to detect potential issues, multi-node Kubernetes support and the generally available (GA) release of Docker Bake. Meanwhile, AWS has added Fastlane support to AWS CodeBuild for macOS, a Docker-only container image for Ruby that works with the Continuous Integration/Continuous Deployment service. Okta customers can now use Universal Logout with Auth0, while JetBrains AI Assistant gains support for models via Amazon Bedrock.
Microsoft has released Windows MIDI Services Customer Preview 1 and Windows Terminal Preview 1.23. Additionally, the company has updated Microsoft.UI.Xaml to version 2.8.7, which includes a .NET 9 projection DLL that makes the component available for consumption by Universal Windows Apps that use .NET runtime.
Effortless form creation with Blazor Data Form is possible with the auto-generated fields feature. Control of web app titles in Microsoft Edge is now also possible, while a new .NET Rocks! podcast discusses AI extensions for .NET.
Elsewhere, Let's Encrypt is ending expiration notice emails, while new Go builds for Microsoft are available. Google has taken the wraps off of Gemini 2.0, which introduces Flash and Pro modes, offering developers a simpler way to write code. Also, Azure has launched an open rival to OpenAI's o1 'reasoning' model for under $50.
A new guide to DevOps Automation explores best practices, benefits and more, while on the database front, SQL Server migration is possible using a distributed availability group. Further afield, Mobile Bans in schools were found not to improve grades or behaviour.

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Dev

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Introducing Gridstore: Qdrant's Custom Key-Value Store

Qdrant built its custom storage engine called Gridstore, to address issues in RocksDB, which required compaction, handles generic keys, and took time to configure due to different options. Qdrant wanted better control over when and which data was written and flushed. It did not demand generic keys, and already has optimizers for online compaction. Full control over how data is stored is achieved by building its own storage expediting performance irrespective of memory status or any other issue.
Qdrant's custom storage engine, Gridstore is composed of three layers: The Data Layer, The Mask Layer, and The Gaps Layer. The Data Layer stores and retrieves variable-sized data quickly based on specific keys, and its array of pointers helps it to retrieve the data significantly faster than generic methods. While storing the data, Gridstore categorizes each data in fixed-sized blocks, and these blocks are further grouped into larger page files.
The Mask Layer helps handle updates and deletions without the need for expensive data compaction. Instead of maintaining complex metadata for each block, Gridstore tracks usage with a bitmask. The Gaps Layer helps optimize update handling. It provides a higher-level view of block availability, splitting the block into regions and keeping track of the largest contiguous free space. The layered approach ensures space optimization Gridstore does not waste space and data integrity is maintained.
Qdrant relies on a Write-Ahead Log (WAL) to avoid inconsistencies with data integrity. Qdrant ensures that when committing an operation, it has recorded it in the WAL, and if a crash happens before all updates are flushed, the system can safely replay operations from the log. Data is updated lazily; Gridstore prioritizes the most critical part and writes changes in a buffer, only finalizing updates when explicitly requested, ensuring data is not accidentally marked as deleted before being safely persisted.
To prove Gridstore's durability while handling heavy workloads, Qdrant built Crasher that tests Qdrant's data integrity limits by brutally killing and restarting it. Crasher continuously writes random data and then randomly crashes Qdrant, and Qdrant replays its Write-Ahead Log (WAL). If there is any data loss or a corrupt payload value, it is highlighted. Gridstore performs twice as fast as RocksDB and more reliably under heavy workloads and unexpected failures despite being slightly larger in size.
Gridstore API remains tightly integrated with Qdrant, and as such, has not been released as a standalone crate. Qdrant will consider publishing Gridstore as a contribution to the community in the future. Gridstore represents an advancement for managing key-value storage needs. Qdrant achieved faster, more reliable data ingestion while maintaining data integrity, making it a win for them.

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Devopsonline

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Shifting Perception: QA Beyond Testing

Quality Assurance (QA) engineering faces challenges from AI-driven automation, but its strategic value lies in factors like adaptability and business awareness.
To thrive, QA engineers should align their work with business goals, customer impact, and process enhancements.
Shifting perception of QA from a technical function to a business driver involves embracing customer-driven testing and proactive problem-solving.
QA can provide valuable customer insights by focusing on improving user experience and aligning with business KPIs like revenue, retention, and efficiency.
Quantifying QA's contributions to revenue, customer satisfaction, and operational efficiency helps demonstrate its strategic value to the company.
Proactive QA involves predicting and preventing issues by analyzing trends and advocating for continuous feedback loops.
Cross-department collaboration and leveraging AI technologies are crucial for QA's expanded role in enhancing efficiency and business impact.
QA professionals need to think like business strategists, align testing with company objectives, measure impact in business terms, and work collaboratively with AI.
The future of QA lies in its ability to focus on business outcomes, adapt to changes driven by AI, and integrate with company-wide goals for success.

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Dev

170

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HNG Internship: DevOps Stage 0 - NGINX Configuration and Optimization for High-Performance Web Servers

This article presents a case study of NGINX deployment on a freshly provisioned Ubuntu server on Amazon Web Services for the HNG DevOps Stage 0 challenge.
The steps involved in setting up NGINX on a fresh Ubuntu server are explained, including creating an instance on AWS, installing NGINX, and configuring the server to serve a custom web page.
The key takeaways from this project include provisioning an AWS EC2 instance, Linux command-line operations, installing and configuring a web server, and troubleshooting common server issues.
These skills are important for anyone working in DevOps, cloud computing, or web development.

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Dev

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Optimizing Call Routing Efficiency with Phone Number Carrier Lookup APIs

Efficient call routing is essential for businesses, SaaS platforms, and developers.
Phone number carrier lookup APIs provide accurate telecom data for optimizing call flows, reducing costs, and preventing fraud.
Benefits of carrier lookup APIs include optimized call routing, enhanced fraud prevention, cost reduction, and improved customer experience.
Integrating carrier lookup APIs streamlines call routing logic, prevents fraudulent activities, and enhances customer support systems.

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Dev

264

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DevSecOps: How to Integrate Security into Your DevOps Workflow

Adopt a Shift Left Strategy for Security. This means setting up automated security checks in code repositories and continuous integration pipelines.
Automate Your Security Testing by integrating security tools into your CI/CD pipelines for early vulnerability identification and resolution.
Prioritize IaC Security by applying security best practices to Infrastructure as Code (IaC) templates.
Enforce Access Control via Least Privilege by limiting access to important systems using RBAC and MFA.

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Dev

409

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Troubleshooting | Kubernetes

Pods stuck? - Use 'kubectl get pods -A' to check status and 'kubectl describe pod ' to dig into events. Also, check container logs using 'kubectl logs --previous'.
Nodes Not Ready? - Identify unhealthy nodes with 'kubectl get nodes', check node details with 'kubectl describe node ', and verify kubelet/docker service status by SSH into the node.
Service Not Routing Traffic? - Ensure endpoints match your service’s label selector using 'kubectl get endpoints' and confirm ClusterIP/NodePort is set correctly with 'kubectl get svc'.
DNS Issues? - Test cluster DNS resolution by running 'nslookup ' inside a pod.

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Dev

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Networking 101: Part 1

Networking is the practice of connecting computers and other devices so they can talk to each other, like a system of pipes and wires that carries data.
Networking is essential for DevOps and Cloud Engineers as it connects every computer, tool, and cloud service, allowing data to move and tools/processes to function properly.
In both traditional on-premises networks and modern cloud deployments, networking plays a crucial role in enabling communication and collaboration between resources.
Networking underpins DevOps tools and processes such as CI/CD pipelines and automation, as well as connectivity between cloud resources to ensure seamless data sharing and scalability.

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Dev

409

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From Code to Cloud: Building and Deploying a Smart Number Classification API with FastAPI

The Number Classification API is a FastAPI-powered web service that classifies numbers based on their mathematical properties and provides a fun fact.
It determines whether a number is prime, perfect, Armstrong, even, or odd and fetches an interesting fact from an external API.
The API is publicly accessible and deployed on Google Cloud Compute Engine.
It provides a structured JSON response and handles errors gracefully.

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Dev

256

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CREATING A WINDOWS VITUAL MACHINE IN AZURE

Step 1: Search for virtual machine on the azure search bar
Step 2: Create a New Virtual Machine by clicking on the + create drop down menu
Set up the Administration account and password, select the inbound port to HTTP and RDP
Install Windows features into the virtual machine using PowerShell command: "Install-WindowsFeature -name Web-Server -IncludeManagementTools"

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Dev

170

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Podman vs Docker: Should Your Business Care?

Podman is a container management tool gaining traction as an alternative to Docker, especially for security-focused enterprises.
Key differences between Podman and Docker include daemonless architecture, rootless execution, and increased security.
Podman follows Open Container Initiative (OCI) standards and is compatible with Docker images and registries.
Businesses seeking cost savings, improved reliability, and enhanced security should consider Podman as a long-term strategy.

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Siliconangle

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Red Hat’s trailblazer: Sally O’Malley on curiosity, code and collaboration

Sally O’Malley shares insights on Red Hat’s open-source AI innovations and the impact of collaboration on technology advancement.
O’Malley’s been central in expanding Red Hat’s open-source artificial intelligence solutions to a broader audience, which she also discussed with theCUBE, SiliconANGLE Media’s livestreaming studio, during KubeCon + CloudNativeCon NA.
During an exclusive interview with SiliconANGLE Media, O’Malley talked about her unique career trajectory, passion for open-source collaboration and how Red Hat’s open-source innovations are helping shape the future of AI.
O’Malley has made her mark at Red Hat by championing projects that embody the company’s commitment to open-source collaboration and technical excellence.
Her ability to translate upstream innovation into enterprise-ready solutions has driven the adoption of cloud-native technologies.
Building on her experience with containerization, O’Malley co-created Red Hat’s AI Lab Recipes, a modular toolkit that accelerates AI adoption by reducing development complexity.
Red Hat’s collaboration with IBM Corp. on InstructLab empowers organizations to develop AI models tailored to their specific needs by distilling large frontier models with domain-specific data.
Maintaining open-source collaboration is essential to ensuring AI technologies serve diverse societal needs, according to O’Malley.
Red Hat’s open-source success is built on a culture that values adaptability and long-term growth, allowing employees to evolve alongside the constantly shifting tech field, according to O’Malley.
Red Hat’s engagement with the open-source community has enabled it to stay ahead in the rapidly evolving AI ecosystem.

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