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Managing Kiali Instance Finalizers During Helm Chart Uninstallation in Kiali-Operator

When deploying the Kiali-operator using the Helm chart from Kiali's GitHub repository, there is an issue during uninstallation where the Kiali instance's finalizer prevents its deletion.
Two options to resolve this issue are: manually managing the Kiali instance outside of the Helm chart, or adding a Helm hook job to automate the deletion of the Kiali instance.
The Helm hook job can be defined in the Helm chart to patch the Kiali instance and remove the finalizer, ensuring complete removal of the operator.
This post explores the challenges and provides solutions for managing Kiali instance finalizers during the Helm chart's uninstallation process.

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Dev

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How Kubernetes Simplifies Cloud Application Deployment, Scaling, and Management

Kubernetes simplifies the deployment, scaling, and management of containerized applications in cloud environments.
It automates tasks like infrastructure management, allowing developers to focus on application code.
Kubernetes facilitates consistent application running across different environments, irrespective of the underlying hardware.
Automating application deployment is a key feature of Kubernetes, utilizing declarative configuration files to define the application state.
Kubernetes scales applications automatically, offering horizontal and vertical scaling mechanisms based on metrics like CPU usage.
Its Horizontal Pod Autoscaler adjusts pod replicas in real-time, optimizing resource usage.
Kubernetes supports Cluster Autoscaling to automatically scale the underlying infrastructure based on resource demands.
The platform ensures self-healing and fault tolerance by monitoring service health and taking corrective actions.
Service discovery is simplified with Kubernetes providing DNS-based internal service discovery.
Kubernetes offers load balancing to distribute traffic across healthy instances for high availability and resource efficiency.

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Dev

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[ Solution Library AWS ] Live Chat Content AI on AWS

The Live Chat Content Moderation with generative AI on AWS project aims to create a scalable, real-time chat moderation system for live chat platforms using AWS services and generative AI.
The architecture involves services like Amazon API Gateway, AWS Lambda, Amazon DynamoDB, Amazon SQS, AWS AppSync, Amazon Bedrock, among others, ensuring real-time moderation using generative AI and multiple AI model support.
AWS services costs vary based on usage, such as Amazon API Gateway, AWS Lambda, Amazon DynamoDB, etc., and a monthly cost estimate example is detailed for various services.
The system supports four AI models for chat moderation, each with different capabilities and strengths, allowing users to switch between these models.
Prerequisites for deployment include an AWS Account, AWS CLI, Node.js, Git, and jq installed, with steps outlined for deployment, deployment validation, and usage after installation.
Deployment validation involves checking the status of various resources like CloudFormation stack, DynamoDB tables, Lambda function, SQS queues, AppSync API, and CloudFront distribution to confirm a successful deployment.
Monitoring and observability through CloudWatch dashboard, performance considerations, security measures like encryption at rest and least-privilege IAM roles, and cleanup steps are highlighted for efficient system management.
Next steps for system enhancement include customizing AI prompts, implementing user authentication, and enhancing DDoS attack protection using AWS Shield Advanced.
Readiness notices emphasize the customer's independent assessment responsibility, and the authors of the project are Gabriel Costa and Juliano Baeta.

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Dev

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DevOps with AWS CodePipeline

Integrate GitLab/Github repositories with AWS CodePipeline to automate the CI/CD process.
The integration allows for automatic triggering of pipeline actions when code changes are pushed to GitLab.
The setup provides benefits of automation and scalability for DevOps workflows.
It allows developers to focus on writing code while automation handles building and deploying applications.

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Dev

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Azure Logic Apps & AWS Step Functions

Azure Logic Apps and AWS Step Functions offer graphical approaches to deploying workflows, suitable for teams lacking programming expertise or seeking simplicity in workflow implementation.
These tools aim to reduce complexity and allow engineers to create complex workflows without extensive coding, serving as No-Code solutions.
Logic Apps and Step Functions help visualize workflows with cause-and-effect relationships, simplifying the process and directing actions from start to completion.
AWS Step Functions provide a straightforward way to set up no-code solutions, guiding users through template selection and customization.
Within Step Functions, tasks can be edited and configured according to requirements, with options for exporting configurations and generating diagrams.
In contrast, Azure Logic Apps, while offering similar functionalities, caters to specific actions like those in Buffer, a social media service.
AWS Step Functions are considered more developer-friendly, while Azure Logic Apps are perceived as more suitable for specific actions outside engineering.
Overall, AWS Step Functions are perceived as better suited for engineers looking for a simpler way to handle workloads, while Azure Logic Apps are more tailored to non-engineering tasks.

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AI Agents For Cloud & DevOps Engineers: RAG Operations

AI is not taking away jobs in engineering fields like cloud and DevOps; instead, it can create new job opportunities.
AI Agents are specialized in performing specific tasks effectively, such as writing code or generating responses.
RAG (Retrieval Augmented System) enhances a Large Language Model by providing access to real-time external data sources.
Fine-tuning a model involves retraining it on specific data sets, while RAG allows feeding external information to the model.
Weights in AI models determine the importance of words and ideas in responses, affecting the output.
Using base models like GPT and implementing RAG operations with tools like Serper and WebsiteSearchTool can optimize task performance.
Local model deployment with Ollama and leveraging the langchain_openai library enable using AI models locally for specific tasks.
Creating RAGs involves specifying websites for information retrieval and using them in AI Agent tools for enhanced functionality.
Code generation with AI Agents involves setting allow_code_execution to create and execute code for specific tasks like deploying clusters.
DevOps professionals can enhance their skills by learning to create and deploy AI Agents for performing engineering tasks effectively.

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Microsoft

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Markdown for large text fields (private preview)

Microsoft Azure DevOps is introducing a private preview for Markdown support in large text fields.
Users now have the option to opt-in and use the Markdown editor for individual work items and fields.
The conversion from HTML to Markdown is performed on a best-effort basis, but complex HTML may not be accurately converted.
The private preview is only available in New Boards and interested organizations can sign up to join the preview.

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Dev

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5 Tricks to Secure your Docker Images

Regularly scan Docker images for vulnerabilities using tools like Docker Scout to catch issues early.
Improve security by using minimal images, reducing unnecessary components and limiting potential vulnerabilities.
Avoid hardcoding credentials in Dockerfiles, use environment variables or secret management tools instead.
Run Docker containers as non-root users to limit the impact of a compromise.

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Dev

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Phase 3: Securing Packages with AWS CodeArtifact

Phase 3 of the Seven Days DevOps Challenge focuses on implementing AWS CodeArtifact for package management.
Key learnings include package management, repository domains, upstream repositories, and access control for packages.
The key tools and concepts used are AWS CodeArtifact, Maven, AWS IAM, and upstream repositories.
Steps involved creating a CodeArtifact repository, reviewing the package flow, connecting to the repository, connecting to the EC2 instance, creating the settings.xml file, and compiling the project with Maven.

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The New Stack

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One Mighty kro; One Giant Leap for Kubernetes Resource Orchestration

kro is an open source project that extends Kubernetes core capabilities to simplify the management of interdependent Kubernetes resources.
AWS open sourced kro at KubeCon North America in November 2024, and later Google Cloud Platform (GCP) and Microsoft Azure joined the project.
kro simplifies resource orchestration by providing a configuration-based framework, eliminating the need for custom code.
Features like Simple Schema, CEL-based expressions, and dependency management make kro intuitive and easy to adapt.
kro works with any Kubernetes resource, interacts exclusively with the Kubernetes API, and can help manage cloud resources efficiently.
By using kro, organizations can turn Kubernetes into a centralized cloud platform, standardizing resource management across different teams.
kro increases developer velocity, improves fleet management, and simplifies data and MLOps infrastructure management.
The collaboration among AWS, GCP, and Azure through kro sets the stage for cross-cloud standardization in Kubernetes resource management.
Explore kro in your development environment, connect with the GitHub community, and contribute to shaping the future of cloud native development.
Join the transformative journey of Kubernetes resource orchestration at KubeCon + CloudNativeCon Europe in London or meet with the AWS team at the event.

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Kubectl Demystified: Mastering the `kubectl apply` Command

kubectl apply is a declarative management command used to create or update Kubernetes resources based on configuration files.
When you run kubectl apply, Kubernetes performs a three-way merge to calculate differences between the current state, last applied configuration, and the new configuration.
The command can be used for initial deployment, updating resources, GitOps/CI/CD pipelines, and managing multiple resources.
Proper usage of kubectl apply is essential for Kubernetes administrators to ensure consistent and repeatable deployments.

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Amazon

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Watch the recordings from AWS Developer Day 2025

The AWS Developer Day 2025 showcased the transformative power of generative AI for software development.
Developers learned how AWS is empowering them to embrace this evolution through their generative AI developer tools, making it faster and easier to build and scale quality software in the cloud.
The event highlighted the exciting possibilities that generative AI offers for the future of software development.
AWS is committed to equipping developers with the tools and insights they need to thrive in this changing landscape.

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Dev

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Understanding the CAP Theorem: Choosing Your Battles in Distributed Systems

The CAP theorem, formulated by Eric Brewer in 2000, states that in a distributed system, you can achieve only two out of the following three guarantees: Consistency, Availability, or Partition Tolerance.
CP (Consistency + Partition Tolerance) sacrifices availability, while AP (Availability + Partition Tolerance) sacrifices strict consistency.
A CA (Consistency + Availability) system is not practically possible in a distributed environment.
The PACELC theorem extends CAP to capture performance considerations and indicates the tradeoff between Availability/Consistency during a partition and Latency/Consistency under normal conditions.

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What is Infrastructure Monitoring? From Basics to Implementation

Infrastructure monitoring involves real-time observation and analysis of IT components for optimal performance.
It focuses on hardware, virtualization, network, storage, and cloud resources.
Monitoring aims to proactively detect issues, optimize performance, plan capacity, save costs, and enhance security.
It operates through data collection, analysis, visualization, and alerting processes.
Key benefits include preventing downtime, optimizing resource utilization, and enabling faster troubleshooting.
Infrastructure monitoring tools cater to server, cloud, network, data center, virtualization environments, and more.
Best practices involve focusing on critical metrics, setting meaningful alerts, proper visualization, and regular adjustments.
Integration with other monitoring systems and choosing the right tool are essential for effective monitoring.
Considerations for different environments and scalability factors impact tool selection.
ROI of implementing robust monitoring includes reduced downtime, lower costs, better utilization, and extended hardware lifespans.
Infrastructure monitoring must evolve to address containerized environments with dynamic monitoring and service-level metrics.

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Dev

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Go's Building Blocks: Syntax, Variables & Types Demystified 🧩

Go, a language created in 2009 by Google engineers, offers a blend of C's performance and Python's readability.
The language's syntax is minimal, allowing for quick learning and readability without excessive complexity.
Functions in Go can return multiple values, reducing the need for additional code for value handling.
Variables in Go can be declared explicitly or inferred, with automatic assignment of zero values to uninitialized variables.
Constants in Go are immutable and declared using the 'const' keyword.
Go's type system enforces strict rules but ensures clarity once understood.
Basic types in Go include integers, floats, strings, and booleans, with strings being immutable UTF-8 encoded sequences.
Composite types like slices, maps, and structs are used for more complex data structures in Go.
Go's simplicity, performance, and developer productivity make it suitable for various applications, from CLI tools to web servers.
While lacking classes and inheritance, Go employs structs and interfaces for simpler and more maintainable code.

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