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How to Host Helm Charts on GitHub Container Registry

This tutorial shows how to host Helm charts using GitHub Container Registry (GHCR).
GHCR offers a free private registry feature, making it ideal for personal projects or small team collaborations.
The tutorial provides step-by-step instructions on publishing Helm charts to GHCR, using a minimalist example chart.
GHCR offers advantages such as free private registries, seamless GitHub integration, and OCI standard compatibility.

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Dev

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How to Use a GitHub Private Repository as a Helm Chart Repository

This article explains how to use a GitHub private repository as a Helm Chart repository.
Benefits of using GitHub as a Helm Chart repository include securely managing private Kubernetes manifests, easy sharing of Helm Charts within teams, and no additional infrastructure management required.
Prerequisites for setting up the repository include a GitHub private repository, a GitHub Personal Access Token, and Helm CLI installed.
The step-by-step guide covers packaging the chart, generating the index file, pushing to GitHub, configuring Helm, and verifying the setup.

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Dev

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Introduction to cloud data engineering with AWS

Cloud data engineering involves designing, building, and managing scalable data pipelines and infrastructure in the cloud, using AWS.
AWS provides a rich ecosystem of services that enable data engineers to build and manage data pipelines efficiently.
Some core AWS services used in cloud data engineering are S3, Glue, RDS, Redshift, Kinesis, Lambda, EMR, and Data Pipeline.
Benefits of data engineering in the cloud include scalability, cost efficiency, flexibility, managed services, security, and compliance.
Best practices for data engineers working with AWS include implementing Infrastructure as Code (IaC), optimizing ETL processes, monitoring and managing costs, automating data workflows, and securing data at all stages.
Cloud data engineering with AWS provides a powerful platform for managing data pipelines, processing large volumes of information, and enabling insightful analytics.
Whether it's batch processing with Amazon EMR, real-time streaming with Kinesis, or building a robust data lake with S3, AWS equips data engineers with the tools they need to succeed in the data-driven world.
As the field of data engineering continues to evolve, AWS remains at the forefront, providing the innovation and stability required to handle complex data challenges.
Whether you're a seasoned data engineer or just starting, AWS offers a comprehensive platform to explore, build, and optimize data solutions at scale.
Data engineering with AWS can be leveraged to build scalable, efficient data architectures that meet the challenges of a growing, data-driven business.

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Dev

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High Performance Notification System Practices

Building a high-performance notification system requires a unified system that can handle large volume message requests while ensuring system stability.
The notification system consists of several layers, including the configuration layer, interface layer, core service layer, common component layer, and storage layer.
The initial message sending design employs RPC service requests and MQ message consumption to handle message-sending requests, with idempotency designs implemented to prevent processing duplicate message content.
For message-failing due to bottlenecks or errors, messages are retried via distributed task scheduling frameworks, utilizing lock control that prevents duplicate processing across nodes.
Stability assurance of the notification system is done through two-layer degradation approaches that handle traffic spikes, resource isolation for problematic services, and third-party service protection.
Fault tolerance for middleware is essential to ensure service continuity while a robust monitoring system should be established to detect and mitigate issues before they escalate.
Active-active deployment across multiple data centers ensures service availability while elastic scaling, based on comprehensive service metrics, accommodates traffic variations while optimizing costs.
System design must address service architecture, functionality, and stability assurance comprehensively be tailored to specific business needs through thoughtful planning and iteration.

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Dev

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Learning linux basic

Linux is an open-source Unix-like operating system first released in 1991.
Learning Linux basic commands are crucial to becoming a good Linux user.
Basic Linux commands include sudo apt update, sudo apt install open-vm-tools -y, sudo reboot, and sudo su.
To navigate the file system, use pwd, ls, cd.., clear, history, and ls -l commands.
To perform file and directory operations, use touch, cp, mv, cat, rm, and rmdir commands.
To view or edit files, use less, more, and vim commands. To display system resources, use top, df -h, free -h, and grep commands.
To exit vim editor, press esc and type wq to save and quit.

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Medium

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Introduction to System Monitoring

Monitoring system health is crucial for ensuring system reliability and performance.
Switching from email notifications to real-time data collection and visualization improved monitoring approaches.
Metrics help visualize the system's behavior and identify symptoms of potential issues.
The Elastic Stack, including Beats, Elasticsearch, Kibana, and the alerting system, provides comprehensive monitoring capabilities.

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Siliconangle

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Observability provider LogicMonitor raises $800M at $2.4B valuation

Observability provider LogicMonitor has raised $800M in funding.
The funding round included participation from PSG, Golub Capital, and other backers.
The deal closed at a valuation of $2.4B, nearly $2B more than what Vista Equity Partners paid in 2018.
LogicMonitor plans to use the funding to enhance its observability platform and make acquisitions.

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Testim.io

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Testim Mobile: Elevating Mobile App Quality and Speed

Testim Mobile brings unparalleled test automation support for custom mobile apps.
Current testing approaches haven’t kept pace with modern mobile technology.
The latest update to Testim Mobile enhances the ability to automate testing for custom mobile apps.
Testim Mobile offers a complete mobile test automation solution with proven technology and unmatched stability.

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The New Stack

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Observe Simplifies K8s Troubleshooting With Kubernetes Explorer

Observe, Inc. has launched Kubernetes Explorer, intended to simplify visualising and troubleshooting for cloud native environments. The company’s observability platform's unique tool gives engineers, for the first time, a visual, unified interface for K8s.
Kubernetes Explorer unifies fragmented data across metrics, traces and logs to deliver contextual insights that span applications, the K8s platform, and cloud-native infrastructure. Connected with Observe’s AI Investigator, it can create custom, incident-specific visualisations, acting as a de-facto K8s assistant to support on-call engineers’ troubleshooting efforts.
The company’s agentic AI approach allows Kubernetes Explorer to provide engineers with actionable insights. This enables teams to identify, diagnose and rectify issues faster and more easily than before.
Traditional monitoring tools used by engineering and DevOps teams have failed to give a comprehensive view of application performance. However, the Kubernetes Explorer effectively brings together silo data across metrics, traces, and logs to provide full visibility into all K8s components, making it easier for teams to understand the interdependencies of components.
As a result, it becomes far more straightforward to detect, diagnose and resolve issues much more quickly, retrospectively even. The tool provides the agent with reason through, determine root causes, and offer resolution suggestions.
Observe's mission is to bring comprehensive observational solutions without hidden fees, and Kubernetes Explorer is now available free of charge to all Observe customers.
The fast-growing adoption of K8s and the complexity of K8s and cloud-native environments has significantly increased the need to cut through K8s' complexity. Observe’s tool is a leap forward in observability for cloud-native environments.
Traditional monitoring tools often struggle to integrate fragmented data types. However, Kubernetes Explorer can deliver historical visibility, resource descriptors to provide visibility to the total YAML configuration of K8s resources, and cluster optimisation to provide a visual map of workload distribution across the K8s cluster.
Kubernetes Explorer is expected to be a big win for engineering and DevOps teams as traditional monitoring tools fail to provide a comprehensive view of application performance.
According to Jeremy Burton, CEO of Observe, engineers need actionable insights unavailable with current observability offerings.

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Amazon

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Introducing a managed hook for Guard

AWS CloudFormation is introducing a new managed hook for Guard which seamlessly integrates compliance into users' AWS CloudFormation workflows.
AWS CloudFormation Hooks let users validate and enforce policies when provisioning and managing resources in CloudFormation stacks.
A Guard Hook is a service-managed CloudFormation Hook that integrates AWS CloudFormation Guard into a CloudFormation deployment process.
Guard Hooks can be used to enhance compliance and security, including security compliance, resource configuration, and operational governance.
With a Guard hook, you can now bring your AWS CloudFormation Guard rules directly into your CloudFormation stacks, change sets, and individual resources.
Guard Hooks provide fully managed cloud native experience that allows users to specify Guard rules using the Guard DSL and apply Guard-based compliance policies.
Guard Hooks enable users to take advantage of CloudFormation’s native stack-level and resource-level hooks to enforce policies across entire stacks and individual resources.
Getting started with a Guard hook involves configuring the hook, uploading Guard rules to Amazon S3, activating the hook, and enabling it to run against CloudFormation stacks.
Guard Hooks revolutionize the way users enforce compliance and security policies in their AWS infrastructure.
Guard Hooks simplify compliance enforcement, enhance security posture, streamline deployment workflows, and allow users to focus on innovation.

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Dev

296

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Introduction to AWS

Amazon Web Services (AWS) is the world’s leading cloud computing platform, offering a wide range of services to help businesses scale and innovate.
AWS is a comprehensive cloud computing platform provided by Amazon, offering on-demand resources such as compute power, storage, networking, and databases.
The core benefits of AWS include scalability, cost-effectiveness, global availability, security, and flexibility.
Some of the key AWS services are Amazon EC2, AWS Lambda, Amazon S3, Amazon RDS, Amazon VPC, Amazon SageMaker, AWS CodePipeline, and Amazon EKS.

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Medium

220

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Simplify AWS Learning with this Story-Driven Article

John wakes up to a beautiful morning in his small green village.
He gets ready for the day in his professional attire.
John's mom greets him and serves him breakfast.

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Dev

130

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Deploying AI Projects Through a Jenkins Pipeline

CI/CD automates the build, tests, and deployment of models, allowing large teams to collaborate and deploy seamlessly
Jenkins facilitates building, testing, and deploying AI projects and has a user interface to track pipeline runs visually
Steps to deploying AI projects through a Jenkins pipeline include prerequisites like a code hosting platform, KitOps, Jenkins, and a container registry
After installation, the KitOps can be verified by running the 'kit version' command on the local terminal
By logging in to the Jozu Hub, you can unpack any of the available ModelKits and grab a Qwen model from Jozu Hub
Create Jenkins credentials to securely enter your Jozu Hub username and password, and then build your pipeline by configuring the pipeline to grab the Jenkinsfile from the GitHub repository
It is very frustrating to deploy your AI projects manually every time you make a change, whereas Jenkins and KitOps automate the building, testing, and deployment of models and their dependencies in production
Jenkins and KitOps improve team collaboration, resulting in faster, more reliable deployments
You can automate deployment whenever a change is made using Jenkins and KitOps
Use KitOps to package models and manage dependencies for automating the deployment process of AI projects

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Solarwinds

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Discover New Paths with SolarWinds at AWS re:Invent 2024

AWS re:Invent 2024 kicks off on December 2 in Las Vegas, Nevada.
SolarWinds will be in attendance, offering demos and insights.
Product Management VP Josh Stageberg will have a session on December 3, discussing customer's cloud migration to AWS.
Visit booth #2159 to explore the benefits of leveraging SolarWinds and AWS for operational efficiency and digital transformation.

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Medium

116

My Little-Known Method to Learn Cloud! — (Blogging)

This blog outlines the exact method I use to learn Cloud.
The method combines theoretical study, hands-on projects, and more.
Understanding the topic is emphasized over memorization.
The structured plan helps with organization and motivation.

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