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Kubernetes Storage Options: Exploring NFS, Ceph, GlusterFS, and EBS

Kubernetes supports various storage systems to provide persistent storage for applications.
The four popular storage options in Kubernetes are NFS, Ceph, GlusterFS, and EBS.
NFS is suitable for shared storage, while Ceph offers scalability and fault tolerance.
GlusterFS provides distributed storage, and EBS integrates seamlessly with AWS.

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Managing Kubernetes Resources: A Comprehensive Guide to Create, Update, Delete, and Watch Operations

Kubernetes provides a rich API for managing cluster resources.
The core operations for managing resources are creating, updating, deleting, and watching.
Resources can be created declaratively using YAML/JSON files or imperatively using direct commands.
Best practices include using namespaces, monitoring resources, and automating resource management.

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Kubernetes Storage Classes and Dynamic Provisioning: A Guide to Automated Storage Management

Storage Classes and Dynamic Provisioning in Kubernetes enable efficient management of storage resources in a cluster.
A StorageClass is used to provision Persistent Volumes (PVs) dynamically and defines different classes or types of storage, based on performance, cost, and other factors.
Dynamic Provisioning allows automatic creation of PVs based on a Persistent Volume Claim (PVC) without manual intervention.
StorageClasses can be customized with specific parameters and define provisioning strategies and reclaim policies.
Different types of storage providers, such as AWS EBS, GCE Persistent Disk, and NFS, may require different parameters to be defined in the StorageClass.
The reclaim policy of a PV determines the volume's fate after the associated PVC gets deleted. The most common policies include Retain, Delete, and Recycle.
Best practices for Storage Classes and Dynamic Provisioning include setting a default StorageClass, defining multiple classes, monitoring storage usage, and setting appropriate reclaim policies.
By using StorageClasses and Dynamic Provisioning, administrators and users can effectively manage persistent storage in a Kubernetes cluster, improving its scalability and efficiency.

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Solarwinds

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More Service Desk Agents Aren’t the Answer

Increasing the ratio of service desk agents to employees has little effect on average incident resolution time.
Data shows the more incidents each agent handles, the faster they tend to fix them.
The cause for this could stem from Parkinson’s Law, which can slow work completion when more time is allotted for a task.
Protecting employees against burnout is a major concern for service desk managers.
Gallup’s State of the Global Workplace Report 2023 says burnout drains upwards of $8,900 billion in global GDP.
ITSM automation is a powerful tool against burnout by tackling repetitive tasks.
The report revealed automation can reduce average ticket resolution time by over three hours and cut missed service-level agreements (SLAs) in half.
Organizations utilizing fewer agents invested in KB articles to encourage independent problem-solving among staff.
Investing in refining processes may yield better results than simply hiring more agents for struggling ITSM workloads.
The modern service desk solution has AI features designed to maximize productivity and accelerate resolution times.

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How I Use My Own Product to Maintain Its Help Docs

The product manager for Apidog, a SaaS product, shares their experience of using their own product to maintain the help documentation.
Previously, they used a code-based tool called Docusaurus which was challenging and lacked support for multiple languages.
They decided to experiment with using Apidog directly to maintain the help documentation, leveraging its powerful API documentation features.
Through updates and new features, they successfully migrated the documentation, improved its style, and enhanced functionality.

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Enable EKS Auto Mode on an existing cluster

Amazon Elastic Kubernetes Service (Amazon EKS) Auto Mode provides a new capability streamlining Kubernetes cluster management for compute, storage, and networking.
The introduction of EKS Auto Mode represents a significant shift in how Kubernetes clusters are managed on AWS.
With EKS Auto Mode, much of the manual effort that previously fell on users is now automated, making it easier to run and scale Kubernetes workloads.
EKS Auto Mode automates critical infrastructure management tasks, freeing up resources for more important work.
The key benefits include reduced operational overhead, improved security, and cost optimization.
The service automatically selects the best-suited instances for the Kubernetes cluster based on the workload's needs, optimizing both performance and cost.
EKS Auto Mode also ensures that essential Kubernetes plug-ins are installed and updated automatically.
EKS Auto Mode automates upgrades and security patches, ensuring that clusters remain secure without manual intervention.
The Cluster IAM Role of the existing EKS Cluster must include sufficient permissions for EKS Auto Mode.
EKS Auto Mode significantly reduces the operational complexity of running Kubernetes clusters on AWS.

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Devopsonline

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How Smart Cities are Bridging the Digital Divide With AI

Urban areas are expected to house 68% of the world's population, putting significant strain on infrastructure and critical services.
There are still around 14.5mn people in the US alone without reliable broadband, often leaving rural or urban communities behind and exacerbating social and economic divides.
AI and machine learning is being used to help close the digital divide by identifying areas where broadband can be brought to underserved communities.
In order to use AI to create smart cities that are more technologically advanced and equitable, city planners must first focus on building a robust data foundation and establishing a strong data infrastructure.
To ensure that citizens from low-income or remote regions have equal access to healthcare, education, and other critical connected services, municipalities can also establish data-driven control rooms through integration of data from various departments.
It is essential to keep the needs and experiences of residents at the forefront when incorporating AI into city operations, which allows technology deployment to be guided by the actual needs of the community.
Scalable and adaptable AI solutions should be planned for smart city projects to include pilot projects and expand as the technology proves its value.
AI-powered scenario planning tools can help municipalities develop long-term resilience strategies to meet evolving urban dynamics without requiring complete overhauls.
The smart city journey must begin with data by keeping citizens at the center of the smart-city plan, identifying areas where AI can be applied for the greatest impact.
Leveraging AI to bridge the digital divide is a promising early step to advancing digital equity and creating smart city success.

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KeyMash, type your ways to feature with DevCycle feature flags

Teddy created KeyMash, a typing platform that turns typing practice into a fun game.
Users can challenge themselves, customize their experience, and enjoy the arcade vibe.
No account creation is needed to join, and the project utilizes DevCycle feature flags.
Using DevCycle was a smooth experience for Teddy, allowing for easy feature management and gradual feature launches.

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Docker and Kubernetes Integration: Building and Managing Containerized Applications at Scale

Docker and Kubernetes are two of the most popular technologies used in the world of containerization and orchestration.
Docker enables developers to package applications and their dependencies into containers, while Kubernetes automates the deployment, scaling, and management of containerized applications.
Docker and Kubernetes integrate to manage containerized applications effectively, making development, deployment, and scaling easier.
Kubernetes ensures complex applications work together seamlessly, and scale quickly and efficiently.
Developers use Docker to create container images, which Kubernetes deploys to Pods, ensuring the application is deployed across multiple servers.
Kubernetes automates the deployment of containers using the Deployment resource, ensuring the specified pods are always running and enabling seamless application updates.
Services in Kubernetes enable load balancing and service discovery, distributing traffic to Pods efficiently, regardless of where they are running in the cluster.
The combination of Docker and Kubernetes provides a very efficient solution for modern application deployment and management, allowing organizations to handle increased traffic and improve application reliability.
Using Docker containers, Kubernetes optimizes the use of resources, ensuring efficient deployment of applications in the cluster.
Together, Docker and Kubernetes streamline the development process, ensure consistency across environments, and allow organizations to efficiently manage containerized applications in production.

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Introduction to Kubernetes with Docker: Orchestrating Containers at Scale

Docker is an environment for automating the deployment, scaling and management of applications in lightweight, portable containers.
Kubernetes is an open-source container orchestration platform designed to automate the deployment, scaling, and management of containerized applications.
Docker and Kubernetes are often used together to build, deploy, and manage containerized applications to ensure high availability, reliability, and efficient resource utilization.
Docker handles the creation and execution of containers, whereas Kubernetes manages and orchestrates the deployment of these containers across multiple nodes.
Kubernetes ensures that the container is running and exposed to external traffic. Furthermore, Kubernetes manages the scaling, monitoring and networking of the containers.
Kubernetes provides a way to separate and manage different environments (e.g., development, testing, production) within a single Kubernetes cluster.
Kubernetes allows you to scale Docker containers horizontally by increasing or decreasing the number of container instances based on traffic.
Kubernetes excels at managing microservices, making it easy to deploy and scale, individual components of a microservices-based application..
Docker and Kubernetes together enable organizations to deploy highly available, scalable, and efficient applications in both development and production environments.
Docker and Kubernetes are powerful tools for both developers and operators.

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Docker on Kubernetes (K8s): A Powerful Combo for Scalable Containerized Applications

Docker and Kubernetes provide a powerful combination for running distributed applications at scale.
Kubernetes is an open-source platform that helps manage, scale, and deploy containerized applications.
Docker packages an application and its dependencies into a container image, and Kubernetes orchestrates these containers.
To run Docker containers on Kubernetes, you need to build a Docker image, push it to a container registry, create a Kubernetes deployment configuration file, and apply the deployment and the service.
Advantages of using Docker with Kubernetes include portability, scalability, automation, isolation, and declarative management.
Kubernetes is more powerful, scalable, and suitable for large-scale production environments than Docker Swarm.
Docker and Kubernetes together form a powerful toolset for managing containerized applications at scale.

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Itrevolution

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2024: A Year of Innovation and Transformation at IT Revolution

IT Revolution had a year marked by publications, evolving events, and continued commitment to helping technology leaders this year.
The rebranding of DevOps Enterprise Summit to the Enterprise Technology Leadership Summit reflects the broader scope IT Revolution community has developed over the past decade.
The Las Vegas event brought together senior technology leaders at the director, VP, and principal engineer levels as well as individual contributors.
IT Revolution released a graphic novel, transforming The Phoenix Project into a graphic novel to reach a broader audience.
In Flow Engineering: From Value Stream Mapping to Effective Action, Steve Pereira and Andrew Davis introduced a revolutionary approach to handling today’s business complexity.
Two more volumes of The Phoenix Project graphic novel are planned for 2025 by IT Revolution.
Stephen Fishman and Matt McLarty’s Unbundling the Enterprise: API’s, Optionality & the Science of Happy Accidents introduced the concept of finding “digital treasure” through the “science of happy accidents.
This year marked an important evolution in IT Revolution journal offerings by rebranding it to the Enterprise Technology Leadership Journal.
We’re significantly expanding our Immersion Courses, which offer in-depth learning experiences taught by IT Revolution authors and industry experts.
IT Revolution successfully launched ETLS Connect events program with our inaugural virtual session, featuring global watch parties in New York, Lisbon, Amsterdam, Cologne, Berlin, and Leeds.

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Dev

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Tips for CFOs to Unlock 30% Savings on Cloud Spend

Cloud spend often accounts for 10% of annual revenue, with up to 30% wasted due to inefficiencies.
CFOs can optimize cloud costs using Cloudgov.ai to unlock savings and reinvest them for growth.
Key strategies include understanding the financial impact, using Cloudgov.ai for optimization, and reinvesting the savings strategically.
Cloudgov.ai combines expert-driven insights, automation, and advanced analytics to achieve tangible results.

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Insightful Tips for Optimizing AWS Data Transfer Costs

Architect for Data Localization: Keep data within the same region or availability zone to avoid cross-region charges.
Use AWS PrivateLink and VPC Endpoints: Leverage VPC endpoints to route traffic within AWS at a lower cost than internet-based transfers.
Right-Size Data Transfer Paths: Consolidate resources within a single Availability Zone to avoid cross-AZ charges.
Leverage Data Compression: Compress data before transfer to reduce size and lower costs.

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Community Drawing 🎨

A developer created a unique daily drawing app for the DevCycle Feature Flag Challenge.
The app utilizes feature flags to control pen tool settings for all active users.
The developer configured a web socket API, implemented drawing features, and utilized DevCycle variables for stroke weight and color.
The app is live and the code is available on GitHub.

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