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Kubernetes Secrets: How to Create, Use, and Manage Secrets

Kubernetes Secrets are used to securely manage sensitive information like API keys, database credentials, or SSH keys.
They provide security by not exposing secrets in plaintext configurations or logs.
Secrets can be created using kubectl command, defined in a YAML file, or created from a file.
They can be accessed in pods and deployments as environment variables or mounted volumes.

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DevOps Automation Tools to Faster development Process

CI/CD pipelines are essential for incorporating automation into the software development lifecycle.
Recommended automation tools for CI/CD pipelines include Jenkins, Circle CI, AWS CodePipeline, and GitLab.
Jenkins is open-source and offers a rich plugin ecosystem with over a thousand plugins.
Circle CI is a cloud-based tool that is easy to set up, customizable, and has built-in Docker support.

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Building and Testing the Gemini API with CI/CD Pipeline

This technical blog explores the process of building, testing, and deploying a Gemini API using Flask and Docker, while also integrating a CI/CD pipeline using GitHub Actions.
By implementing a CI/CD pipeline, we can automate the build, testing, and deployment processes, ensuring a smoother and more reliable workflow for future updates to the application.
Testing is a critical aspect of ensuring that the Gemini API works correctly and reliably. For this project, we employed both unit tests and integration tests.
The core of the project is the Gemini API, a Flask-based web application that processes user input and generates responses using Google’s Gemini API.
CI/CD helps automate repetitive tasks such as building Docker images, running tests, and deploying the application.
The pipeline consists of three main jobs: Build Job, Test Job, and Deploy Job; and each job ensures that the Docker images are properly built, tested, and deployed to the correct environment.
Implementing CI/CD automation streamlines the software development lifecycle by eliminating manual, repetitive tasks, reducing the risk of human error, and ensuring consistency across the build, test, and deployment stages.
Managing sensitive data, like the GEMINI_API_KEY, posed a challenge as we needed to ensure its security while still making it accessible to the CI/CD pipeline. To address this, we utilized GitHub’s Secrets feature.
To solve the challenge of maintaining consistency across test environments, we leveraged Docker containers, ensuring that both the application and tests ran in an isolated, consistent environment.
CI/CD has proven to be an essential component of modern software development. It empowers teams to deliver high-quality features more quickly, with fewer bugs, and with increased reliability.

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Automate Pull Request Sharing from GitHub to Slack

Automate Pull Request Sharing from GitHub to Slack
prbot is a tool built to automate Pull Request sharing from GitHub to Slack
Features include automatic Slack message, synced Pull Request status and reviews status, multiple channel support, customizable message format, and advanced settings
Upcoming features include reminders, codeowners support, Microsoft Teams support, etc.

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Simplifying multicloud performance with new cloud-native technologies

Recent advancements in new cloud-native technologies are transforming how Kubernetes applications are deployed and managed.
These cloud-native technologies simplify multicloud performance while also enabling organizations to more easily navigate complex ecosystems and foster open-source, community-driven collaboration.
Enterprises are moving beyond simply adopting multiple clouds. A new wave of multicloud strategies, such as Kubernetes technology, allows organizations to leverage the best features of different cloud platforms with services that can be moved to the edge while minimizing risks like vendor lock-in.
As organizations explore how to integrate new cloud-native technologies, challenges persist around managing complexity.
This challenge is creating the need for tools to help developers navigate the ecosystem, which is why Akamai recently announced its application platform.
The Akamai app platform simplifies Kubernetes deployment and integration with other open-source tools, according to Jenkins.
Akamai is expanding its vision of distributed cloud. Recognizing the need for support from the open-source community, the company has increased its support for the open-source ecosystem by pledging $1 million to Cloud Native Computing Foundation projects.
As organizations scale their workloads and integrate AI-driven models, sustainability has become a critical focus, according to Jenkins.
Sustainability initiatives often overlap with cost-saving measures, making this dual objective an impactful goal for enterprises.
By enabling latency-sensitive applications to run closer to users or selecting locations based on carbon emissions, organizations can balance environmental and financial priorities.

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Build a Simple AWS CI/CD Pipeline with deployment to Elastic Beanstalk

In this project, the author guides readers through building an AWS native pipeline that builds code and hosts it directly on AWS. The architecture uses Bitbucket for source control and CodeBuild, S3, Elastic Beanstalk, and CodeArtifact to automate the pipeline elements. The pipeline includes triggers, tests, and security accommodations. The end product is free of servers running and only charged for the build time. The author includes detailed steps and configurations for each of the pipeline components.
The pipeline fetches code, runs a quality gate on Sonar Cloud, launches the build phase, stores the artifact in an S3 bucket, and updates Elastic Beanstalk with the artifact. A test phase with a DB test user then tests the deployed software with the results’s screenshots stored in another S3 bucket.
The tutorial covers how to host code in Bitbucket, configure CodeArtifact, create code quality jobs on Sonar Cloud, build artifact jobs, and create a release pipeline. It also covers how to create an RDS database, initialize the database, and add parameters to the parameter store.
The AWS pipeline created includes an email notifications system and a code analysis stage on Sonar Cloud.
The pipeline building and automation guide ensures cost considerations. Access control policies ensure pipeline security and include custom IAM roles and permission policies.
A single Elastic Beanstalk environment is created with the help of Beanstalk roles, security groups, and EC2 key pairs, to prompt beanstalk updates of detected changes in the branch via the pipeline.
After the pipeline build success, the article concludes with demonstration screenshots of a working app connected to the Elastic Beanstalk environment and to RDS, making live a fully configured cicd pipeline.

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Mastering Ingress with AWS Load Balancer Controller: Expose Your Applications Like a Pro!

This guide explains how to deploy a robust NGINX application on an Amazon Elastic Kubernetes Service (EKS) cluster.
The guide incorporates the AWS Load Balancer Controller (ALB Controller) to efficiently manage ALB resources in Kubernetes.
By following this guide, you'll be able to expose your application to the internet with a custom domain and SSL/TLS security.
The guide also covers steps to configure DNS in Route 53 to map your custom domain to your application.

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The New Stack

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Kubernetes + LLMs: Cast AI Solves the Cost Puzzle

Cast AI has launched an AI Optimizer service that automatically cuts the cost of deploying large language models (LLMs) by integrating with any OpenAI-compatible API endpoint.
Cast AI has also launched AI Enabler, which leverages the vendor’s Kubernetes infrastructure optimisation capabilities to intelligently route queries from organisations and DevOps to the most cost-efficient LLM.
DevOps teams can compare LLMs side-by-side for performance and cost with the Cast AI Playground, another tool from the provider.
Cast AI is helping developers manage the costs of AI operations with its AI Optimizer and now AI Enabler.
The costs of running LLMs can grow quickly, with a single instance of an LLM hosting reaching upwards of $20,000 per month.
MLOps teams responsible for building and maintaining the infrastructure for generative AI workloads struggle to determine the best model for their specific needs.
Cast AI’s various tools help developers get their arms around the growing number of LLMs and the costs to run them.
AI Enabler compares LLMs and creates benchmarks, helping developers to optimise performance and cost.
Cast AI has launched Commercially Supported Container Live Migration, which enables automatic and uninterrupted migration of stateful and uninterruptible workloads in Kubernetes.
Cast AI is integrating the Container Live Migration feature with its other automation tools, including Bin-Packing and Eviction, Cluster and Node Rebalancing, Spot Fallback, Spot Interruption ML Prediction, and Spot Instance Price Drift Rebalancing.

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A KeyVault for the Power Platform

The deployment of Azure Key Vault for a Power Apps project can be challenging due to firewall restrictions.
The author discusses how to create a Key Vault in Azure with a firewall for a Power Apps project.
The article describes the creation of a virtual network with a subnet and network security group.
The main resources required include a Key Vault (KV), a Virtual Network (VNet), a Subnet (subnet), and a Network Security Group (NSG).
Creating these resources can be confusing for a non-network specialist as they have dependencies on one another.
The article describes the creation of network security groups, virtual networks, and subnets using Azure documentation and Bicep templates.
The network security group sets up the appropriate settings for allowing traffic to and from the Key Vault.
Virtual networks with IP ranges and subnets must also be created.
This article provides essential information on the creation of various resources such as a Key Vault, Virtual Network, and Network Security Group.
It also shares a pipeline that can be used in Azure DevOps for automating the deployment of resources with condition statements.

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AWS Services in DevOps

AWS offers a suite of tools for seamless DevOps practices.
Key AWS services include Infrastructure as Code (IaC), Monitoring and Logging, Continuous Integration / Continuous Deployment (CI/CD), Configuration Management, and Security and Compliance.
Infrastructure as Code (IaC) enables automated provisioning and management of infrastructure.
AWS provides tools for monitoring, logging, CI/CD pipelines, configuration management, and security & compliance in DevOps.

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The New Stack

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Why Red Hat Had To Improve the Edge Developer Experience

Red Hat has released a new version of Red Hat Device Edge, which is designed to address the specific needs of clients using Kubernetes across various edge deployments, including telcos leveraging the technology for 5G and industrial applications.
By using Red Hat Device Edge and running Kubernetes at the Edge, Red Hat provides a solution to reduce latency and provide features valued by operations teams for the underlying infrastructure while enhancing the developer experience.
Red Hat Device Edge combines RHEL with MicroShift, which acts as a Kubernetes-based solution derived from OpenShift, designed for minimal hardware typically found in edge environments, retaining many benefits associated with OpenShift, according to Shobhan Lakkapragada, Red Hat’s senior director of product management.
Red Hat Device Edge 4.17 during KubeCon+CloudNativeCon includes enhanced support for low-latency workloads and improvements to edge AI workload support.
Red Hat’s OpenShift AI enables the creation and deployment of AI-enabled applications across hybrid cloud environments.
OpenShift AI supports both predictive AI and generative AI use cases, providing a broad set of functionalities across the AI lifecycle.
Looking ahead, future releases of OpenShift AI would integrate additional AI features into the product, enhancing productivity through AI-driven solutions.
Red Hat Developer Hub provides templates and resources for organizations to begin their AI-assisted journey for developing edge applications or apps for other types of deployments on Kubernetes and containers.
Red Hat Developer Hub eliminates the need for developers to spend time figuring out how to use the assets or searching for documentation by providing a centralized catalog for AI assets and significantly enhances productivity through AI-driven solutions.
Future releases will incorporate additional AI features into the product, further enhancing productivity through AI-driven solutions.

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Proactive Downtime Prevention: AWS Site Monitoring Strategies

Proactive Downtime Prevention: AWS Site Monitoring Strategies provides a comprehensive approach to proactive system health management.
Key takeaways include the strategic use of AWS native tools, third-party solutions, redundancy and failover mechanisms, balancing monitoring needs with cost-effectiveness, and advanced monitoring techniques.
Core elements of AWS monitoring include Amazon CloudWatch, AWS Config, AWS CloudTrail, AWS X-Ray, and AWS Distro for OpenTelemetry (ADOT). Observability is achieved through the collection and analysis of data, including metrics, logs, and traces.
Organizations can employ various proactive monitoring techniques to predict and prevent future problems by analyzing trends with synthetic monitoring and predictive analytics.
AWS provides native tools for real-time insights into infrastructure performance and health, including CloudWatch, Trusted Advisor, and Cost Explorer.
Integrating third-party solutions offers additional layers of observability and specialized functionalities to enhance AWS monitoring efforts.
Implementing redundancy, failover mechanisms, and regular testing can minimize AWS downtime.
AWS offers automated incident response capabilities through services such as AWS Config, Security Hub, GuardDuty, and Lambda.
AWS cost management tools like Cost Explorer and Budgets facilitate optimizing cost-effectiveness.
Customizing monitoring for microservices and serverless architectures using APM tools is essential for achieving comprehensive coverage of AWS environments.

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Navigating AWS Service Contracts: Key Points for Businesses

Understanding the intricacies of AWS Enterprise Agreements and how to optimize costs, customize contracts, manage risks, and plan for the future is crucial for companies looking to leverage the cloud to its fullest potential.
To access the benefits of the AWS Enterprise Discount Program (EDP), businesses must meet certain eligibility criteria.
Accurately forecasting AWS cloud requirements is essential for businesses to ensure they are not overcommitting or underutilizing resources.
Consolidating your organization's cloud spend into a single budget, you create a clearer picture of your total AWS investment.
To secure better discounts from AWS, strategic planning is essential.
When entering negotiations with AWS, it's crucial to focus on operational flexibility to ensure that the service contract aligns with your business's evolving needs.
Customizing your AWS service contract is essential to align with your company's unique requirements.
Mitigating the risks associated with long-term commitments is crucial to maintain operational agility.
To facilitate navigating AWS service contracts, businesses should establish a point of contact within AWS for consistent communication and seek advice on cost optimization.
Ultimately, the key to successful AWS contract navigation lies in thorough preparation, clear understanding of business requirements, and effective negotiation tactics.

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Creating Azure DevTest Labs!

Azure DevTest Labs simplifies the process of managing development and testing environments.
It provides an efficient, cost-effective solution by leveraging virtual machines and automating infrastructure setups.
Teams can speed up their development cycles with DevTest Labs.
It offers features like cost management, automated deployments, and integration with other Azure services.

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AWS Cloud Development Kit (CDK) vs. Terraform

AWS Cloud Development Kit (CDK) and HashiCorp Terraform are two different ways to implement Infrastructure as Code (IaC).
Terraform is a cloud-agnostic, declarative provisioning tool that works across multiple cloud platforms while CDK is an open-source framework that utilizes general-purpose programming languages to define cloud infrastructure, native of AWS services.
Declarative and Imperative types of IaC are present, where Terraform is a declarative tool and CDK is an imperative approach.
CDK offers programmatic code, constructs and imperative control. Also, it is compatible with CloudFormation, provides built-in validation, and helps in automatic checks.
On the other hand, Terraform supports modular infrastructure, provider ecosystem, has a state management tool, and uses HashiCorp Configuration Language.
When it comes to choosing between these two tools, factors such as existing technical expertise, infrastructure footprint, developer experience, and use-case must be considered.
AWS CDK works best for AWS-focused, developer-driven environments, prioritizing code reuse and flexibility, while Terraform excels when multi-cloud and compatibility across other systems are important.
Spacelift is a platform that provides IaC orchestration and speeds up infrastructure deployment without compromising control.
It provides policies, multi-IaC workflows, self-service infrastructure, integrations with third-party tools, creature comforts such as contexts, and drift detection with optional remediation.
Spacelift enhances collaboration, allows engineers to implement guardrails, and provides a central location to make infrastructure-related decisions.

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