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The New Stack
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Securely Deploy and Run Multiple Tenants on Kubernetes
- Kubernetes is increasingly used to consolidate resources and workloads by organisations running multiple tenants within the same infrastructure.
- Organizations increasingly seek to consolidate workloads and resources by running multiple tenants within the same Kubernetes infrastructure.
- Practitioners have three primary options for deploying multiple tenants securely on Kubernetes:
- A namespace-based isolation approach that uses RBAC, network policies, and security controls.
- A cluster-level isolation approach that assigns a dedicated Kubernetes cluster to each tenant.
- Virtual clusters that provide tenant-specific control planes within a shared physical cluster.
- Failures to implement robust multitenancy strategy can lead to security breaches, resource contention, regulatory penalties and inefficiencies.
- Secure multitenancy in Kubernetes saves money with centralized management, but it introduces significant security and operational challenges that must be addressed through best practices.
- The implementation of robust security measures and isolation techniques is essential for maintaining a secure and efficient multitenant environment in Kubernetes.
- Multitenancy consolidates workloads and resources efficiently, but it introduces significant security and operational challenges that must be addressed to maintain a secure and efficient environment.
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Dev
1w
21
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Comparison: Terraform vs. CloudFormation
- Terraform and AWS CloudFormation are Infrastructure as Code (IaC) tools.
- Terraform is multi-cloud with support for AWS, Azure, GCP, etc., while CloudFormation is limited to AWS.
- Terraform uses HCL language, offers state management through a .tfstate file, and supports modular design.
- CloudFormation uses YAML or JSON, manages state internally, and has nested stacks for reusability.
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TechCrunch
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251
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OpenAI blames its massive ChatGPT outage on a ‘new telemetry service’
- OpenAI experienced a major outage on its AI-powered chatbot platform, ChatGPT, due to a 'new telemetry service' gone awry.
- The outage lasted for approximately three hours, affecting ChatGPT, Sora (video generator), and the developer-facing API.
- The problem was caused by the telemetry service, which unintentionally overwhelmed the Kubernetes control plane, impacting DNS resolution.
- OpenAI will implement measures to prevent similar incidents, including improved monitoring and access to Kubernetes API servers.
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The New Stack
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208
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Flexibility Matters When Setting Kubernetes Resource Limits
- When it comes to allocating CPU and memory resources to workloads in Kubernetes, there’s broad agreement on the importance of setting request values.
- The lack of controversy around setting requests is due to general awareness that workloads without a minimum amount of CPU are at risk of pod eviction.
- The internet is full of opinions, of course.
- Considerations for Setting CPU Limits
- While those are all logical reasons to not set CPU limits, doing so across the board doesn’t satisfy all use cases.
- Considerations for Setting Memory Limits
- If it doesn’t, you end up with an out-of-memory (OOM) kill — or worse, if you’re dealing with a memory leak, which can provoke failure across the entire node.
- To further complicate things, internal resource configurations in the JVM are tied to limits in Kubernetes.
- The reality is that there is no single axiomatic truth when it comes to setting Kubernetes limits.
- Automation informed both by engineering expertise and the real-time needs of an application is the only combination suited to address the challenges we’ve discussed.
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Dev
1w
68
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Resource Dependencies Lifecycle in Terraform day 7
- Resource dependencies and lifecycle management are critical concepts in cloud computing, DevOps, and infrastructure-as-code (IaC).
- Implicit dependencies occur when a resource's lifecycle depends on another resource without explicitly defining the dependency. Tools like Terraform automatically infer implicit dependencies based on resource configuration.
- Explicit dependencies are manually defined to ensure resources are created or destroyed in a specific order. This is useful when implicit dependencies are insufficient or more granular control is required.
- Lifecycle rules define how resources are managed during their creation, update, and destruction. These rules include retain policies, ignore changes, and replacement strategies.
- The create/destroy order ensures resources are created in the correct sequence and destroyed in the reverse order to maintain dependencies and system stability.
- Prevention of destruction involves protecting resources from accidental deletion using tools, policies, or configurations.
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Dev
1w
93
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Advanced VPC Networking: Detailed Breakdown
- AWS VPC allows creating a custom network environment with control over infrastructure. Communication in a VPC involves routing traffic between AWS resources, internet, and on-premises networks.
- An Internet Gateway (IGW) allows communication between instances within the VPC and the internet. Traffic flow for public subnets and incoming traffic from external users to the web server via IGW is explianed.
- NAT Gateway comes into play for private subnets that do not have direct access to the internet. The traffic flow via NAT Gateway explained with an example.
- Route Tables control how traffic flows within the VPC and between subnets. Route table example explained with two instances in different subnets.
- Security is crucial in a VPC setup. AWS provides Network Access Control Lists (NACLs) and Security Groups for controlling network traffic.
- NACLs filter traffic at subnet level based on explicit inbound and outbound rules. Example of denial of all inbound traffic on port 22 using NACL.
- Security Groups act as stateful firewalls for EC2 instances. Allow inbound traffic, and return traffic is automatically allowed. Examples of security group rules provided.
- NACLs is better for broad filtering across multiple instances, while Security Groups are associated directly with specific instances.
- Layered security approach combining NACLs and Security Groups is recommended. Examples of common security scenarios and common mistakes to avoid are also mentioned.
- The article concludes with a mermaid diagram illustrating how the communication flow works, from internet to the VPC and across subnets.
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Dev
1w
264
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Finding and Validating Unused Security Groups in AWS with Python and Boto3
- Unused security groups in AWS can accumulate over time and pose security risks or increase costs. This article describes how to use Python and Boto3 to identify and validate unused security groups and ensure that they are not being referenced by any other resources.
- The article walks through the code that identifies unused security groups in a given AWS region, validates them, and checks if they are referenced by any other groups.
- The code first retrieves all security groups and network interfaces in the specified region and creates a set of security groups that are in use.
- It then identifies and lists all the unused security groups in the region and checks if any other security group references it in their rules.
- The code helps to automate the process of finding unused security groups in AWS and ensure that unnecessary resources are removed. It can help reduce clutter, improve security posture, and potentially lower costs.
- The script can be further extended to add filters based on tags, VPCs, or other criteria to implement more advanced reporting or alerting when unused groups are detected.
- The article requires an AWS account, Boto3, and credentials configured either using the AWS CLI or directly using IAM roles or environment variables.
- The script can be executed by running the validate_unused_groups function.
- The script outputs whether the unused security group is referenced or not and whether it can be safely deleted.
- You can extend the script to integrate with AWS Lambda for automated, scheduled checks.
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Dev
1w
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Top Use Cases of Generative AI in DevOps for Enhanced Efficiency
- DevOps has changed software development by bringing teams together, automating tasks, and speeding up delivery, and adding Generative AI makes it even more effective by simplifying tasks and generating new ideas.
- Generative AI is an AI that creates new content, ideas, or solutions by learning from existing data, and it produces entirely new and original outputs. Generative AI uses advanced machine learning models like GANs or transformers.
- By integrating Generative AI into DevOps the teams can enjoy 6 key benefits like automating repetitive tasks, getting intelligent support, and more efficient problem-solving.
- Moreover, Generative AI is efficient in optimizing the allocation of computing resources in real-time, and it helps reduce operational costs by automating tasks like testing, bug-fixing, and deployment.
- Generative AI helps in better collaboration and enhances team performance by providing real-time insights, feedback, and automated reports, helping to get more accurate test cases, and reducing the risk of failures in the production process.
- Moreover, it helps in automated code and script generation, optimizing the continuous integration and continuous deployment, and intelligent infrastructure as code (IaC) management.
- Generative AI also helps in real-time log analysis, performance, and cost optimization, security and compliance automation, knowledge management, and documentation automation.
- By integrating Generative AI into DevOps workflows, organizations can stay ahead in the competitive landscape by enhancing operational efficiency and opening new avenues for creativity and problem-solving.
- According to McKinsey, organizations using AI in their DevOps pipelines can reduce deployment times by 30%, and companies that use AI for code generation and testing have seen a 50% reduction in bug-related issues. This helps improve software quality and lets teams release updates faster.
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Dev
1w
384
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Why Startup Founders Need Continuous Cost Awareness
- Cost awareness is crucial for startup founders to ensure sustainability and growth.
- Treat cost as a requirement from the beginning and design efficient systems.
- Measure and manage costs by tracking key metrics and investing in monitoring tools.
- Adopt a cost-conscious architecture and continuously optimize for savings.
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Dev
1w
409
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Day 11: Advanced Docker Compose
- Day 12 of Advent of Docker focuses on advanced features of Docker Compose.
- Environment files allow you to store and load environment variables for services.
- Healthchecks ensure that a service is healthy before starting another dependent service.
- Docker Compose Watch automatically syncs code changes into containers and triggers rebuilds.
- Profiles help manage different service combinations and can be used to separate development and production services.
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Dev
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Why an Accredited AI Certification Matters for Job Seekers in Tech
- Accredited AI certifications add more value to professional development efforts.
- Accreditation proves commitment to continuous learning in AI.
- Accreditation ensures critical assessment of course content and reduces risks.
- Accredited certifications enhance credibility and provide better job opportunities.
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Dev
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145
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Complete Guide to AWS Compute Rightsizing
- AWS Compute Rightsizing helps you align EC2 instances with your workload’s exact needs, reducing expenses and boosting efficiency.
- Understand your workload by identifying resource needs like CPU, memory, and storage.
- Analyze resource utilization patterns to uncover overprovisioned or underutilized resources.
- Choose the right instance type based on your workload's requirements and embrace automation with Cloudgov for advanced analytics and AI-driven recommendations.
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Dev
1w
247
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How To Add The Whitelist for Your Ingress-Nginx Controller of K8S?
- Access control is a critical aspect of securing Kubernetes applications.
- Whitelisting in Ingress-Nginx allows restricting access based on IP addresses or CIDR ranges.
- Steps to add a whitelist include verifying Ingress-Nginx installation, identifying IPs to whitelist, configuring the Ingress Nginx Controller, configuring the Ingress resource, applying the configuration, and testing the whitelist.
- Best practices for whitelisting include combining it with HTTPS, using ConfigMaps or Secrets for sensitive configurations, and regularly reviewing and updating the whitelist.
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Dev
1w
380
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Cloud Solutions vs. On-Premise Speech Recognition Systems
- Speech recognition technologies powered by machine learning algorithms are integrated into our applications to create virtual assistants, voice interfaces, automatic transcription, and translation systems.
- Developers must choose a technology for their projects from two options – local systems or cloud solutions.
- On-premise speech recognition systems are a good option for organizations requiring full control over their data and avoiding reliance on third-party services.
- Cloud-based speech recognition solutions are accessible and provide ready-to-use APIs with high-quality speech recognition models.
- Technical features of on-premise solutions include use of open-source solutions and customizable models, high performance, and data privacy and security.
- Limitations of on-premise solutions include high development and maintenance costs, limited scalability, and integration complexity with external systems.
- Technical features of cloud solutions include scalability, use of neural networks and machine learning, and fault tolerance.
- Limitations of cloud solutions include internet dependency, cost, and security and compliance issues.
- Hybrid solutions, combining local and cloud solutions for specific requirements, have become popular.
- Cloud solutions are best for high-traffic projects and scalability, while on-premise systems are suited for operations requiring data privacy, high performance, or operation in environments with limited internet access.
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Dev
1w
247
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EKS Auto Mode Unlocked for Existing Clusters with Terraform
- This article explains how to enable EKS Auto Mode on existing clusters and migrate workloads from EKS Managed Node Groups to EKS Auto nodes using Terraform code with zero downtime and application availability.
- The author suggests to use bootstrap_self_managed_addons = true while enabling EKS Auto Mode on an existing cluster to avoid an error where Terraform tries to recreate the cluster.
- The article explains how to verify the current state of an EKS cluster before and after enabling EKS Auto Mode.
- It also provides two ways (using eksctl or kubectl) to migrate existing workloads from EKS MNG to EKS Auto with minimal disruption while maintaining application availability throughout the migration.
- The bonus section of the article explains how to schedule pods always on EKS Auto nodes and control the deployment of workload in mixed-mode EKS clusters using nodeSelector Labels and nodeAffinity.
- The article suggests using EC2 instance type c6a.large for EKS Auto nodes, which can be customized using nodeClass and NodePool.
- The Terraform code for creating the EKS cluster with Managed node group and deploying workload yaml code using Terraform is provided on Github.
- Follow the author on Linkedin or Dev.to for timely updates and feel free to reach out to them in case of any errors while migrating existing workloads to EKS Auto Mode nodes.
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