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What Big Tech Companies Can Teach Us About Incident Management

Incident management is the process of identifying, analyzing, and resolving incidents that impact an organization's operations, services, or systems.
Best practices for incident management include having an incident response plan, establishing communication channels, prioritizing incidents, monitoring incidents, and conducting post-incident reviews.
Big tech companies like Amazon, Google, and Microsoft have robust incident management processes in place.
These companies invest in preparing for the worst, automating incident response, prioritizing communication, conducting post-incident reviews, and having dedicated incident management teams.

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Setting Up an Automated Java Build and Deployment Pipeline with AWS CodeArtifact

This guide explains how to set up an automated Java build and deployment pipeline with AWS CodeArtifact.
The process involves setting up an EC2 instance, installing Java and Maven, cloning and configuring the Java project, setting up AWS CodeArtifact, configuring IAM permissions, generating an authorization token, configuring Maven to use CodeArtifact, and finally, publishing the package to CodeArtifact.
By following this setup, developers can ensure a robust and reusable package management process in a cloud-native CI/CD pipeline.
The integration of AWS CodeArtifact enables secure and scalable software delivery.

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My Experience with OpenStack Ironic

The author has been accepted into the MLH fellowship program for the third time.
The author is working on OpenStack/Ironic under the guidance of the G-Research Open Source Team.
OpenStack is an open-source cloud computing platform for managing compute, storage, and networking resources.
Ironic is an OpenStack component for managing bare metal infrastructure, providing flexibility and automation for hardware deployments.

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Dev

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Azure Container Apps: Technical Insights from Production Deployments

Scaling Architecture: Production scaling patterns reveal behavior and adaptation in real workloads.
Network Implementation: Network patterns exhibit dynamic behavior and effective adaptation capabilities.
State Management Patterns: State handling in production demonstrates practical and efficient approaches.
Deployment Architecture: Deployment patterns showcase sophisticated orchestration and efficient processes.

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Hackernoon

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Crafting Multi-Cloud Systems: How Tech Experts Are Solving Connectivity

Multi-cloud strategies offer a solution to optimize performance across regions, but they come with challenges of inconsistent architecture, security models, and service offerings.
Arun Pandiyan Perumal, an expert in multi-cloud strategies and complexities, emphasizes integrating automation, security, and reliability checks to design comprehensive and scalable systems.
Site Reliability Engineering principles standardize automation workflows, reduce downtime, and ensure application availability across multiple cloud providers.
Perumal believes that AI-driven cloud infrastructure management can lead to self-healing systems, resulting in enhanced performance, security, and cost efficiency.

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Latitude Cuts DB Costs by $12K/Month While Scaling Real-Time AI Gaming 🎮

AI gaming company Latitude migrated from Amazon Aurora to TimescaleDB for their interactive storytelling platform, saving $12K monthly in storage costs while improving query performance.
TimescaleDB is an open-source database built on PostgreSQL, optimized for time-series data and real-time analytics.
Latitude faced challenges with rising storage costs, degrading query performance, challenges with real-time analytics, and resources consumed by database management.
After migrating to TimescaleDB, Latitude achieved $12,000 monthly cost reduction, improved query performance, seamless real-time analytics, and scalability to handle larger datasets.

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The New Stack

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How Slack Transformed Cron into a Distributed Job Scheduler

Slack transformed its cron jobs into a distributed system due to issues with a single server and increased maintenance time.
Initially, Slack used a single server to handle cron jobs but faced limitations and downtimes due to out-of-memory errors.
Moving to a distributed system enabled Slack to increase reliability, reduce maintenance, and gain more job insight.
Instead of opting for Kubernetes's own cronjob, Slack leveraged its existing asynchronous computing platform and integrated a job execution engine with cron.
Cron scripts were executed through a dedicated queue wrapped as jobs using Kafka and an AWS EC2 instance.
Leader election with locking was implemented to ensure a primary server executed all scripts, with backup servers ready to take over swiftly.
A database was introduced to track script execution statuses, providing centralized information on job runs.
By leveraging existing systems like Golang and Kubernetes, Slack successfully transitioned to the distributed cron system, reducing on-call burdens.
The new system has executed over six million scripts with reduced maintenance complexities, highlighting the benefits of utilizing current resources.
Adams emphasized the importance of simplicity and utilizing available tools to streamline operations and scale efficiently.

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Hackernoon

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Here's How To Connect an Azure Kubernetes Cluster Using Kubectl

Azure Kubernetes Service (AKS) simplifies the deployment, management, and scaling of containerized applications using Kubernetes.
To connect to an Azure Kubernetes Service (AKS) cluster using kubectl, you will need to perform the following steps: Install the Azure CLI, Install Kubectl, Authenticate with Azure, Set the subscription, Generate kubeconfig file, and Verify the connection.
Connecting to an AKS cluster using kubectl is essential for monitoring cluster health, deploying applications, and performing cluster administration tasks.
By following this guide, you can effectively manage your Kubernetes environment in Azure and utilize tools like kubectl and Azure CLI for efficient cluster management.

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Dev

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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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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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[ 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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Dev

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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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