Gustavo Lima

🔍 𝐔𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐅𝐮𝐥𝐥 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐨𝐟 𝐘𝐨𝐮𝐫 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐰𝐢𝐭𝐡 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠! 🔍 Today, ensuring your Kubernetes clusters perform reliably is more important than ever. Learn how effective monitoring and logging can make a difference in our latest carousel. 📊 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮'𝐥𝐥 𝐋𝐞𝐚𝐫𝐧: 1️⃣ 𝑾𝒉𝒚 𝑴𝒐𝒏𝒊𝒕𝒐𝒓 𝒂𝒏𝒅 𝑳𝒐𝒈? Understand the necessity of visibility in complex Kubernetes environments to ensure optimal performance and quick issue resolution. 2️⃣ 𝑲𝒆𝒚 𝑴𝒐𝒏𝒊𝒕𝒐𝒓𝒊𝒏𝒈 𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒊𝒆𝒔: Discover both infrastructure and application monitoring approaches that keep your systems efficient and robust. 3️⃣ 𝑴𝒆𝒆𝒕 𝑷𝒓𝒐𝒎𝒆𝒕𝒉𝒆𝒖𝒔: Explore how Prometheus serves as the cornerstone for monitoring within Kubernetes, providing extensive data collection and real-time alerting capabilities. 4️⃣ 𝑽𝒊𝒔𝒖𝒂𝒍𝒊𝒛𝒊𝒏𝒈 𝒘𝒊𝒕𝒉 𝑮𝒓𝒂𝒇𝒂𝒏𝒂: See how integrating Grafana can enhance your understanding of data through dynamic and insightful dashboards. 5️⃣ 𝑪𝒐𝒎𝒑𝒓𝒆𝒉𝒆𝒏𝒔𝒊𝒗𝒆 𝑳𝒐𝒈𝒈𝒊𝒏𝒈 𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒊𝒆𝒔: Learn about the essential tools like Fluentd and the ELK stack that empower you to capture and analyze logs for deeper insights. 6️⃣ 𝑪𝒉𝒐𝒐𝒔𝒊𝒏𝒈 𝒕𝒉𝒆 𝑹𝒊𝒈𝒉𝒕 𝑻𝒐𝒐𝒍𝒔: Tailor your monitoring and logging tools to your specific needs based on the scale and complexity of your cluster. 7️⃣ 𝑪𝒐𝒏𝒄𝒍𝒖𝒔𝒊𝒐𝒏 - 𝐀 𝐖𝐞𝐥𝐥-𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐞𝐝 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐞𝐝 𝐂𝐥𝐮𝐬𝐭𝐞𝐫: Summarize the empowerment and control you gain with these practices, leading to a more stable and performance-optimized Kubernetes environment. 👉 Swipe through to gain valuable insights that can revolutionize your deployment management. For more detailed guides and expert advice, visit our website www.atmosly.com 🚀 #Kubernetes #Monitoring #Logging #DevOps #CloudComputing #TechInnovation #Atmosly #DevOpsCommunity #DevOpsEngineer #DevOpsEngineering #PlatformEngineering #PlatformEngineer #KubernetesClusters #SquareOps

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"Looking to streamline your development workflow in the AWS Cloud ecosystem? Look no further! 🚀 When it comes to Version Control Management and CI/CD pipelines, choosing the right tools is crucial. Here are my top picks for the best CI/CD tools: 1. AWS CodePipeline: Seamlessly orchestrate your release process with automated pipelines, integrated with AWS services for smooth deployment. 2. AWS CodeCommit: Securely store your Git repositories in the cloud with fully managed hosting, ensuring reliable version control. 3. Jenkins: Extend your CI/CD capabilities with the flexibility of Jenkins, easily integrated with AWS services for automated builds and deployments. 4. GitLab CI/CD: Empower your teams with a comprehensive DevOps platform, offering robust version control and CI/CD pipelines within the AWS environment. These tools offer the scalability, reliability, and integration capabilities needed to drive efficiency and innovation in your AWS projects. Ready to elevate your development process? Let's connect and explore the possibilities together! #AWS #DevOps #CI/CD #VersionControl #CloudComputing"

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Kubernetes CronJob Usecase "Uncover the versatility of Kubernetes CronJobs through practical use cases. Explore how CronJobs enable the automation of recurring tasks within Kubernetes clusters. From periodic backups and log rotation to data processing pipelines and scheduled jobs, discover how CronJobs streamline operational workflows and enhance system reliability. Delve into real-world examples to grasp the breadth of possibilities offered by Kubernetes CronJobs." #Kubernetes, #CronJob, #Automation, #DevOps, #CloudComputing, #UseCase, #ScheduledTasks, #OperationalEfficiency, #DataProcessing, #SystemReliability https://lnkd.in/gXsDN4K6

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Explore an end-to-end guide on using #MLflow to train a linear regression model, package it, and deploy it to a #Kubernetes cluster. ✔ Understand how MLflow simplifies the entire process, from training to serving. Deploy MLflow Model to Kubernetes ➡ https://lnkd.in/eUmH-wZa #opensource #oss #linuxfoundation #mlops #linuxfoundation

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Another dbt blog post during our Datashift dbt month!🔶In this blog post, Sofie Theys and Cedric Raeymaeckers examine how #Hive and #Iceberg tables differ in AWS Athena and how they affect your data workflows, especially when you use dbt. For businesses that rely heavily on data analytics or run data warehouses, the table structures they use are critical for optimizing data operations. The right choice of table structures with dbt will help users get the most out of their data analysis and achieve better business results. Discover your ideal table structure. Check out our 🐝Vs. 🧊! #dbtblog dbt Labs #datapiplines #tablestructures

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Is write once, run once becoming a reality? Does #WebAssembly / #WASM present an alternative to creating and maintaining secure and optimised containers to run application services? #kubernetes #cloudnative https://lnkd.in/eEAUHeDm

For kubernetes expert : If we do not have Kubernetes scheduler installed in the master cluster, how can we deploy a pod in a worker node?

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Explore our comparative analysis of Percona Server for MongoDB on bare metal vs. Percona Operator for MongoDB in Kubernetes. Learn how containerized databases sustain performance using the Operator and access the complete report on the efficiency of cloud-native databases in Kubernetes environments. https://hubs.ly/Q02v9ZLh0 #OpenSource #CloudNative

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[1]: https://lnkd.in/ex_DfjHD "" [2]: https://lnkd.in/eyzqBH7e "" [3]: https://lnkd.in/eh79hbCf "" [4]: https://lnkd.in/eh-dxTey "" Certainly! Here's a LinkedIn post about AWS Lambda functions and optimization: --- 🚀 **Optimizing AWS Lambda Functions for Performance and Cost Efficiency** As serverless computing gains popularity, AWS Lambda has become a go-to choice for building scalable and cost-effective applications. To make the most of Lambda, it's essential to optimize your functions for both performance and cost efficiency. Let's dive into some key strategies: 1. **Memory Configuration Matters** 💡 - When you create a Lambda function, you specify the amount of memory allocated to it. This directly affects its performance. Higher memory settings allow for more CPU power and faster execution. - Experiment with different memory configurations to find the sweet spot for your workload. Remember that you pay for the memory you allocate, so balance performance needs with cost considerations. 2. **Reducing Cold Starts** ❄️ - Cold starts occur when a Lambda function is invoked for the first time or after a period of inactivity. These can impact latency. - To minimize cold starts: - Keep your functions warm by invoking them periodically (e.g., using CloudWatch Events or scheduled triggers). - Opt for provisioned concurrency to maintain a pool of warm instances. - Use smaller deployment packages to reduce initialization time. 3. **Error Handling and Retries** 🔄 - Implement robust error handling in your Lambda functions. Handle exceptions gracefully and consider retries for transient failures. - Configure dead-letter queues (DLQs) to capture failed events and troubleshoot issues effectively. 4. **Function Concurrency** 🔄 - Lambda automatically scales based on incoming requests. However, excessive concurrency can lead to resource contention. - Set appropriate concurrency limits to prevent overloading your function. 5. **Timeouts and Function Duration** ⏱️ - Set reasonable timeouts for your functions. If a function runs too long, it may be terminated. - Optimize your code to execute within the allocated time window. 6. **Monitoring and Observability** 👀 - Use CloudWatch Logs and Metrics to monitor your Lambda functions. - Leverage X-Ray for distributed tracing and performance insights. Remember, AWS Lambda is a powerful tool, but thoughtful optimization ensures you get the best performance while keeping costs in check. Share your Lambda optimization tips in the comments! 👇 #AWSLambda #Serverless #CloudComputing

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Is Kubernetes the hardest technology to recruit in the modern Infrastructure / DevOps automation stack? This question has been on my mind recently, and I'm curious to hear your thoughts? In my experience, finding someone with expert knowledge in Kubernetes has proven to be quite challenging. Interestingly, I've noticed that individuals who are considered Kubernetes experts in one organization may struggle in interviews with other companies due to a lack of depth in the technology. This raises the question: why is Kubernetes such an enigma? I'd love to hear your insights on this topic. Have you had similar experiences when interviewing or recruiting for positions that require Kubernetes skills? What strategies have you found to be effective in finding and hiring Kubernetes experts? #Kubernetes #Recruiting #Technology #Expertise

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Kubernetes is a powerful orchestrator, but what if your workloads run better in VMs instead of containers? 🤔 Discover how KubeVirt extends the Kubernetes API to schedule VM workloads alongside containers. We'll configure an AWS EKS cluster to run KubeVirt, scheduling VMs on bare metal nodes and containers on EC2 instances. 📅 Date: 5/22/24 🕒 Time: 10AM PST Don't miss out! Register now and elevate your Kubernetes game! 📈 https://bit.ly/4ayOJxU

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Redis and Its Default Configurations: A Dangerous Cocktail at Scale 🍸🚀 When it comes to deploying Redis in a production environment, the default configurations are fine for local or dev setups. But at scale, certain settings can become ticking time bombs. Beyond the obvious tweaks like eviction policy and maxmemory configs, let's explore some lesser known but critical settings you need to adjust: 🔧 1. repl-backlog-size (Buffer for Replication) Default Size: 1MB At scale, more than 1MB of data can change within a second. If your replica goes offline for more than a second, it might have to perform a full sync due to the small backlog, severely impacting master performance. Adjust this setting to ensure your replicas can recover and stay in sync with the master. 🔧 2. client-output-buffer-limit (Replica) Default Transfer Limit: 64MB The default transfer limit between master and replica is 64MB. Any minor connection issue can force a replica to go out of sync, leading to a full sync which might fail due to this limit. Increase this limit to handle higher data transfer volumes. 🔧 3. save (Backup Dump) Default Backup Intervals: Every 15 minutes if at least 1 key changes. Every 5 minutes if at least 300 keys change. Every 1 minute if at least 10,000 keys change. At peak scale, these thresholds are easily hit, causing frequent backups and significant I/O and CPU overhead. Adjust these intervals to reduce overhead in high-scale environments. 💡 Key Takeaway: Default Redis configurations aren’t cut out for production at scale. Properly tuning settings like repl-backlog-size, client-output-buffer-limit, and save intervals is crucial for maintaining performance and stability. 📊 Pro Tip: Always monitor your Redis instance and adjust configurations based on your specific workload and scale. #Redis #TechTips #DatabaseManagement #Scalability #DevOps #PerformanceOptimization #TechLeadership #ProductionReady

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#StarWind_how_to Exploring #GitSubmodules in #AzureDevOps? Part II of our series by Didier Van Hoye for #StarWind provides clear instructions on how to add, update, and clone submodules, ensuring a smoother workflow for your projects. Read more here: https://starwind.com/s/2X

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New Blog Alert! Curious about the best tool for managing your Azure infrastructure? Our latest blog by Samir Zorlak, Software Developer, dives deep into the comparison between Terraform and Azure Bicep, exploring their key features, deployment processes and pros and cons. 🔍 What You'll Learn: - The versatility of Terraform with its multi-cloud support - How Azure Bicep simplifies Azure-native deployments - Detailed comparisons to help you choose the right tool for your project Whether you're a multi-cloud enthusiast or an Azure purist, this blog will provide you with valuable insights to streamline your infrastructure management. 👉 Read the full blog here:https://lnkd.in/dxafTaCC

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#StarWind_how_to Embark on a journey through #Git submodules in #Azure #DevOps pipelines with our insightful article by Didier Van Hoye for #StarWind. Delve into the challenges faced, solutions discovered, and the path to seamless integration in private repositories. Read more here: https://starwind.com/s/1V

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🚨 What's the state of Kubernetes, 10 years into it? During #KubeCon, Sander Almekinders sat down with Shaun O'Meara from Mirantis to talk about what's happening in the world of #kubernetes. It's an interesting discussion about maturity, challenges and - of course - the impact AI has and will have on #cloudnative development. You can find the link to the episode in the first comment below 👇 Martha de Monclin

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Model Gateway is now available on GitHub and Docker Hub. Typical use cases and scenarios: ✅ Load balancing between multiple inference endpoints and regions. For example: 👍 #AzureOpenAI load balancing between multiple regions. 👍 #Ollama load balancing between multiple nodes running in your cluster. ✅ Automatic failover and redundancy in case of AI service outages. ✅ Handling of AI service provider token and request limiting. Documentation: https://modelgw.com/docs GitHub: https://lnkd.in/dXd35SrS

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In March, Fullstaq could be found at KubeCon | CloudNativeCon Paris! Afterwards, Gerrit had an interview with Daniëlle in which he shared his insights on the event, the topics, and the potential future of Kubernetes and the cloud-native landscape. In this blog post, you can read all about these findings, tips on how to use AI, how to make developers comfortable with Kubernetes, and more! 🚀 Read it on our website: https://lnkd.in/enjWwq9G #cloudnative #cloudengineer #kubernetes #kubernetesandAI

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