Cortex Update: Ask from Slack, Focus on Business Value

The latest update for #Cortex includes "The job is not to write code. It's to produce business value." and "Faster code doesn't mean faster delivery". #microservices #SRE #devops https://lnkd.in/ebGhkU-j

The latest update for #Cortex includes "#Softwaredevelopment standards and best practices" and "What is an EngOps platform? Key Features, Benefits, and Use Cases". #microservices #SRE #devops https://lnkd.in/ebGhkU-j

The latest update for #Cortex includes "Every engineering org is taking an #AI readiness test right now" and "Software development standards and best practices". #microservices #SRE #devops https://lnkd.in/ebGhkU-j

𝗗𝗮𝘆 𝟴𝟲 𝗼𝗳 #𝟭𝟬𝟬𝗗𝗮𝘆𝘀𝗢𝗳𝗦𝘆𝘀𝘁𝗲𝗺𝗗𝗲𝘀𝗶𝗴𝗻 — 𝗕𝗹𝘂𝗲-𝗚𝗿𝗲𝗲𝗻 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 In distributed systems, deployments are one of the riskiest moments. A single bad release can break features, affect users, or bring everything down. Blue-green deployments are designed to remove that risk by changing how releases happen. Instead of updating the live system directly, you maintain two identical environments. One runs the current version, while the other holds the new version ready to go. The new version is deployed and tested in isolation, without affecting users. When everything is confirmed to be working, traffic is simply switched to the new environment, making the release instant and seamless. If anything goes wrong, switching back is just as fast. Without this approach, deployments can feel like a gamble. With blue-green deployments, releases become controlled, predictable, and reversible. The trade-off is cost and complexity, since you need to maintain duplicate environments and handle data consistency carefully. But in return, you gain confidence. Because in real systems, it is not just about building features. It is about releasing them safely. #SystemDesign #DistributedSystems #DevOps #BackendEngineering #100DaysOfCode

Platform Engineering 102 : Source code by itself does not make a company $$. It's only when it is deployed, and accessed by the customers, does a company start making $$. So, the whole point of a technology platform is to optimize this process, every step of the way - basically make it as seamless as possible to move code from the repo -> compute. The faster this process is, the more $$ the company is poised to make. #flowoptimization #platformengineering #devops

𝗗𝗮𝘆 𝟵𝟭 𝗼𝗳 #𝟭𝟬𝟬𝗗𝗮𝘆𝘀𝗢𝗳𝗦𝘆𝘀𝘁𝗲𝗺𝗗𝗲𝘀𝗶𝗴𝗻 — 𝗭𝗲𝗿𝗼-𝗗𝗼𝘄𝗻𝘁𝗶𝗺𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 In distributed systems, downtime during deployments is no longer acceptable, because users expect services to be available at all times, regardless of updates or changes happening behind the scenes. Zero-downtime deployments are designed to meet this expectation by allowing systems to be updated without taking them offline, ensuring that users can continue interacting with the system without interruption. Instead of shutting down services to apply changes, new versions are introduced gradually while the system is still running. Old and new versions coexist for a period of time, and traffic is shifted carefully until the transition is complete. This approach relies on strategies like rolling updates, blue-green deployments, and canary releases, all working together to make deployments smooth and controlled. The challenge, however, lies in ensuring compatibility. Both versions of the system must work together seamlessly, especially when dealing with shared data and ongoing user activity. Without this level of planning, deployments can introduce inconsistencies or unexpected failures. With it, deployments become invisible to users. Because in modern system design, it is not just about releasing new features. It is about releasing them without anyone noticing. #SystemDesign #DistributedSystems #DevOps #BackendEngineering #100DaysOfCode

Thinking Kubernetes resource limits only impacts production stability misses a massive opportunity for developer productivity. Setting precise CPU and memory requests and limits isn't just about resource allocation for the scheduler. It’s about creating predictable environments that prevent 'noisy neighbor' issues and guide engineers on their application's true footprint. * Automate default resource definitions. Remove the guesswork; give developers a baseline that scales with their application. * Integrate resource recommendations into CI/CD. Use historical data or load test results to auto-tune and flag deviations early. * Empower local environments to *enforce* these limits. Catch OOMKills and performance regressions during development, not after deployment to shared clusters. This shift transforms resource management from a production burden into a powerful developer tool, speeding up iterations and reducing friction across the development lifecycle. How do you help your teams set effective Kubernetes resource limits? #Kubernetes #DevOps #DeveloperExperience #CloudNative #Productivity

Innovation is only as good as its uptime. Many teams overlook that the 'initial build' is just the tip of the iceberg regarding Total Cost of Ownership. Bridging that gap early is a massive competitive advantage.

This is the transitions I like to see, As much as I like vibe coding lately, there are still many concern about how the code you start "for fun" end up in production, security is the first issue, if you don't really know what your code do, someone else will understand that and probably in a malicious way, but if you start with solid foundations you can build on top of it easily!

𝗗𝗮𝘆 𝟴𝟳 𝗼𝗳 #𝟭𝟬𝟬𝗗𝗮𝘆𝘀𝗢𝗳𝗦𝘆𝘀𝘁𝗲𝗺𝗗𝗲𝘀𝗶𝗴𝗻 — 𝗖𝗮𝗻𝗮𝗿𝘆 𝗥𝗲𝗹𝗲𝗮𝘀𝗲𝘀 In distributed systems, releasing a new version to all users at once can be one of the riskiest decisions a team makes, because even a small issue can quickly scale into a widespread failure when exposed to full production traffic. 𝗖𝗮𝗻𝗮𝗿𝘆 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 solve this problem by introducing change gradually instead of all at once, allowing a new version of a system to be deployed to a small subset of users while the majority continues using the stable version. This creates an opportunity to observe real-world behavior, monitor system performance, and detect issues early before they impact everyone. As confidence grows, the rollout is expanded step by step until the new version fully replaces the old one, making the entire deployment process feel less like a leap and more like a controlled transition. Without canary releases, failures tend to affect all users at the same time, making them harder to contain and more damaging. With canary releases, the impact is limited, giving teams the ability to react quickly and make informed decisions based on actual system behavior. This approach does come with added complexity, as it requires strong monitoring, traffic routing, and the ability to manage multiple versions of a system simultaneously, but the trade-off is a much safer and more reliable deployment process. In the end, canary releases shift deployments from high-risk events into gradual experiments, where systems evolve carefully instead of changing all at once. #SystemDesign #DistributedSystems #DevOps #BackendEngineering #100DaysOfCode