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Struggling to remember DevOps commands during interviews? πŸ˜“ You’re not alone… I created a DevOps Quick Reference Guide that covers: βœ” Linux, Git, Docker, Kubernetes βœ” Terraform, Jenkins, AWS βœ” R
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🚨 DevOps Engineers β€” Read This Before 2026 Hits Most engineers don’t fail because of lack of tools… They fail because of these hidden mistakes ⚠️ From ignoring observability to weak cloud fundament
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πŸ“Œ Why do memory issues appear only after long runtime? Memory issues usually don’t show instantly because they build up slowly as the application keeps running. --- 1️⃣ Memory Leaks 🧠 The most c
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Restarting a service resets its runtime state, memory, and connections, which can remove symptoms of underlying problems. However, the root cause usually remains, so the issue may reappear. --- 1️⃣ M
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πŸ“Œ Role of ArgoCD & Why not deploy directly from pipeline? In our setup, ArgoCD is used to implement GitOps, where Git is the single source of truth for deployments. --- 1️⃣ Git as Source of Truth
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In distributed systems, not all requests are handled the same way. Some succeed while others fail due to inconsistent conditions across components. --- 1️⃣ Load Balancing Differences βš–οΈ Requests ar
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Volume mount failures happen when Kubernetes cannot attach or mount storage required by the Pod. 1️⃣ Persistent Volume Not Available πŸ’Ύ If the requested PersistentVolume (PV) is: not created not boun
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A successful pipeline only validates build and basic tests, not the full complexity of production environments. --- 1️⃣ Environment Differences βš™οΈ Production may differ from CI in: configs environ
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Systems slow down as load increases and hidden inefficiencies accumulate. --- 1️⃣ Data Growth πŸ“Š As data increases: queries become slower indexes become larger storage access takes more time --
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Caching improves performance, but it introduces data consistency and correctness challenges if not handled properly. 1️⃣ Stale Data ⚠️ Cached data may not reflect the latest updates. Example: user up
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Deployments always carry risk. Doing them on Friday increases the chance that issues will impact weekend support and recovery time. 1️⃣ Limited Team Availability πŸ‘₯ On weekends: fewer engineers are a
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Auto-scaling helps handle traffic, but it can still fail if metrics, timing, or architecture are not properly designed. 1️⃣ Scaling Delay ⏳ Auto-scaling is not immediate. metrics need to cross thresh
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About @master.devops

SDE | DevOps | SRE | PAAS is a Instagram nano influencer. With 2.1K followers and 12 posts, master.devops's account shows a follower-to-following ratio of N/A:1. View all content anonymously on Pictame - no login required.

master.devops's engagement rate sits at 1.00%. For accounts with 1K-10K followers, the average is around 5%, so there's some room to grow β€” though every community is unique.

Out of the last 12 posts, 92% are videos and 8% are photos. Notably, photos tend to get 53.7x more engagement than videos. Content shows up about every 4 days. Captions lean detailed, averaging 1235 characters.

1.00% engagement rate β€” room to grow compared to average for 1K-10K follower accounts.
92% videos, 8% photos. Photos get 53.7x more engagement here.
Posts about every 4 days.

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master.devops
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@master.devops

SDE | DevOps | SRE | PAAS

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#DevOps#SRE#Cloud#Debugging#Production#CICD#Automation#AutoScaling

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