Get live statistics and analysis of Karan Jagtiani's profile on X / Twitter

The future of DevOps isn’t full autonomy. It's Human-in-the-loop AI. A copilot that triages, proposes, and runs safe playbooks. Humans approve, adjust, and provide judgment. Autonomy without explainability is risk. Autonomy without approval is noncompliance. The goal isn’t to replace DevOps engineers. It’s to amplify them. If your AI agent can’t explain its actions or offer an easy rollback, it’s not ready for production.

Big update from AWS on gp3 volumes: 👉 Max storage size increased from 16 TiB to 64 TiB (4× increase) 👉 Max IOPS increased from 16,000 to 80,000 (5× increase) 👉 Max throughput increased from 1,000 MiB/s to 2,000 MiB/s (2× increase) Why this matters for DevOps and cloud engineers: ✅ You can now use a single large gp3 volume rather than striping multiple smaller ones for large-scale workloads. ✅ You maintain the cost-efficient general-purpose SSD class while unlocking much higher performance ceilings. ✅ Analytics, media processing, large databases and containerized workloads running on fewer volumes just got simpler. If your architecture includes a high-volume database, data lake, media repository, or container cluster with heavy I/O, this upgrade opens up a lower-maintenance path.

We hit 5TB daily logs and Elasticsearch started choking. What we found: - Unbounded index count - Replica shards set too high - Heap saturation on data nodes Fix: - Consolidated indices by date - Reduced replicas to 1 - Introduced ILM with rollover policy

You don't need Kubernetes even after you hit PMF. Too many teams over-engineer their cloud setup before their first user even lands on the product. Kubernetes, service meshes, and GitOps pipelines sound good on paper, but in reality they slow you down when you're pre-scale. If you're under 10 engineers and shipping a single product: 👉 Docker Compose and a few EC2 instances are enough. 👉 CI/CD can be a simple GitHub Action, not Argo + Helm + Flux. 👉 A simple autoscaling group is all you need until scale actually becomes a problem. Kubernetes isn't bad, it's just expensive cognitive overhead. Every YAML line you write early on is a debt you'll pay in context switching later. At 1 million users, it's resilience. At 10 users, it's resistance to shipping. 💡 Early stage: focus on fast feedback, observability, and recovery. 🚀 Later stage: add orchestration, scaling, and control planes. The best infra choice is the one that doesn't slow down your velocity.

Everyone talks about shipping faster. Few talk about recovering faster. The rate at which you can recover from a failure is what defines your velocity. If your mean time to recovery (MTTR) is hours, you aren't moving fast, you're gambling. To truly "move fast and break things", you need: ✅ Rollbacks in seconds, not hours ✅ Immutable deploys ✅ Backups you've actually restored before ✅ A culture where incidents teach, not punish The fastest teams ship small, break things, and recover instantly. Failing fast is useful only if you can come back online faster than your users notice.That's not recklessness. That's good engineering.

A cached user profile expires and a worker recomputes it from the DB. User updates their profile while recompute is running. Worker writes cache with the old DB value and evicts the fresh change. Now the cache shows stale info. How do you prevent this?

Kubernetes Learning Roadmap 1️⃣ Start: Learn the Core Concepts → Pods = smallest deployable unit → Deployments = manage replicas → Services = expose apps → ConfigMaps & Secrets = app configs 2️⃣ Build: Get Hands-On → Run a local cluster with Minikube or Kind → Deploy a sample microservice → Expose it using port forwarding → Scale replicas and test rolling updates 3️⃣ Certify: 🎓 CKA – Certified Kubernetes Administrator ⚙️ CKAD – Certified Kubernetes Application Developer 4️⃣ Deep Dive: Networking • Storage • RBAC • Ingress • Monitoring • Autoscaling 5️⃣ Specialize: → GitOps with ArgoCD → Service Mesh with Istio → Helm & Operators → Multi-cluster and edge setups 💡 Don't just deploy. Observe, scale, and break things to learn.

How to scale Kubernetes clusters? 1–10 pods → Just starting • Single node or small cluster • Manual deployments 10–100 pods → Maturity begins • Use Deployments + HPA • Add readiness/liveness probes • Configure resource requests/limits 100–500 pods → Stability challenges • Use Cluster Autoscaler • Introduce node taints and affinities • Centralized logging and metrics 500–5000 pods → Control plane strain • Split into multiple namespaces • Tune kubelet and API server params • Enforce PodSecurityPolicies or OPA 5000+ pods → Enterprise scale • Multi-cluster federation • Dedicated control planes per region • GitOps workflows (ArgoCD, Flux) • Automated upgrades + chaos testing

If you've never done a blameless postmortem, you're not truly doing DevOps. Incident reviews should focus on system design and decision flow, not who did what. That's how reliability scales.

If you're serious about backend mastery: Start with these 5 pillars: - DB schema design - Query optimization - API versioning - Observability - CI/CD automation Each deserves its own weekend project.

AWS N. Virginia outage explained in plain English 👇 1. A DNS automation fault in DynamoDB's internal control system caused its main endpoint to lose valid IP records. 2. Two internal DNS update processes ran at the same time and overwrote each other's changes, removing the active entries in Route 53. 3. DynamoDB in us-east-1 went offline, taking down dependent services like EC2, Lambda, Redshift, and IAM that rely on it for state and configuration data. 4. EC2 couldn't launch new instances because the control plane uses DynamoDB tables to store instance mapping and health data. 5. Network, Load Balancer, and monitoring systems began failing next since they depend on those APIs to fetch routing and resource metadata. 6. Engineers isolated the faulty automation job, restored DNS entries, and gradually rebalanced traffic. Full recovery took close to 15 hours. For DevOps and Cloud Engineers, this is a case study in how tightly coupled dependencies can cascade across global systems. A single DNS entry in one region disrupted large parts of the internet. 🔁 Repost if this helped you understand it better.

Every infra team hits this point: CI speed becomes the bottleneck, not compute. Optimizing Docker layer caching + parallelizing build jobs with self-hosted runners = 10x velocity. Worth revisiting your pipeline architecture every 3 months.

We hit 5TB daily logs and Elasticsearch started choking. What we found: - Unbounded index count - Replica shards set too high - Heap saturation on data nodes Fix: - Consolidated indices by date - Reduced replicas to 1 - Introduced ILM with rollover policy

AWS N. Virginia outage explained in plain English 👇 1. A DNS automation fault in DynamoDB's internal control system caused its main endpoint to lose valid IP records. 2. Two internal DNS update processes ran at the same time and overwrote each other's changes, removing the active entries in Route 53. 3. DynamoDB in us-east-1 went offline, taking down dependent services like EC2, Lambda, Redshift, and IAM that rely on it for state and configuration data. 4. EC2 couldn't launch new instances because the control plane uses DynamoDB tables to store instance mapping and health data. 5. Network, Load Balancer, and monitoring systems began failing next since they depend on those APIs to fetch routing and resource metadata. 6. Engineers isolated the faulty automation job, restored DNS entries, and gradually rebalanced traffic. Full recovery took close to 15 hours. For DevOps and Cloud Engineers, this is a case study in how tightly coupled dependencies can cascade across global systems. A single DNS entry in one region disrupted large parts of the internet. 🔁 Repost if this helped you understand it better.

You don't need Kubernetes even after you hit PMF. Too many teams over-engineer their cloud setup before their first user even lands on the product. Kubernetes, service meshes, and GitOps pipelines sound good on paper, but in reality they slow you down when you're pre-scale. If you're under 10 engineers and shipping a single product: 👉 Docker Compose and a few EC2 instances are enough. 👉 CI/CD can be a simple GitHub Action, not Argo + Helm + Flux. 👉 A simple autoscaling group is all you need until scale actually becomes a problem. Kubernetes isn't bad, it's just expensive cognitive overhead. Every YAML line you write early on is a debt you'll pay in context switching later. At 1 million users, it's resilience. At 10 users, it's resistance to shipping. 💡 Early stage: focus on fast feedback, observability, and recovery. 🚀 Later stage: add orchestration, scaling, and control planes. The best infra choice is the one that doesn't slow down your velocity.

How to scale Kubernetes clusters? 1–10 pods → Just starting • Single node or small cluster • Manual deployments 10–100 pods → Maturity begins • Use Deployments + HPA • Add readiness/liveness probes • Configure resource requests/limits 100–500 pods → Stability challenges • Use Cluster Autoscaler • Introduce node taints and affinities • Centralized logging and metrics 500–5000 pods → Control plane strain • Split into multiple namespaces • Tune kubelet and API server params • Enforce PodSecurityPolicies or OPA 5000+ pods → Enterprise scale • Multi-cluster federation • Dedicated control planes per region • GitOps workflows (ArgoCD, Flux) • Automated upgrades + chaos testing

A cached user profile expires and a worker recomputes it from the DB. User updates their profile while recompute is running. Worker writes cache with the old DB value and evicts the fresh change. Now the cache shows stale info. How do you prevent this?

The future of DevOps isn’t full autonomy. It's Human-in-the-loop AI. A copilot that triages, proposes, and runs safe playbooks. Humans approve, adjust, and provide judgment. Autonomy without explainability is risk. Autonomy without approval is noncompliance. The goal isn’t to replace DevOps engineers. It’s to amplify them. If your AI agent can’t explain its actions or offer an easy rollback, it’s not ready for production.

If you've never done a blameless postmortem, you're not truly doing DevOps. Incident reviews should focus on system design and decision flow, not who did what. That's how reliability scales.

Kubernetes Learning Roadmap 1️⃣ Start: Learn the Core Concepts → Pods = smallest deployable unit → Deployments = manage replicas → Services = expose apps → ConfigMaps & Secrets = app configs 2️⃣ Build: Get Hands-On → Run a local cluster with Minikube or Kind → Deploy a sample microservice → Expose it using port forwarding → Scale replicas and test rolling updates 3️⃣ Certify: 🎓 CKA – Certified Kubernetes Administrator ⚙️ CKAD – Certified Kubernetes Application Developer 4️⃣ Deep Dive: Networking • Storage • RBAC • Ingress • Monitoring • Autoscaling 5️⃣ Specialize: → GitOps with ArgoCD → Service Mesh with Istio → Helm & Operators → Multi-cluster and edge setups 💡 Don't just deploy. Observe, scale, and break things to learn.

Every infra team hits this point: CI speed becomes the bottleneck, not compute. Optimizing Docker layer caching + parallelizing build jobs with self-hosted runners = 10x velocity. Worth revisiting your pipeline architecture every 3 months.

If you're serious about backend mastery: Start with these 5 pillars: - DB schema design - Query optimization - API versioning - Observability - CI/CD automation Each deserves its own weekend project.

Big update from AWS on gp3 volumes: 👉 Max storage size increased from 16 TiB to 64 TiB (4× increase) 👉 Max IOPS increased from 16,000 to 80,000 (5× increase) 👉 Max throughput increased from 1,000 MiB/s to 2,000 MiB/s (2× increase) Why this matters for DevOps and cloud engineers: ✅ You can now use a single large gp3 volume rather than striping multiple smaller ones for large-scale workloads. ✅ You maintain the cost-efficient general-purpose SSD class while unlocking much higher performance ceilings. ✅ Analytics, media processing, large databases and containerized workloads running on fewer volumes just got simpler. If your architecture includes a high-volume database, data lake, media repository, or container cluster with heavy I/O, this upgrade opens up a lower-maintenance path.

Everyone talks about shipping faster. Few talk about recovering faster. The rate at which you can recover from a failure is what defines your velocity. If your mean time to recovery (MTTR) is hours, you aren't moving fast, you're gambling. To truly "move fast and break things", you need: ✅ Rollbacks in seconds, not hours ✅ Immutable deploys ✅ Backups you've actually restored before ✅ A culture where incidents teach, not punish The fastest teams ship small, break things, and recover instantly. Failing fast is useful only if you can come back online faster than your users notice.That's not recklessness. That's good engineering.