Technologies
I bring extensive expertise in Kubernetes, Docker, Helm, and Terraform, coupled with hands-on experience in CNCF tools like ArgoCD, Cilium, Karpenter, Kyverno, Prometheus, and Grafana. My knowledge extends to AWS cloud services (EC2, VPC, SSM, IAM, CloudFormation, etc), alongside proficiency in automation tools such as Ansible and PowerShell, and scripting with Python.
Services and Stack
Use: What, Where, why, why not this
AWS
What: AWS (Amazon Web Services) is a cloud computing platform developed by Amazon that provides on-demand computing resources like Virtual servers, Storage, Databases, Networking, DevOps tools, AI/ML services, Container and Kubernetes services, Monitoring tools
Where:
Why: oldest
400+ edge locations (CDN) - low latency
105+ Availability Zones
30+ geographic regions - disaster recovery, and high availability.
A huge community for support and collaboration.
Pay-as-you-go model with Reserved, On-demand, Spot instances.
Why not Azure
AWS = Best overall platform for scalability (largest global infrastructure), reliability (AWS pioneered multi-AZ and multi-region architectures.), and range of services (AWS offers 300+ services, covering everything)
Azure = Best for enterprise integration (Natively integrates with Active Directory (AD), Office 365, Windows Server, and SQL Server) (especially if you use Microsoft tools).
GCP = Best for AI/ML (Google is the creator of TensorFlow and TPU (Tensor Processing Unit).), data analytics (Google BigQuery, Dataflow, and Pub/Sub are highly optimized for big data workloads), and developer experience (GCP’s UI and SDKs are clean, modern, and developer friendly.).
☁️ AWS vs Azure vs GCP — Unique Strengths & Comparisons
Feature / Area
AWS
Azure
GCP
Remarks / Why
Storage cost & simplicity
✅ Cheaper & simple (S3)
❌ Costlier
❌ Slightly costlier
AWS S3 pricing is lowest and easiest to integrate.
Service maturity & reliability
✅ Most mature
⚪ Mature
⚪ Newer
AWS launched in 2006 — longest experience.
Serverless maturity (Lambda)
✅ Most mature
⚪ Good (Functions)
⚪ Good (Cloud Functions)
Lambda has best ecosystem and performance tuning.
Kubernetes management
⚪ Good (EKS)
⚪ Good (AKS)
✅ Best (GKE)
GKE is Google’s own creation — most stable & auto-managed.
AI / ML ecosystem
⚪ Good (SageMaker)
⚪ Good (Azure AI)
✅ Best (Vertex AI, TensorFlow)
GCP dominates in AI/ML and data science tools.
Hybrid cloud integration
⚪ Good (Outposts)
✅ Best (Arc, Stack)
⚪ Limited (Anthos is multi-cloud, not hybrid infra)
Azure has tight hybrid integration with on-prem.
Microsoft tools integration
❌
✅ Best (AD, Office, Windows)
❌
Only Azure natively integrates with Microsoft stack.
Open-source friendliness
⚪ Average
⚪ Moderate
✅ Best (K8s, TensorFlow, Istio)
GCP contributes heavily to open-source projects.
Developer experience & simplicity
⚪ Complex
⚪ Moderate
✅ Simplest
GCP UI, SDKs, and automation are most developer-friendly.
Data analytics & warehousing
⚪ Good (Redshift)
⚪ Good (Synapse)
✅ Best (BigQuery)
BigQuery is serverless, ultra-fast, and auto-scaled.
Global infrastructure footprint
✅ Widest
⚪ Growing
❌ Smaller
AWS has 100+ AZs — highest availability.
Networking backbone
⚪ Strong
⚪ Good
✅ Best (Google global fiber network)
GCP routes traffic on its private backbone.
Cost optimization options
✅ Flexible (Spot, Savings Plans)
⚪ Good (Reserved Instances)
✅ Auto discounts
GCP auto-applies sustained-use discounts.
Pricing simplicity
❌ Complex
❌ Complex
✅ Simple
GCP’s billing is easiest to predict.
Data transfer (egress) cost
⚪ Average
❌ High
✅ Cheapest
GCP offers lowest egress costs globally.
Cold storage (archive)
✅ Cheapest (Glacier Deep Archive)
⚪ Moderate
⚪ Slightly higher
AWS Glacier is lowest-cost archival option.
Quantum computing
✅ (Braket)
⚪ (Azure Quantum)
⚪ (Cirq SDK only)
AWS Braket provides managed quantum access.
Custom hardware / chips
✅ Graviton, Inferentia, Trainium
⚪ Maia (AI chip)
✅ TPU (AI chip)
AWS best for compute chips, GCP best for ML chips.
Enterprise adoption
✅ Very high
✅ Very high
⚪ Moderate
AWS & Azure dominate enterprise workloads.
Startup / AI adoption
⚪ Good
⚪ Moderate
✅ Most preferred
GCP is popular among AI/data startups.
Hybrid licensing benefit
❌
✅ Yes (Azure Hybrid Benefit)
❌
Azure lets you reuse Windows/SQL licenses.
Sustainability / Green cloud
⚪ By 2025 target
⚪ By 2030 target
✅ Already carbon-neutral since 2007
GCP leads in green energy operations.
Security services
✅ Strong (IAM, GuardDuty, Macie)
✅ Strong (Defender, Entra ID)
⚪ Good (Security Command Center)
Azure integrates deeply with identity (AD).
Backup & DR automation
✅ Built-in (S3 cross-region, Backup)
✅ Paired Regions
⚪ Manual
Azure DR is region-paired by design.
Marketplace ecosystem
✅ Largest
⚪ Big
⚪ Smaller
AWS Marketplace has most third-party integrations.
Learning curve
❌ Steep
⚪ Moderate
✅ Easy
AWS has more services, GCP is simplest to start.
💡 Summary Insights
Cloud
Overall Strength
Known For
AWS
🌍 Scalability, reliability, widest range
Best all-rounder, mature ecosystem
Azure
🏢 Enterprise & hybrid integration
Best for Microsoft-based environments
GCP
🤖 AI, ML, data, developer focus
Best for data & innovation workloads
Would you like me to expand this into a “Top 20 Unique Differences” summary table (short bullet form like: “S3 cheaper”, “BigQuery faster”, “Azure AD strongest identity”)? That’s great for quick memory recall before interviews.
VPC
What: “A VPC is basically our private, isolated network inside the cloud where you have full control-like designing your own data center but with the scalability of the cloud. we define IP ranges, subnets, routing, and connectivity so our resources (like EC2s, databases, or containers) can communicate securely within that controlled environment. It’s the foundation for building secure and structured cloud architectures.
Where:
Why
Issues
IAM
Where:
Why
Why not Azure
Where:
Why
Why not Azure
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Why
Why not Azure
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Why not Azure
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Why not Azure
Where:
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Where:
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Why not Azure
Where:
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Why not Azure
Where:
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Why not Azure
Where:
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Why not Azure
Where:
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Why not Azure
Where:
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Why not Azure
Where:
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Why not Azure
Where:
Why
Why not Azure
SRE Engineer mostly asks questions on Linux, Kubernetes, and Docker
Make a list of repeated question and make them perfect before interview
https://www.linkedin.com/jobs/search/?currentJobId=4254499797&f_TPR=r3600&keywords=devops&origin=JOB_SEARCH_PAGE_JOB_FILTERimp topics
handle merge conflict
python in real life to automate
system design concepts
yaml syntax
linux netowking/ stats
dockerfile
kubernetes services
deployment vs statefulsets
docker network
sli/slo/sla
Architecture
Installation
download and install dependencies
download package
add package to list (apt/yum)
update package
install package
components
Use cases
best practices
Monitoring
RBAC
Backup and restore
security and Updates
Terraform is a widely used Infrastructure as Code (IaC) tool developed by HashiCorp that allows us to define, provision, and manage infrastructure using a declarative configuration language called HashiCorp Configuration Language (HCL). It supports multiple cloud providers like AWS, Azure, GCP, Kubernetes, and even on-premises data centers. ensuring repeatability, consistency, and automation. This helps to avoid manual configurations and human errors
Terraform compares the actual infrastructure state with the desired configuration and makes necessary changes to bring the infrastructure to the expected state.
Ansible is an open-source automation tool used for configuration management, application deployment, and infrastructure automation. It allows us to automate repetitive tasks across multiple servers in a simple and agentless manner, ensuring repeatability, consistency, and automation. This helps to avoid manual configurations and human errors
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