Perfect choice 👍
We’ll do A + B + C together in ONE coherent system so AWS finally feels logical, visual, and practical — not a list to memorize.
From here, your confusion will drop permanently.
🧠 MASTER AWS USING 3 LAYERS
(Mental Model + Mini Projects + Interview Cheat Sheet)
🧩 A) ONE MASTER AWS ARCHITECTURE (VISUAL MEMORY)
Think in FLOW, not services.
🏗️ A1. CORE DATA ENGINEERING FLOW (Your Primary Lens)
Data Source
↓
S3 (Storage)
↓
Glue Catalog (Metadata)
↓
Spark (Glue / EMR / EMR Serverless)
↓
S3 Curated
↓
Athena / BI
🔑 Who does what?
- S3 → holds data
- Glue → understands data
- Spark → processes data
- Athena → queries data
📌 80% of AWS data roles revolve around THIS diagram.
🏗️ A2. APPLICATION + DATA COMBINED (Senior-Level)
User / Event
↓
Lambda (control)
↓
Step Functions (flow)
↓
Glue / EMR (heavy compute)
↓
S3 + CloudWatch
📌 Lambda NEVER does heavy compute
📌 It controls, not processes
🛠️ B) MINI PROJECTS — ONE PER CATEGORY
(Hands-on clarity, no overload)
🔹 B1. COMPUTE PROJECT (EC2 vs Lambda vs EMR)
🎯 Goal
Understand WHEN to use WHAT
Mini Project
| Task | Service |
|---|---|
| Python script running 24/7 | Amazon EC2 |
| Event-based Python validation | AWS Lambda |
| Large PySpark ETL | Amazon EMR |
🧠 Rule:
Small → Lambda
Medium → EC2
Huge → EMR / Glue
🔹 B2. STORAGE PROJECT (S3 vs EBS vs EFS)
Mini Project
Upload CSV → S3
Attach disk → EC2 (EBS)
Share files → EFS
| Data Type | Service |
|---|---|
| Data Lake | Amazon S3 |
| OS Disk | Amazon EBS |
| Shared FS | Amazon EFS |
🔹 B3. DATABASE PROJECT (RDS vs DynamoDB)
Mini Project
| Use Case | Service |
|---|---|
| Transactions, joins | Amazon RDS |
| Key-value, scale | Amazon DynamoDB |
📌 Glue Catalog ≠ RDS (metadata only)
🔹 B4. ORCHESTRATION PROJECT
Mini Project
S3 upload
→ Lambda (validate)
→ Step Functions (flow)
→ Glue Spark Job
| Purpose | Service |
|---|---|
| Control flow | AWS Step Functions |
| Heavy ETL | AWS Glue |
🔹 B5. CI/CD + IaC PROJECT
Mini Project
Git push
→ CodePipeline
→ CloudFormation
→ AWS Resources
| Task | Service |
|---|---|
| CI/CD | AWS CodePipeline |
| Infra creation | AWS CloudFormation |
📌 CloudFormation builds infra
📌 CI/CD deploys code
🎯 C) INTERVIEW CHEAT SHEET (SAVE THIS)
🔑 COMPUTE — Which & Why?
| Scenario | Answer |
|---|---|
| Need VM | EC2 |
| Need serverless Python | Lambda |
| Need containers | ECS |
| Need Spark | Glue / EMR |
| No cluster mgmt | EMR Serverless |
🔑 ORCHESTRATION — Which & Why?
| Scenario | Answer |
|---|---|
| Simple flow | Step Functions |
| Complex DAG | MWAA (Airflow) |
🔑 STORAGE — Which & Why?
| Data | Answer |
|---|---|
| Data Lake | S3 |
| Disk | EBS |
| Shared FS | EFS |
| Archive | Glacier |
🔑 CI/CD vs IaC (CONFUSION KILLER)
| Topic | Truth |
|---|---|
| CloudFormation | Creates infra |
| CodePipeline | Moves code |
| Terraform | Multi-cloud IaC |
🔑 ROLES — FINAL CLARITY
IAM User → Human
IAM Role → AWS Service
Policy → Permission
Example:
EMR Role → S3 access
Lambda Role → Glue start permission
🧠 FINAL ONE-PAGE AWS MAP (MEMORIZE)
IDENTITY → IAM
COMPUTE → EC2 | Lambda | EMR | ECS
STORAGE → S3 | EBS | EFS
DATABASE → RDS | DynamoDB
ORCHESTRATION → Step Functions | Airflow
CI/CD → CodePipeline
IaC → CloudFormation
MONITORING → CloudWatch
🚀 WHAT I SUGGEST NEXT (VERY EFFECTIVE)
Option 1️⃣
👉 Build ONE end-to-end architecture diagram (I’ll guide you step-by-step)
Option 2️⃣
👉 Convert this into a 30-day AWS mastery plan
Option 3️⃣
👉 Interview-only AWS service selection questions (50+)
Reply with 1 / 2 / 3 (or combination).
After this, AWS will feel obvious, not confusing 💯
