Perfect 👍
I’ll design this exactly like an industry mentoring program, not a boring AWS course.
This will be interactive, hands-on, PySpark-first, Python-first, and resume-oriented.
From this point onward, think of this as:
“AWS for On-Prem PySpark Data Engineers → Cloud Expert”
🧠 How This Tutorial Will Work (IMPORTANT)
🧩 Learning Style
Each lesson will have:
- Concept (WHY) – very short
- Mapping from On-Prem → AWS
- Hands-on task (you do)
- Mini interview insight
- Resume bullet you can claim
🛠 Tools You’ll Use
- AWS Free Tier
- AWS Console + CLI
- Python (boto3)
- PySpark
- EMR / Glue
- S3
🧭 Overall Roadmap (Preview)
| Phase | Outcome |
|---|---|
| Phase 1 | AWS fundamentals + IAM confidence |
| Phase 2 | S3 + Glue + PySpark |
| Phase 3 | Spark on EMR |
| Phase 4 | Orchestration + monitoring |
| Phase 5 | Production-grade AWS projects |
| Phase 6 | Resume + Interview mastery |
🚀 PROJECTS WE WILL BUILD (Resume-Ready)
🔥 Project 1: Cloud Data Lake (Core Resume Project)
Source Data (CSV/JSON)
→ S3 (raw / cleansed / curated)
→ Glue Catalog
→ PySpark on EMR
→ Athena Queries
Resume bullets
- Built AWS-based data lake using S3, Glue, EMR, and Athena
- Implemented PySpark ETL with partitioning and schema evolution
🔥 Project 2: On-Prem → AWS Migration Project
Local/HDFS Data
→ S3 Migration
→ Glue Catalog
→ EMR Spark Jobs
Resume bullets
- Migrated on-prem Hadoop workloads to AWS EMR with minimal downtime
🔥 Project 3: Serverless Python Data Pipeline
S3 Trigger → Lambda (Python)
→ Validation → Glue / S3
→ CloudWatch Logs
🔥 Project 4: Orchestrated ETL Pipeline
S3 → EMR PySpark → S3
MWAA (Airflow)
Monitoring + Retry
Now let’s START PROPERLY.
📘 LESSON 1 — AWS BASICS (FOUNDATION YOU CANNOT SKIP)
🎯 Lesson Goal
Understand:
- What AWS really is
- How it maps to your on-prem experience
- Create your first AWS account mental model
1️⃣ What Is AWS (In One Sentence)
AWS is on-demand infrastructure + managed services so you don’t manage hardware.
2️⃣ Core AWS Building Blocks (VERY IMPORTANT)
| On-Prem | AWS |
|---|---|
| Data Center | Region |
| Rack | Availability Zone |
| Physical Server | EC2 |
| HDFS | S3 |
| Hive Metastore | Glue Catalog |
| Spark Cluster | EMR |
| Firewall | Security Group |
3️⃣ AWS Regions & AZs (Visual Mental Model)
Key Rules
- Data does NOT move across regions automatically
- Always choose nearest region for cost & latency
4️⃣ AWS Account Structure
AWS Account
├── IAM Users
├── IAM Roles
├── Services (S3, EMR, Glue...)
📌 Golden Rule
Never use root user after setup
🧪 HANDS-ON TASK (DO THIS NOW)
Task 1: Open AWS Console
- Go to AWS Console
- Check:
- Current Region
- Services menu
👉 Reply with:
- Which region you see
- One service name you opened
💡 Interview Insight
“Explain AWS Region vs AZ”
✔ Region = geographical location
✔ AZ = isolated data center inside region
🧾 Resume Line You Earn (After This Phase)
Familiar with AWS global infrastructure, regions, and availability zones
🧠 Quick Check (Answer Mentally)
- Can AWS auto-move your data across regions?
- What replaces HDFS in AWS?
⏭ NEXT LESSON (After you confirm)
➡ Lesson 2: IAM Deep Dive (Critical for Data Engineers)
➡ You will create users, roles, policies (real hands-on)
⚠️ Before We Continue — One Small Question
This helps me tailor everything perfectly:
👉 Do you already have an AWS Free Tier account created?
(Yes / No — I’ll guide accordingly)
Once you reply, we continue immediately 🚀