Excellent. DAY 7 is where everything locks in.
Today we connect Days 1–6 into ONE mental model, do whiteboard-style thinking, and make sure there are no weak links.

📅 DAY 7 — WEEK 1 ARCHITECT REVIEW (FOUNDATION LOCK-IN)

From individual services → one coherent AWS system

🎯 DAY 7 GOAL

By the end of today, you will:

• See AWS as one connected platform, not isolated services
• Explain a complete AWS data architecture on a whiteboard
• Know why each service exists
• Be interview-ready on AWS fundamentals
• Be ready to move into core data engineering (Week 2)

🧠 PART 1 — THE ONE DIAGRAM YOU MUST REMEMBER

This diagram represents 80% of AWS data engineering systems.

Data Source
   ↓
S3 (Storage)
   ↓
Glue Catalog (Metadata)
   ↓
Spark (EMR / Glue)
   ↓
S3 Curated
   ↓
Athena / BI

🔑 Service roles (plain English)

• Amazon S3 → Holds data
• AWS Glue (Catalog) → Understands data
• Amazon EMR / Glue Spark → Processes data
• Athena → Queries data
• CloudWatch → Observes everything

📌 If this diagram makes sense → you understand AWS at foundation level

🧠 PART 2 — HOW DAYS 1–6 CONNECT (NO GAPS)

Day-by-Day Mapping

🧠 Architect insight

Every AWS issue is either IAM, Networking, or Wrong Service Choice.

🧠 PART 3 — WHITEBOARD EXPLANATION (INTERVIEW MODE)

🎤 Question:

“Explain a simple AWS data pipeline.”

✅ Your Answer (Out Loud):

“Data lands in S3, which acts as the data lake.
Glue Catalog stores metadata about that data.
Spark jobs running on EMR or Glue process the data and write curated outputs back to S3.
Athena is used for ad-hoc querying, and CloudWatch monitors jobs.
IAM roles control secure access between services.”

📌 This answer alone clears many interviews

🧠 PART 4 — SERVICE CONFUSION CHECK (VERY IMPORTANT)

Let’s eliminate remaining doubts.

❓ Why not use RDS for analytics?

• Not scalable
• Expensive
• Not designed for scans

❓ Why not Lambda for Spark?

• Memory/runtime limits
• No distributed compute

❓ Why Glue Catalog instead of Hive Metastore?

• Serverless
• Shared across services
• No infra to manage

🧠 PART 5 — REAL PRODUCTION FLOW (STEP-BY-STEP)

S3 Upload
 → Lambda (validate)
 → Step Functions (orchestrate)
 → Glue / EMR Spark
 → S3 Curated
 → Athena

• AWS Lambda → Lightweight control
• AWS Step Functions → Orchestration
• Spark → Heavy compute

📌 Lambda controls, Spark computes

🧠 PART 6 — COMMON INTERVIEW TRAPS (YOU NOW AVOID)

❌ “Glue stores data”
❌ “S3 is like HDFS”
❌ “IAM users should be used by services”
❌ “All AWS services need VPC”

✔ You now know the correct mental models

🧠 PART 7 — WEEK 1 MEMORY SNAPSHOT (SAVE THIS)

Cloud = Programmable Infra
IAM = Who / What
VPC = Private Network
EC2 = Compute Control
S3 = Data Lake
Glue = Metadata
RDS/DynamoDB = State

🧪 DAY 7 SELF-TEST (VERY IMPORTANT)

You should now be able to answer YES to all:

1. Can I explain IAM roles vs users clearly?
2. Do I know why EMR needs VPC but Glue doesn’t?
3. Do I know where data actually lives in AWS?
4. Can I draw a basic AWS data pipeline on a whiteboard?

If yes → you’re ready for Week 2

🚀 WHAT STARTS NEXT (WEEK 2 PREVIEW)

Week 2 = CORE DATA ENGINEERING POWER

We will go deep into:

• Glue Catalog internals
• Athena optimization
• Spark on Glue vs EMR
• EMR Serverless
• Real ETL patterns
• Cost-aware Spark design

⏭️ NEXT STEP

Reply with:

DAY 8

We move into Glue Catalog at MASTER LEVEL — where real data engineering begins 🔥