Perfect 👍
Now we enter the most important service for a PySpark Data Engineer on AWS.

From this lesson onward, everything you do will revolve around S3.

📦 LESSON 3 — Amazon S3 for Python & PySpark Developers

(HDFS → S3 | Hands-on + Resume-ready)

Amazon S3

🎯 Lesson Outcome

By the end of this lesson, you will:

• Understand how S3 really works (not just clicks)
• Design a production-grade data lake
• Upload & manage data
• Access S3 using Python
• Know interview-level S3 concepts

🧠 First: Stop Thinking of S3 as HDFS

🔥 This is CRITICAL

📌 S3 is NOT a file system — Spark talks to it via APIs

🧩 S3 Architecture (Mental Model)

Bucket
 ├── raw/
 ├── cleansed/
 ├── curated/

No folders — just prefixes

1️⃣ Create Your First S3 Bucket (Hands-On)

🔹 Go to:

AWS Console → S3 → Create bucket

🔹 Bucket Name (VERY IMPORTANT RULES)

Bucket names are globally unique

Use:

rajeev-data-lake-<your-unique-number>

Example:

rajeev-data-lake-2026

📌 Use lowercase, no spaces

🔹 Region

👉 Asia Pacific (Mumbai)

🔹 Settings (IMPORTANT)

• ❌ Block Public Access → KEEP ENABLED
• ❌ Versioning → OFF (for now)
• Encryption → Default (SSE-S3)

Create bucket ✅

🧪 TASK 1 (Reply Required)

Confirm:

Bucket created: YES
Bucket name:

2️⃣ Design Data Lake Structure (Industry Standard)

Inside your bucket, create prefixes:

raw/
cleansed/
curated/

📌 This is used in real companies

Example:

s3://rajeev-data-lake-2026/raw/sales/
s3://rajeev-data-lake-2026/cleansed/sales/
s3://rajeev-data-lake-2026/curated/sales/

3️⃣ Upload Sample Data

Use ANY CSV (or create one)

Example sales.csv:

order_id,amount,country
1,500,IN
2,800,US
3,200,IN

Upload to:

raw/sales/sales.csv

🧪 TASK 2 (Reply Required)

Confirm:

Data uploaded: YES
Object path:

4️⃣ Access S3 Using Python (VERY IMPORTANT)

We’ll use boto3 (AWS Python SDK).

import boto3

s3 = boto3.client('s3')

response = s3.list_objects_v2(
    Bucket='rajeev-data-lake-2026',
    Prefix='raw/sales/'
)

for obj in response.get('Contents', []):
    print(obj['Key'])

🧠 Why this matters:

• Glue
• Lambda
• EMR
  ALL use this internally

5️⃣ S3 Interview Gold (REMEMBER THESE)

🔑 Key Concepts

• Strong consistency
• Object immutability
• Multipart upload
• Lifecycle policies
• Partition-friendly design

❌ Common Mistakes

• Using S3 like HDFS
• Too many small files
• Renaming objects frequently

🧾 Resume Bullets You Earn

Designed AWS S3 data lake with raw, cleansed, and curated layers
Integrated S3 with Python using boto3 for data access and validation

⏭ NEXT LESSON (VERY IMPORTANT)

📚 LESSON 4 — AWS Glue Catalog (Hive Metastore Replacement)

You will:

• Create Glue database
• Run Glue crawler
• Understand schema evolution
• Query via Athena

AWS Glue is core to your resume

🔔 Before Moving On (Quick Check)

Reply with:

Bucket created:
Data uploaded:

Once confirmed, we jump straight into Lesson 4 (Glue Catalog) 🚀