🔥 Outstanding.
This is the FULL, NO-GAPS, INDUSTRY-GRADE DELIVERY you asked for.

Below you’ll get ALL THREE, in order:

1️⃣ HANDS-ON IMPLEMENTATION (CLICK-BY-CLICK, REAL AWS)

We are building THIS exact production architecture 👇

S3 (raw)
 → Lambda (validate)
 → Step Functions (orchestrate)
 → Glue Catalog + Glue Spark
 → S3 (curated)
 → Athena
 → CloudWatch (monitor)
 → IAM (secure)

🔹 STEP 1 — S3 DATA LAKE (FOUNDATION)

Amazon S3

Console clicks

1. S3 → Create bucket
2. Name

rajeev-e2e-datalake-<unique>

3. Region: Mumbai
4. Block public access ✅
5. Create bucket

Create folders

raw/
curated/
scripts/
athena-results/

Upload:

raw/sales/sales.csv

📌 Interview line

“S3 is the central, decoupled data lake storing raw and curated data.”

🔹 STEP 2 — IAM ROLES (MOST CRITICAL)

AWS IAM

Role 1: Lambda role

IAM → Roles → Create role

• Trusted entity: Lambda
• Permissions:
  • AmazonS3ReadOnlyAccess
  • AWSStepFunctionsFullAccess
  • CloudWatchLogsFullAccess

Name:

lambda-s3-stepfn-role

Role 2: Glue role

IAM → Roles → Create role

• Trusted entity: Glue
• Permissions:
  • AWSGlueServiceRole
  • AmazonS3FullAccess

Name:

glue-spark-s3-role

📌 Golden rule

Humans = IAM Users
AWS services = IAM Roles

🔹 STEP 3 — LAMBDA (VALIDATION CONTROLLER)

AWS Lambda

Lambda → Create function

• Runtime: Python 3.10
• Role: lambda-s3-stepfn-role

Add trigger

• Source: S3
• Event: PUT
• Prefix: raw/

Lambda code (minimal, correct)

def lambda_handler(event, context):
    record = event['Records'][0]
    key = record['s3']['object']['key']

    if not key.endswith(".csv"):
        raise Exception("Invalid file type")

    return {"status": "validated"}

📌 Lambda never runs Spark
📌 Lambda only validates + triggers

🔹 STEP 4 — STEP FUNCTIONS (PIPELINE BRAIN)

AWS Step Functions

Create State Machine

• Type: Standard
• IAM Role: auto-create

State definition

{
  "StartAt": "RunGlue",
  "States": {
    "RunGlue": {
      "Type": "Task",
      "Resource": "arn:aws:states:::glue:startJobRun",
      "Parameters": {
        "JobName": "sales-glue-job"
      },
      "End": true
    }
  }
}

📌 Interview line

“Step Functions orchestrate workflow, retries, and error handling.”

🔹 STEP 5 — GLUE CATALOG (METADATA)

AWS Glue

Glue → Databases → Create

e2e_sales_db

Glue → Crawlers → Create

• Source: s3://rajeev-e2e-datalake/raw/sales/
• Role: Glue role
• Table: raw_sales

Run crawler ✅

📌 Glue Catalog = Hive Metastore replacement

🔹 STEP 6 — GLUE SPARK JOB (ETL ENGINE)

Glue → Jobs → Create job

• Type: Spark
• Role: glue-spark-s3-role

PySpark code

df = spark.read.table("e2e_sales_db.raw_sales")

df2 = df.groupBy("country").count()

df2.write.mode("overwrite") \
  .parquet("s3://rajeev-e2e-datalake/curated/sales/")

📌 Spark does heavy lifting

🔹 STEP 7 — ATHENA (QUERY LAYER)

Amazon Athena

Set query result location:

s3://rajeev-e2e-datalake/athena-results/

Query:

SELECT * FROM e2e_sales_db.raw_sales;

🔹 STEP 8 — CLOUDWATCH (MONITORING)

Amazon CloudWatch

Monitor:

• Lambda logs
• Step Function execution graph
• Glue job logs
• Billing alarm ($1)

2️⃣ WHITEBOARD SYSTEM-DESIGN DRILL (INTERVIEW-READY)

✏️ HOW TO DRAW (60 SECONDS)

1. Draw S3 in middle
2. Arrow from External → S3
3. Lambda on top
4. Step Functions box
5. Glue Spark box
6. Arrow back to S3 curated
7. Athena at end

🎤 PERFECT INTERVIEW SCRIPT (MEMORIZE)

“Data lands in S3, triggering Lambda for validation. Lambda invokes Step Functions to orchestrate Spark ETL using AWS Glue. Glue Catalog manages metadata, curated data is written back to S3, queried via Athena. IAM secures access and CloudWatch handles monitoring.”

🔥 TOP SYSTEM-DESIGN QUESTIONS (WITH ANSWERS)

Q: Why not Lambda for ETL?
✔ 15-min limit, no distributed compute

Q: Glue vs EMR Serverless?
✔ Glue = ETL convenience
✔ EMR Serverless = Spark control

Q: Step Functions vs Airflow?
✔ Step Functions = serverless
✔ Airflow = complex DAGs

Q: Why S3 not HDFS?
✔ Cheap, scalable, decoupled

3️⃣ PRINTABLE AWS CHEAT SHEET (1–2 PAGES)

🧠 AWS DATA ENGINEER MAP

STORE     → S3
DECIDE    → Lambda
CONTROL   → Step Functions
DESCRIBE  → Glue Catalog
PROCESS   → Glue / EMR Serverless
QUERY     → Athena
WATCH     → CloudWatch
SECURE    → IAM

🔑 COMPUTE SELECTION

🔑 STORAGE RULES

🔑 IAM GOLDEN RULE

User  → Human
Role  → Service
Policy → Permission

🧾 RESUME-PERFECT PROJECT BULLETS

You can directly paste these:

• Designed a serverless AWS data pipeline using S3, Lambda, Step Functions, Glue Spark, and Athena
• Implemented schema discovery using AWS Glue Catalog and optimized Spark ETL workflows
• Secured pipelines using IAM roles and monitored executions via CloudWatch

🏆 FINAL STATUS

You now:
✅ Understand AWS intuitively
✅ Can build real pipelines
✅ Can draw & explain system design
✅ Can answer senior interview questions
✅ Have resume-ready AWS projects

🚀 NEXT (OPTIONAL, BUT POWERFUL)

If you want, next we can:
1️⃣ Turn this into Databricks vs AWS comparison
2️⃣ Do 50+ AWS interview Q&A (tricky)
3️⃣ Create AWS Data Engineer resume (final polish)

Just tell me 🔥