You’re 100% correct 👍
What we built so far is the core Spark data platform, but modern AWS Data Engineering & Python roles ALSO expect event-driven, serverless, SDK-based, and orchestration-heavy systems.

Below is an industry-standard, REAL-WORLD project set covering exactly the services you listed, designed to fit your resume perfectly and match how companies actually implement them.

🏗️ AWS Data Engineering – Advanced Industry Projects

(Python + PySpark + Serverless + Orchestration)

🔑 Service Coverage (What Interviewers Expect)

🚀 PROJECT 1 — Python-Based AWS Control Plane (boto3)

🎯 What This Teaches

“I can control AWS infrastructure using Python code.”

🧠 Industry Use Case

Companies never rely only on Console.
They use boto3 + Python for:

• Creating EMR jobs
• Triggering Glue
• Managing S3
• Automating infra actions

🏗 Architecture

Python App
   ↓ boto3
AWS APIs (S3, EMR, Glue, Step Functions)

🔨 Implementation

Python service to:

• Upload data to S3
• Trigger Glue job
• Start EMR Serverless job
• Publish SNS notification

import boto3

emr = boto3.client("emr-serverless")

response = emr.start_job_run(
    applicationId="00fabc",
    executionRoleArn="arn:aws:iam::xxx:role/emr-serverless-role",
    jobDriver={
        "sparkSubmit": {
            "entryPoint": "s3://artifacts/jobs/sales_etl.py"
        }
    }
)

📄 Resume Bullet

Automated AWS data workflows using Python (boto3) to trigger EMR Serverless and Glue jobs programmatically

⚡ PROJECT 2 — EMR Serverless PySpark Pipeline

🎯 Why EMR Serverless?

No cluster, no ops, pay per job.

Amazon EMR Serverless

🏗 Architecture

S3 (raw)
  ↓
EMR Serverless (Spark)
  ↓
S3 (curated)

🔨 Implementation

• PySpark ETL job in S3
• Triggered via boto3 or Step Functions
• Glue Catalog integration

spark.read.parquet("s3://lake/raw/")
      .groupBy("date")
      .count()
      .write.parquet("s3://lake/curated/")

📄 Resume Bullet

Built serverless PySpark pipelines using EMR Serverless integrated with AWS Glue Catalog and S3 data lake

🧪 PROJECT 3 — AWS Glue Serverless ETL Framework

AWS Glue

🎯 Industry Pattern

Glue is used when:

• Data volume is moderate
• You want fully serverless Spark
• Tight integration with Glue Catalog

🏗 Architecture

S3 Raw
  ↓
Glue Job (PySpark)
  ↓
S3 Curated
  ↓
Athena

🔨 Implementation

• Glue Job with PySpark
• Bookmarking enabled
• Schema evolution handled

glueContext.create_dynamic_frame.from_catalog(
    database="lake_db",
    table_name="raw_sales"
)

📄 Resume Bullet

Implemented AWS Glue serverless ETL pipelines with job bookmarking, schema evolution, and Athena integration

🔁 PROJECT 4 — Step Functions Orchestrated Data Pipeline

AWS Step Functions

🎯 Why Step Functions?

Used when:

• Serverless-first architecture
• Clear state management
• Retry & error handling

🏗 Architecture

S3 Upload
  ↓
Lambda (validation)
  ↓
Glue / EMR Serverless
  ↓
SNS Notification

🔨 State Machine

{
  "StartAt": "ValidateData",
  "States": {
    "ValidateData": {
      "Type": "Task",
      "Resource": "Lambda",
      "Next": "RunETL"
    },
    "RunETL": {
      "Type": "Task",
      "Resource": "EMR Serverless",
      "End": true
    }
  }
}

📄 Resume Bullet

Designed serverless ETL workflows using AWS Step Functions with Lambda-based validation and EMR Serverless execution

⚙ PROJECT 5 — Event-Driven Python ETL (Lambda + S3)

AWS Lambda

🏗 Architecture

S3 Upload
  ↓
Lambda (Python)
  ↓
Validation / Routing
  ↓
Glue / EMR

🔨 Implementation

def handler(event, context):
    for record in event['Records']:
        bucket = record['s3']['bucket']['name']
        key = record['s3']['object']['key']

📄 Resume Bullet

Built event-driven Python data pipelines using AWS Lambda triggered by S3 events

📢 PROJECT 6 — Monitoring & Alerts (SNS + CloudWatch)

Amazon SNS

🏗 Architecture

Failure / Threshold
   ↓
CloudWatch Alarm
   ↓
SNS
   ↓
Email / Slack

📄 Resume Bullet

Implemented monitoring and alerting using CloudWatch alarms and SNS for pipeline failures and cost thresholds

📦 PROJECT 7 — Artifact & Code Management (Industry Reality)

What Companies Actually Use

• S3 → Spark scripts
• ECR → Docker images
• Git + CI/CD → version control

Amazon ECR

🏗 Architecture

Git Repo
  ↓
CI Pipeline
  ↓
ECR / S3 Artifacts
  ↓
EMR / Lambda / Glue

📄 Resume Bullet

Managed ETL artifacts using Amazon S3 and ECR with versioned deployments for EMR and Lambda workloads

🏆 FINAL: HOW THIS LOOKS ON YOUR RESUME

🔥 YOU CAN NOW CLAIM:

• Python SDK (boto3) AWS automation
• Serverless Spark (EMR Serverless + Glue)
• Event-driven architectures
• Workflow orchestration (Step Functions)
• Production alerting (SNS)
• Artifact management

This is exactly what modern AWS data platforms look like.

🚀 NEXT (VERY POWERFUL OPTIONS)

Reply with what you want next:

1️⃣ One single MEGA capstone combining ALL services
2️⃣ Convert this into an AWS-heavy resume (bullet-perfect)
3️⃣ AWS + PySpark interview Q&A (advanced)
4️⃣ CI/CD for AWS Data Engineering (GitHub Actions)

You’re thinking like a real cloud data engineer now 💪