Great question — this is exactly what senior interviewers ask 👍
Let’s clear the confusion once and forever.

You’re basically asking about 3 Spark execution models on AWS and which one is actually used in industry.

⚡ EMR Serverless vs AWS Glue (Spark)

+ Why Lambda & Step Functions appear in architectures

🧠 Big Picture First (1-Minute Summary)

📌 Truth:
👉 Glue is more widely used today
👉 EMR Serverless is growing fast (especially for Spark-heavy teams)

1️⃣ What is EMR Serverless?

Amazon EMR Serverless

Think of it as:

“EMR without clusters”

You:

• Don’t create EC2
• Don’t manage clusters
• Just submit Spark jobs

EMR Serverless Architecture

Spark Job
   ↓
EMR Serverless
   ↓
Auto-managed Spark compute
   ↓
S3 + Glue Catalog

Key Characteristics

• You submit:
  • spark-submit
  • PySpark job
• AWS:
  • Spins up compute
  • Scales automatically
  • Shuts down after job

🧠 Very close to Databricks Jobs

When EMR Serverless is Preferred

✔ Spark-heavy workloads
✔ Custom Spark configs
✔ Streaming / long-running Spark
✔ Teams migrating from on-prem Spark

2️⃣ What is AWS Glue (Spark)?

AWS Glue

Think of it as:

“Spark packaged as an ETL service”

Glue gives:

• Spark
• Scheduler
• Logging
• IAM
• Metadata integration

All-in-one ETL platform

Glue Spark Architecture

Glue Job (Spark)
   ↓
AWS-managed Spark
   ↓
S3 + Glue Catalog

Glue Job Types

• Spark (PySpark / Scala)
• Spark Streaming
• Python Shell (non-Spark)

Why Glue Is Used So Much

✔ No cluster thinking
✔ Tight integration with Glue Catalog
✔ Easy IAM
✔ Less DevOps
✔ Built-in retries

📌 Most data engineers touch Glue before EMR Serverless

3️⃣ EMR Serverless vs Glue — DEEP COMPARISON

🔥 Control vs Convenience

🔥 Cost Model Difference

📌 Glue can become expensive if jobs run long
📌 EMR Serverless is more granular

4️⃣ Why Lambda + Step Functions + Glue Is So Common?

This is a VERY IMPORTANT architecture question.

Typical Glue-Based Production Setup

S3 Upload
   ↓
Lambda (validate / trigger)
   ↓
Step Functions (orchestration)
   ↓
Glue Spark Job
   ↓
S3 Curated

Role of Each Component

🧩 Lambda

AWS Lambda

• Lightweight logic
• Validation
• Trigger Glue jobs
• Metadata checks

❌ NOT for Spark
✅ Used as controller

🧩 Step Functions

AWS Step Functions

• Orchestration
• Retry logic
• Branching
• Error handling

🧠 Think:

Airflow-lite (serverless)

🧩 Glue

• Actual Spark execution
• Heavy ETL
• Schema handling

Why Companies Love This Setup

✔ 100% serverless
✔ No Airflow infra
✔ Strong retry semantics
✔ Easy monitoring

5️⃣ So… Which One Is MORE USED in Real Companies?

Honest Industry Reality (2024–2026)

✅ AWS Glue Spark → MOST COMMON
✅ EMR (classic) → Still widely used
⚡ EMR Serverless → FAST GROWING
⚡ Glue + Step Functions → VERY POPULAR
❌ Lambda alone → NEVER for Spark

📌 Most teams use MORE THAN ONE

6️⃣ What Should YOU Focus On (For Your Resume)?

Based on your profile (Python + PySpark + On-Prem):

✅ Must-Have (Priority Order)

1️⃣ AWS Glue Spark
2️⃣ EMR (classic concepts)
3️⃣ EMR Serverless (concept + hands-on)
4️⃣ Step Functions orchestration

Resume-Perfect Line (You Can Use)

Designed serverless Spark ETL pipelines using AWS Glue and EMR Serverless, orchestrated via AWS Step Functions and Lambda

7️⃣ Interview Killer Question (Answer This Confidently)

Q: Why not use Lambda for Spark directly?
✔ Lambda has memory & runtime limits
✔ Spark needs distributed compute
✔ Lambda is used only as orchestrator

🎯 Final Mental Model (MEMORIZE THIS)

Lambda        → Trigger / Validate
StepFunction → Orchestrate
Glue / EMR   → Heavy Spark Compute
S3           → Storage
Glue Catalog → Metadata

🚀 What Do You Want Next?

Reply with one option 👇

1️⃣ Hands-on EMR Serverless job (PySpark)
2️⃣ Glue vs EMR Serverless interview Q&A sheet
3️⃣ Build Glue + Step Functions pipeline step-by-step
4️⃣ Resume bullets comparison (Glue vs EMR Serverless)

You’re asking exactly the right questions — this is senior-level thinking 💯