Perfect — this is the right next step after AWS foundations.
What you’re asking for is essentially:

“Become a senior PySpark Data Engineer who runs Spark correctly on AWS.”

Below is a 30-DAY PYSPARK MASTERY ON AWS course that is:

• 🔥 Master-level (not beginner)
• ☁️ AWS-first (EMR, Glue, S3, IAM)
• 🧠 Conceptual + internal mechanics
• 🏗️ Real production patterns
• 🎯 Interview + resume aligned

This is the same depth expected from L4/L5 Data Engineers in big tech.

🧠 PYSPARK ON AWS — MASTER REFERENCE ARCHITECTURE

S3 (Raw)
 → Spark (Glue / EMR / EMR Serverless)
 → S3 (Cleansed / Curated)
 → Glue Catalog
 → Athena / Downstream
 → Orchestration (Step Functions / Airflow)
 → Monitoring (CloudWatch)

Every topic below maps to this.

🏆 30-DAY PYSPARK MASTERY ON AWS

🔹 WEEK 1 — SPARK CORE FOUNDATIONS (ENGINEER, NOT USER)

Day 1 — Spark Mental Model (CRITICAL)

Master Concepts

• What Spark actually is (driver, executor, cluster manager)
• Why Spark replaced MapReduce
• Lazy evaluation & DAG

AWS Mapping

• Driver → EMR master
• Executors → EMR core/task nodes
• Storage → S3 (not HDFS)

Real Life

• Why jobs “do nothing” until action
• Why bad DAGs cause slowness

Day 2 — Spark Architecture Deep Dive

Master

• Driver lifecycle
• Executor JVMs
• Task scheduling
• Stages vs tasks

Interview Gold

“Explain how Spark executes a job end to end.”

Day 3 — RDDs (Yes, You MUST Know Them)

Master

• What RDDs really are
• Lineage & fault tolerance
• Narrow vs wide dependencies

Real Life

• Debugging shuffle failures
• Why recomputation happens

Day 4 — DataFrames & Spark SQL Internals

Master

• Logical vs physical plan
• Catalyst optimizer
• Tungsten engine

AWS Context

• Why Glue Spark behaves differently than local Spark

Day 5 — Actions, Transformations & Lazy Eval

Master

• Why .count() is dangerous
• Action explosion problem

Production Mistake

• Accidental multiple actions = cost blowup on EMR

Day 6 — Spark Session, Configs & Runtime

Master

• SparkSession internals
• Spark configs hierarchy
• Glue vs EMR Spark defaults

Day 7 — WEEK 1 CONSOLIDATION

• Draw Spark execution diagram
• Explain Spark without code
• Debug a fake failed job mentally

🔹 WEEK 2 — DATA ENGINEERING WITH PYSPARK (REAL WORK)

Day 8 — Reading & Writing Data (AWS Style)

Master

• CSV vs JSON vs Parquet vs ORC
• Schema inference vs explicit schema
• S3 consistency & listing

Real Life

• Why Parquet is mandatory on S3

Day 9 — Schema Design & Evolution

Master

• StructType deep dive
• Nullable vs non-nullable
• Schema drift handling

AWS Glue

• How Glue Catalog interacts with Spark

Day 10 — Joins (MOST FAILED INTERVIEW TOPIC)

Master

• Broadcast vs shuffle joins
• Skew handling
• Join hints

AWS Reality

• S3 + skew = disaster if ignored

Day 11 — Aggregations & Window Functions

Master

• GroupBy internals
• Window vs aggregation tradeoffs

Real Life

• Slowly changing dimensions (SCDs)

Day 12 — Partitioning & Bucketing

Master

• Partition pruning
• Write vs read tradeoffs
• Small file problem

AWS Cost Impact

• Bad partitions = high Athena + EMR cost

Day 13 — Performance Tuning (CORE SENIOR SKILL)

Master

• Shuffle tuning
• Executor memory
• Parallelism

Interview Gold

“How would you optimize a slow Spark job?”

Day 14 — WEEK 2 PROJECT

🎯 Build Production ETL

S3 Raw → Spark Transform → Parquet → S3 Curated

🔹 WEEK 3 — PYSPARK ON AWS (EMR, GLUE, SERVERLESS)

Day 15 — Spark on AWS Glue

AWS Glue

Master

• Glue DPUs
• Job bookmarks
• Glue vs EMR Spark configs

Real Life

• Incremental ETL pipelines

Day 16 — Spark on EMR (Classic)

Amazon EMR

Master

• EMR architecture
• Steps
• Bootstrap actions

Day 17 — EMR Serverless

Amazon EMR Serverless

Master

• Application model
• Cost calculation
• When to prefer over Glue

Day 18 — Spark + S3 Internals

Master

• S3A connector
• Committers
• Rename problem

Interview Trap

“Why is rename expensive on S3?”

Day 19 — Orchestrating Spark

Master

• EMR steps
• Step Functions + Glue
• Airflow operators

Day 20 — Error Handling & Idempotency

Master

• Retry-safe Spark jobs
• Exactly-once patterns
• Reprocessing strategies

Day 21 — WEEK 3 PROJECT

🎯 Serverless Spark Pipeline

S3 → Lambda → Step Functions → Glue Spark → S3

🔹 WEEK 4 — ARCHITECT & INTERVIEW LEVEL

Day 22 — Logging & Monitoring Spark

Master

• Spark UI
• CloudWatch logs
• Memory leak detection

Day 23 — Cost Optimization (AWS + Spark)

Master

• EMR ephemeral clusters
• Spot instances
• S3 lifecycle

Day 24 — Security for Spark Pipelines

Master

• IAM roles
• Encryption
• Data access control

Day 25 — Data Lake Architecture

Master

• Raw / Cleansed / Curated
• Lakehouse concepts
• Glue governance

Day 26 — Migration from On-Prem Spark

Master

• HDFS → S3
• Hive → Glue
• Pain points & solutions

Day 27 — CAPSTONE PROJECT

🎯 Enterprise-Grade Spark Platform on AWS

Features:

• Secure
• Scalable
• Cost-aware
• Monitored

Day 28 — Resume Engineering (Spark + AWS)

• Convert projects to bullets
• STAR format
• Metrics & scale

Day 29 — Spark Interview Mastery

• 50+ senior questions
• Failure scenarios
• Whiteboard flows

Day 30 — ARCHITECT MODE

• Design Spark system from scratch
• Choose Glue vs EMR vs EMR Serverless
• Justify every decision

🏆 FINAL STATE AFTER 30 DAYS

You will be able to say:

✔ I understand Spark internals
✔ I tune Spark for performance & cost
✔ I run Spark correctly on AWS
✔ I design production data platforms
✔ I answer senior-level interviews confidently

🚀 NEXT (VERY IMPORTANT)

If you want, we can now:

1️⃣ Start Day-by-Day PySpark tutorials (Day 1 tomorrow)
2️⃣ Build real AWS Spark architecture hands-on
3️⃣ Convert this into whiteboard system-design drills
4️⃣ Create a 1–2 page printable PySpark + AWS cheat sheet

Reply with:

START DAY 1

and we begin immediately 🔥