Below is a true A→Z AWS for Data Engineer (PySpark) Mastery Course — not a syllabus, but a PhD-level learning system with architecture, internals, performance, edge cases, traps, real datasets, and interview mastery.
This is designed specifically for you (PySpark + Big Data + AWS + Interviews) and aligns with your goal of deep conceptual + practical mastery.
🧠 AWS for Data Engineer (PySpark) — PhD-Level Mastery Course
Goal: Become a top-tier AWS Data Engineer who understands
✅ AWS internals
✅ Big Data architecture
✅ Spark on AWS
✅ Performance & scaling
✅ Failure modes
✅ Interview traps
✅ Real-world systems
Think of this as “Data Engineering + AWS + Spark + Distributed Systems + Cloud Architecture” combined.
🧱 PART 0 — FOUNDATIONS (Mandatory for PhD-level understanding)
0.1 Distributed Systems Core (Non-negotiable)
You must master these before AWS:
Concepts
- CAP theorem
- Consistency models (strong, eventual, causal)
- Consensus (Raft, Paxos)
- Partitioning vs Sharding
- Replication strategies
- Leader-follower vs leaderless systems
- Data locality
- Fault tolerance
- Backpressure
- Idempotency
- Exactly-once vs at-least-once
- Latency vs throughput
- Horizontal vs vertical scaling
Interview traps
❓ Why is Spark fast but Hadoop slow?
❓ Why is S3 eventually consistent but HDFS strongly consistent?
❓ Why is Kafka not a database?
0.2 Linux + Networking for Data Engineers
Must-master topics
- TCP/IP, DNS, HTTP, HTTPS
- Ports, sockets
- NAT, Load balancer
- SSH, SCP, rsync
- Linux I/O, memory, processes
- Disk types: HDD, SSD, NVMe
- Ephemeral vs persistent storage
Real exercise
Simulate a distributed cluster using Docker + Spark.
☁️ PART 1 — AWS FUNDAMENTALS (Cloud Internals)
1.1 AWS Global Architecture
- Regions vs AZs vs Edge locations
- Control plane vs data plane
- Shared responsibility model
- Pricing model (hidden costs)
Deep Insight
👉 AWS is not just services — it’s a distributed OS.
Interview traps
❓ Why are multiple AZs needed inside one region?
❓ Why not store data only in EC2 instead of S3?
1.2 AWS Networking (PhD Level)
VPC Deep Dive
- CIDR blocks
- Subnets (public/private)
- Route tables
- Internet Gateway vs NAT Gateway
- Security Groups vs NACLs
- VPC Peering vs Transit Gateway
- PrivateLink
- ENI
Real Lab
Build a private Spark cluster with:
- No public IP
- S3 access via VPC endpoint
- Bastion host
Interview traps
❓ Why can EMR access S3 without public internet?
❓ Difference between SG and NACL in real-world failures?
🧱 PART 2 — LAYER 1: STORAGE (Data Engineer Perspective)
2.1 S3 — The Heart of AWS Big Data
Not just “object storage”
Learn S3 as a distributed filesystem:
Architecture
- Buckets, objects, prefixes
- Partitioning by key
- Metadata vs data
- Consistency model
- Multipart upload
- Storage classes
Performance engineering
- Small files problem
- Partition design
- Prefix sharding
- Throughput limits
- Request rate limits
Data Engineering patterns
- Data Lake architecture
- Bronze / Silver / Gold layers
- Delta / Iceberg / Hudi on S3
- Parquet vs ORC vs Avro
Live dataset
Use:
- NYC Taxi dataset (100GB)
- Amazon reviews dataset
- Wikipedia dumps
PySpark Example
df = spark.read.parquet("s3://data-lake/bronze/nyc/")
df.groupBy("pickup_zone").count().show()
Interview traps
❓ Why does Spark job slow down when reading from S3?
❓ Why is HDFS faster than S3?
❓ How to optimize S3 for Spark?
2.2 HDFS on AWS (EMR HDFS)
HDFS Architecture
- NameNode
- DataNode
- Secondary NameNode
- Block size
- Replication factor
- Rack awareness
Compare HDFS vs S3
| Feature | HDFS | S3 |
|---|---|---|
| Consistency | Strong | Eventual |
| Latency | Low | Higher |
| Cost | High | Low |
| Scalability | Limited | Infinite |
Interview trap
❓ Why does Hadoop prefer HDFS but modern systems prefer S3?
2.3 EFS & FSx
- POSIX file systems
- When Spark needs shared file systems
- Lustre vs Windows FSx
2.4 Glacier
- Cold data strategy
- Data lifecycle policies
- Compliance use cases
⚙️ PART 3 — LAYER 2: COMPUTE (Big Data Perspective)
3.1 EC2 — Beyond Basics
Topics
- Instance families (C, M, R, I)
- Spot vs On-demand vs Reserved
- Auto Scaling
- Placement groups
- Nitro hypervisor
- Ephemeral storage
Data Engineering Insight
👉 Choosing wrong EC2 type can make Spark 5x slower.
Interview traps
❓ Why use r5 instead of c5 for Spark?
❓ Why Spot instances are dangerous for Spark?
3.2 EMR — Hadoop & Spark on AWS (Core Module)
EMR Architecture
- Master node
- Core nodes
- Task nodes
- YARN
- HDFS on EMR
Spark on EMR Deep Dive
- Driver vs Executors
- YARN vs Standalone
- Dynamic allocation
- Shuffle service
- Serialization (Kryo vs Java)
Real experiment
Run Spark job with:
- 1 executor vs 50 executors
- Measure shuffle time
Interview traps
❓ Why Spark job fails only on EMR but works locally?
❓ Why executors die randomly?
3.3 AWS Glue — Serverless Spark
Architecture
- Glue Jobs
- Crawlers
- Data Catalog
- Glue Spark runtime
- DPUs
Glue vs EMR
| Aspect | EMR | Glue |
|---|---|---|
| Control | High | Low |
| Cost | Medium | High |
| Flexibility | High | Medium |
Interview trap
❓ Why Glue is slower than EMR for large Spark jobs?
3.4 Lambda & Fargate for Data Engineering
- Event-driven pipelines
- Micro-batch ingestion
- Lambda vs EC2 vs Fargate
🔥 PART 4 — LAYER 3: PROCESSING (Big Data Engines)
4.1 Spark on AWS — PhD Level
Spark Internals
- DAG
- RDD lineage
- Shuffle
- Partitioning
- Catalyst optimizer
- Tungsten engine
- AQE (Adaptive Query Execution)
Performance tuning
- Repartition vs coalesce
- Broadcast joins
- Skew handling
- Caching strategy
- Memory tuning
Real dataset challenge
Solve:
👉 “Top 1% customers by revenue in 1TB dataset”
Interview traps
❓ Why repartition sometimes slows down Spark?
❓ Why broadcast join fails in Glue?
4.2 Athena — Serverless SQL on S3
- Presto engine
- Partition pruning
- File format optimization
Trap
❓ Why Athena query is slow despite partitioning?
4.3 Redshift — Data Warehouse on AWS
Architecture
- Leader node
- Compute nodes
- Columnar storage
- Distribution styles
- Sort keys
Redshift vs Spark
When to use what?
4.4 Kinesis & Kafka on AWS
Streaming Architecture
- Producers
- Consumers
- Partitions
- Offsets
- Retention
Kinesis vs Kafka
| Feature | Kafka | Kinesis |
|---|---|---|
| Control | High | Low |
| Scaling | Manual | Auto |
| Cost | Lower | Higher |
Interview traps
❓ Why Kafka beats Kinesis in real-time analytics?
🔄 PART 5 — LAYER 4: ORCHESTRATION
5.1 Airflow on AWS
- DAGs
- Executors
- Scheduling
- Backfill
- Idempotency
Real pipeline
S3 → Glue → Redshift → Dashboard
5.2 Step Functions
- State machines
- Retry logic
- Failure handling
5.3 Glue Workflows
🔐 PART 6 — GOVERNANCE & SECURITY
6.1 IAM for Data Engineers
- Roles vs policies
- STS
- Cross-account access
- Least privilege
Trap
❓ Why Spark job fails due to IAM though code is correct?
6.2 Lake Formation
- Data governance
- Row/column-level security
6.3 CloudWatch & Monitoring
- Metrics
- Logs
- Alerts
🧬 PART 7 — BIG DATA ON AWS (Hadoop, MapReduce, Hive)
7.1 MapReduce on AWS (EMR)
MapReduce Flow
Input → Mapper → Shuffle → Reducer → Output
PySpark vs MapReduce
Why Spark killed MapReduce?
7.2 Hive on AWS
Hive Architecture
- Metastore
- Execution engines (MR, Tez, Spark)
- Partitioning & bucketing
Trap
❓ Why Hive query faster than Spark sometimes?
🧠 PART 8 — REAL-WORLD DATA ENGINEERING ARCHITECTURES
Architecture 1 — Modern Data Lake
S3 + Glue + Spark + Athena + Redshift + Airflow
Architecture 2 — Streaming Platform
Kafka/Kinesis + Spark Streaming + S3 + Redshift
Architecture 3 — Lakehouse
S3 + Delta/Iceberg + Spark + BI tools
🎯 PART 9 — INTERVIEW MASTER MODE
9.1 Hardcore Questions
AWS + Spark
- Why Spark on S3 is slower than HDFS?
- How to design a 10TB/day pipeline?
- How to handle skew in Spark on EMR?
System Design
Design:
- Netflix data platform
- Uber real-time analytics
- Flipkart recommendation engine
9.2 Interview Traps (Real)
❌ “S3 is a file system” → WRONG
❌ “Glue is just Spark” → WRONG
❌ “Spark always faster than Hive” → WRONG
❌ “Kafka is database” → WRONG
🧪 PART 10 — LIVE DATASETS (for Practice)
Use these for labs:
Open Big Data
- NYC Taxi Data (Parquet)
- Amazon Reviews
- Wikipedia dumps
- Twitter stream (Kafka)
- Kaggle datasets
I will give you:
✅ Spark coding tasks
✅ AWS architecture labs
✅ Performance experiments
✅ Interview questions
🚀 NEXT STEP (If you want)
I can build this into a structured PhD-level course like:
📘 Module 1 — AWS + Distributed Systems Deep Theory
📘 Module 2 — S3 + HDFS + Spark Internals (with real experiments)
📘 Module 3 — EMR + Glue + Performance Engineering
📘 Module 4 — Kafka + Kinesis + Streaming
📘 Module 5 — Real-world AWS Data Architectures
📘 Module 6 — Interview Killer Playbook
And we go extremely deep with:
- diagrams
- Spark execution simulation
- AWS architecture breakdown
- real PySpark code
- failure scenarios
- interview traps
If you say “Start Module 1 in hardcore mode”,
I will teach AWS + Distributed Systems at a level most engineers never reach.