Perfect. Now we switch to hardcore mode.
No shallow content. No shortcuts. No truncation.
We will build your brain like a cloud + distributed systems + Spark architect.

This is not AWS training.
This is how the cloud actually works under the hood.

🧠 MODULE 1 — AWS + DISTRIBUTED SYSTEMS FOUNDATIONS

“Think like AWS, Spark, and Hadoop engineers”

If you master this module, everything else becomes obvious.

1️⃣ What is AWS REALLY? (Not what tutorials tell you)

Most people think:

AWS = EC2 + S3 + Lambda + Glue

❌ WRONG.

✅ AWS is a GLOBAL DISTRIBUTED OPERATING SYSTEM.

Just like your laptop OS manages:

• CPU
• Memory
• Disk
• Network
• Processes

AWS manages the same things — but at planetary scale.

👉 AWS = Linux OS + Distributed Systems + Hardware abstraction + Billing engine.

2️⃣ Why Cloud Exists? (The REAL reason)

Before cloud, companies had data centers.

Traditional Data Center Problems

Imagine Flipkart in 2010:

• Traffic spikes on Big Billion Days
• Servers idle on normal days
• Buying servers takes months
• Hardware failures
• Scaling is manual
• Huge capital expense (CAPEX)

Cloud solved 3 fundamental problems:

(A) Elasticity

Scale up/down instantly.

(B) Pay-as-you-go

No upfront hardware.

(C) Global distribution

Data closer to users.

3️⃣ Distributed Systems — The Heart of AWS & Spark

To understand AWS, you must understand distributed systems.

3.1 What is a Distributed System?

A system where:

• Multiple machines
• Connected by network
• Work as a single system

Examples:

• Spark cluster
• Hadoop cluster
• AWS S3
• Kafka
• DynamoDB
• EMR

Key Problem:

👉 Network is unreliable.

So distributed systems must handle:

• Machine failures
• Network delays
• Data inconsistency
• Partial failures

4️⃣ The 3 Fundamental Laws of Distributed Systems

Law 1: Everything fails.

Servers fail.
Disks fail.
Networks fail.
Zones fail.
Regions fail.

Example in AWS:

• EC2 instance crashes
• EMR node dies
• S3 request fails
• AZ outage

👉 Therefore AWS is designed assuming failures.

Law 2: Network is slow and unreliable.

Latency exists.

Example:

Spark job reading data from S3.

• HDFS latency: ~1 ms
• S3 latency: ~10–100 ms

👉 That’s why Spark on S3 is slower than HDFS.

Law 3: Data consistency is expensive.

You cannot have:

• perfect consistency
• high availability
• partition tolerance

at the same time.

This leads to:

⚔️ CAP THEOREM

5️⃣ CAP Theorem — Core of AWS + Spark + Kafka + S3

CAP = Consistency, Availability, Partition Tolerance

In a distributed system, you can only guarantee 2 out of 3.

5.1 Definitions (Deep, not textbook)

Consistency (C)

All nodes see the same data at the same time.

Example:
If you write data, every read returns latest value.

Availability (A)

System always responds (even if data is stale).

Partition Tolerance (P)

System continues working even if network breaks.

5.2 CAP in Real Systems

HDFS (Hadoop)

• Consistency ✅
• Partition tolerance ✅
• Availability ❌

Why?
If NameNode fails → cluster stops.

👉 HDFS = CP system.

S3

• Availability ✅
• Partition tolerance ✅
• Consistency ❌ (historically eventual consistency)

👉 S3 = AP system.

Kafka

• Partition tolerance ✅
• Availability ✅
• Consistency ⚠️ (depends on config)

Kafka trades consistency for speed.

Spark

Spark is not storage. It depends on underlying system.

🔥 Interview Trap #1

❓ Why is S3 eventually consistent but HDFS strongly consistent?

Hardcore Answer:

Because S3 is designed for:

• global scale
• multi-region replication
• high availability

To achieve this, AWS sacrifices strict consistency.

HDFS is designed for:

• local cluster
• fewer nodes
• high consistency

6️⃣ Latency vs Throughput — The Most Misunderstood Concept

Latency

Time taken for one request.

Throughput

Amount of data processed per second.

Example:

Spark implication:

Spark on S3:

• High throughput
• Higher latency per request

👉 That’s why Spark reads data in large chunks.

7️⃣ Data Locality — Why Hadoop Was Invented

Before Hadoop:

• Data in storage
• Compute in separate servers
• Network bottleneck

Hadoop Idea:

👉 Move compute to data, not data to compute.

HDFS + MapReduce:

• Data stored in HDFS nodes
• MapReduce runs on same nodes

Spark on HDFS:

Executors run where data resides.

Spark on S3:

Data is remote → network overhead.

🔥 This is why EMR with HDFS is faster than Glue with S3.

8️⃣ MapReduce — Foundation of Spark and AWS Big Data

8.1 What is MapReduce?

A programming model for distributed processing.

Steps:

1. Input data split into blocks
2. Map function processes each block
3. Shuffle groups data by key
4. Reduce aggregates results

Example Dataset (Live)

Suppose we have logs:

user1,click
user2,view
user1,click
user3,click
user2,click

Goal:

Count clicks per user.

MAP phase:

(user1, 1)
(user2, 1)
(user1, 1)
(user3, 1)
(user2, 1)

SHUFFLE phase:

user1 → [1,1]
user2 → [1,1]
user3 → [1]

REDUCE phase:

user1 → 2
user2 → 2
user3 → 1

8.2 Why MapReduce is slow?

Because:

• Disk I/O after each step
• No in-memory processing
• Heavy serialization
• High latency

Spark solved this.

9️⃣ Spark vs Hadoop — Deep Explanation

But here is the truth:

👉 Spark is fast not because of memory only,
but because of DAG execution and lazy evaluation.

10️⃣ Spark DAG — Brain of Spark

When you write PySpark code:

df = spark.read.parquet("s3://logs/")
result = df.groupBy("user").count()
result.show()

Spark does NOT execute immediately.

Spark builds a DAG (Directed Acyclic Graph):

Stages → Tasks → Executors

Spark Execution Flow (Hardcore)

1. Driver program runs on EMR master node.
2. SparkContext created.
3. DAG built.
4. Job submitted to YARN.
5. YARN allocates executors.
6. Tasks sent to executors.
7. Executors fetch data from S3/HDFS.
8. Shuffle happens.
9. Results returned to driver.

🔥 Interview Trap #2

❓ Where does PySpark code run? Driver or Executor?

Correct Answer:

• Python code runs on Driver.
• Transformations executed on Executors.
• Spark plan built on Driver.
• Data processing happens on Executors.

11️⃣ AWS Regions, AZs, and Why They Exist

Region

A geographical area (e.g., Mumbai, US-East-1)

Availability Zone (AZ)

Independent data centers inside a region.

Why AZs exist?

Because:
👉 Entire data center can fail.

Example:

If EMR cluster runs in 1 AZ and AZ fails → cluster dies.

Best practice:

• Multi-AZ architecture.

12️⃣ AWS vs Hadoop Cluster — Mental Model

Think like this:

Hadoop Cluster

• NameNode = Brain
• DataNodes = Workers
• HDFS = Storage
• YARN = Resource Manager
• Spark = Compute engine

AWS Big Data Stack

• S3 = Storage
• EMR = Hadoop/Spark cluster
• Glue = Serverless Spark
• Redshift = Data Warehouse
• Athena = SQL engine
• Kinesis/Kafka = Streaming

👉 AWS is Hadoop ecosystem on steroids.

13️⃣ Hardcore Thought Experiment (Interactive)

Imagine:

You run Spark job on EMR.

Dataset: 1 TB in S3.

Question:

❓ What happens step-by-step?

Try to think before reading answer.

Answer (Deep):

1. Driver starts on EMR master node.
2. Spark queries S3 metadata (list objects).
3. Spark splits files into partitions.
4. Executors allocated on worker nodes.
5. Executors pull data from S3 via network.
6. Data deserialized into JVM memory.
7. Transformations executed.
8. Shuffle data across executors.
9. Results aggregated.
10. Output written back to S3.

Key Insight:

👉 No data locality in S3.
👉 Network is bottleneck.
👉 Serialization overhead exists.

14️⃣ Why AWS Data Engineering is Hard?

Because you are dealing with:

• Distributed systems
• Cloud networking
• Storage engines
• Compute engines
• Serialization
• Memory management
• Cost optimization
• Security
• Failure handling

Most engineers know only 20%.

You are going to master 100%.

🧠 Mind-Blowing Truth

If you understand:

• CAP theorem
• Data locality
• Spark DAG
• S3 vs HDFS
• Network latency

Then:

👉 80% of AWS + Spark performance problems become obvious.

🚀 Next Hardcore Level (Module 1.2)

If you want, I will take you into:

⚔️ AWS NETWORKING FOR DATA ENGINEERS (PhD level)

We will cover:

• VPC internals
• Subnets like memory segments
• Routing tables like OS routing
• NAT vs IGW
• Why EMR accesses S3 without internet
• How Glue talks to S3
• How Spark traffic flows in AWS
• Hidden network costs
• Real failure scenarios
• Interview questions that destroy candidates 😈

And I will also show:

🔥 “How a Spark job packet travels from executor → S3 → driver across AWS network”

If you say:

👉 “Continue Module 1.2 — AWS Networking Hardcore Mode”

I will go even deeper — like a cloud architect + Spark engineer combined.

Excellent.
Now we go where 99% of data engineers never go.

This module will permanently change how you see AWS, Spark, EMR, Glue, S3, Kafka, and networking.

We will not learn AWS networking like “create VPC → subnet → done”.

We will learn it like:

“How exactly does a Spark executor talk to S3 across AWS network, packet by packet?”

🧠 MODULE 1.2 — AWS NETWORKING (HARDCORE MODE)

If Module 1.1 was “what is distributed systems”,
Module 1.2 is “how distributed systems physically talk to each other in AWS”.

1️⃣ The Fundamental Truth of Cloud Networking

Most people think:

EC2 → S3 → Spark → Glue → works magically.

❌ WRONG.

Reality:

Every Spark job, every S3 read, every Glue job, every Kafka message is just:

👉 network packets moving across AWS infrastructure.

If you understand the network, you understand AWS.

2️⃣ Mental Model — AWS Network = Giant Virtual Internet

Imagine AWS as:

• Millions of servers
• Connected by ultra-fast fiber
• Controlled by software-defined networking (SDN)

Your VPC is NOT physical hardware.

👉 It is a logical network carved out of AWS global infrastructure.

3️⃣ VPC — Virtual Private Cloud (Deep Meaning)

Most tutorials say:

VPC = isolated network in AWS.

But deeper truth:

👉 VPC = software-defined network namespace.

Just like Linux namespaces isolate processes.

3.1 CIDR — The DNA of VPC

Example VPC CIDR:

10.0.0.0/16

Meaning:

• IP range: 10.0.0.0 → 10.0.255.255
• Total IPs: 65,536

Why CIDR matters for Data Engineers?

Because:

• EMR nodes get IPs from subnets
• Spark executors communicate using IPs
• Kafka brokers use IPs
• Glue uses ENIs in VPC

If CIDR is wrong → cluster fails.

🔥 Interview Trap #1

❓ Why does EMR cluster fail when subnet runs out of IPs?

Answer:

Each EC2 instance requires an IP.

If subnet has only /24 CIDR:

10.0.1.0/24 → 256 IPs (actually ~251 usable)

If Spark cluster needs 300 nodes → impossible.

👉 This is a real-world failure.

4️⃣ Subnets — Memory Segments of AWS

Think of VPC like RAM.

Subnets = memory segments.

Example:

VPC: 10.0.0.0/16

Subnets:

• Public subnet: 10.0.1.0/24
• Private subnet: 10.0.2.0/24
• Private subnet: 10.0.3.0/24

4.1 Public vs Private Subnet (Real Meaning)

Public Subnet

Has route to Internet Gateway (IGW).

Private Subnet

No direct internet access.

🧠 Key Insight for Data Engineers

Most AWS Big Data clusters run in:

👉 PRIVATE SUBNETS.

Why?

• Security
• Compliance
• Cost
• Data privacy

But Spark still needs S3.

So how does it talk to S3?

We’ll reach that soon.

5️⃣ Route Tables — The Brain of Subnets

Every subnet is associated with a route table.

Route table = rules for traffic.

Example route table:

Meaning:

• Internal traffic stays inside VPC.
• All other traffic goes to Internet Gateway.

6️⃣ Internet Gateway (IGW)

IGW = gateway between VPC and internet.

Public subnet must have:

• Route to IGW
• Public IP on EC2

7️⃣ NAT Gateway — The Most Important Concept for Data Engineers

NAT = Network Address Translation.

Why NAT exists?

Because private subnet cannot access internet directly.

But Spark/EMR in private subnet must:

• download libraries
• access S3
• call AWS APIs

Architecture:

Private Subnet → NAT Gateway → IGW → Internet/S3

🔥 Interview Trap #2

❓ Why does EMR cluster in private subnet still access S3?

Answer:

Because traffic goes through NAT Gateway or VPC Endpoint.

8️⃣ VPC Endpoints — The Secret Weapon

AWS provides:

Interface Endpoint

• ENI-based
• Private connection to AWS services

Gateway Endpoint

• Route table-based
• Used for S3 and DynamoDB

🧠 Hardcore Insight

If you configure S3 Gateway Endpoint:

👉 Spark on EMR accesses S3 WITHOUT internet.

Traffic path:

EMR → VPC Endpoint → S3

No NAT.
No IGW.
No public internet.

Benefits:

• Faster
• Cheaper
• Secure

🔥 Interview Trap #3

❓ Why is Spark faster with S3 VPC endpoint?

Answer:

Because:

• No NAT overhead
• No public internet routing
• Lower latency
• Higher throughput

9️⃣ Security Groups vs NACL — Real Meaning

Security Group (SG)

• Stateful firewall
• Attached to ENI/EC2
• Allows inbound/outbound traffic

NACL

• Stateless firewall
• Applied at subnet level

Hardcore Truth:

Most Spark failures are due to SG misconfiguration.

Example:

• Executor cannot talk to Driver.
• Kafka brokers cannot communicate.
• Glue cannot access RDS.

🔥 Interview Trap #4

❓ Why Spark executors cannot connect to driver?

Real reasons:

• SG does not allow inbound port 7077/4040
• Private IP not reachable
• Wrong subnet routing

10️⃣ ENI — Elastic Network Interface (Hidden Hero)

Every EC2 has an ENI.

ENI = virtual network card.

Glue jobs also create ENIs.

Spark executors communicate via ENIs.

11️⃣ How Spark Traffic Flows in AWS (Mind-Blowing)

Let’s simulate:

Scenario:

• EMR cluster in private subnet
• Data in S3
• Spark job running

Step-by-step packet flow:

STEP 1 — Driver starts

Driver runs on EMR master node.

IP: 10.0.2.10

STEP 2 — Executors allocated

Executors run on worker nodes.

IPs:

• 10.0.2.21
• 10.0.2.22
• 10.0.2.23

STEP 3 — Executors request data from S3

Executor sends HTTP request to S3 endpoint.

Packet flow:

Executor (10.0.2.21)
→ Route Table
→ VPC Endpoint / NAT Gateway
→ S3 service

STEP 4 — Data flows back

S3 sends data back:

S3 → VPC Endpoint / NAT → Executor

STEP 5 — Shuffle happens

Executors send data to each other:

10.0.2.21 → 10.0.2.22
10.0.2.22 → 10.0.2.23

This is internal VPC traffic.

STEP 6 — Results to Driver

Executors → Driver (10.0.2.10)

🧠 Key Insight

Spark has TWO types of traffic:

1. External traffic (S3, Kafka, APIs)
2. Internal traffic (Executor ↔ Executor ↔ Driver)

If internal traffic is slow → Spark job slow.

12️⃣ Why Spark on AWS Sometimes Becomes Slow?

Root causes:

(A) Network bottleneck

• NAT Gateway throughput limits
• No VPC endpoint
• Cross-AZ traffic

(B) Cross-AZ communication

If executors are in different AZs:

• Higher latency
• Higher cost

🔥 This is a real-world issue.

🔥 Interview Trap #5

❓ Why Spark job slower when nodes are in different AZs?

Answer:

Because:

• Cross-AZ network latency
• Cross-AZ data transfer cost
• Shuffle across AZs

13️⃣ Glue Networking — Even More Hidden

Glue is serverless Spark.

But Glue still runs inside AWS-managed VPC.

When you attach Glue to your VPC:

Glue creates ENIs in your subnets.

So:

Glue Executor → ENI → VPC → S3

🔥 Interview Trap #6

❓ Why Glue job fails when attached to private subnet?

Real reason:

• No NAT Gateway
• No VPC endpoint
• Glue cannot reach S3

14️⃣ Kafka / Kinesis Networking on AWS

Kafka on EC2 / MSK

Brokers run on private IPs.

Clients must access brokers via:

• Private IP (inside VPC)
• Load balancer (NLB)
• Peering / VPN

Spark Streaming + Kafka

Traffic:

Spark Executors → Kafka Brokers

If SG blocks port 9092 → job fails.

15️⃣ The Most Important Diagram (Mental)

AWS Data Engineering Network Architecture

User
 ↓
Bastion Host (Public Subnet)
 ↓
EMR Cluster (Private Subnet)
 ↓
S3 (via VPC Endpoint / NAT)
 ↓
Redshift / RDS

16️⃣ Hardcore Real-World Failure Case

Problem:

Spark job extremely slow on EMR.

Root Cause:

All traffic going through NAT Gateway.

NAT Gateway limit: ~45 Gbps.

Cluster size: 500 nodes.

👉 NAT becomes bottleneck.

Solution:

Use S3 VPC Gateway Endpoint.

17️⃣ Hardcore Interview Questions (You must master)

Try answering before reading answers.

Q1

Why does Spark on S3 behave differently from Spark on HDFS?

✅ Answer:
Because S3 is remote object storage accessed via network, while HDFS is local distributed file system with data locality.

Q2

Why is Glue slower than EMR for heavy workloads?

✅ Answer:
Glue is serverless with limited control over executors, network, and memory. EMR allows fine-grained tuning and HDFS locality.

Q3

Why does Spark job fail only in AWS but works locally?

✅ Answer:
Because of:

• IAM issues
• VPC networking issues
• S3 access issues
• SG misconfiguration
• Subnet IP exhaustion

🧠 Ultra-Deep Insight (Most Important)

If you understand AWS networking, you will realize:

👉 Most “Spark performance problems” are actually NETWORK problems.

Not Spark problems.

🚀 Next Level (Module 1.3)

If you want, we go even deeper:

⚔️ MODULE 1.3 — STORAGE ARCHITECTURE WAR

S3 vs HDFS vs EBS vs EFS vs FSx
(from Spark + Hadoop + AWS perspective)

We will cover:

• Why S3 is not a filesystem
• Why small files kill Spark
• Why Parquet is magical
• How Spark reads S3 internally
• Serialization, compression, partitioning
• Data lake architecture (Bronze/Silver/Gold)
• Delta/Iceberg/Hudi internals
• Real-world Spark + S3 performance tuning
• Interview traps that destroy candidates 😈

If you say:

👉 “Continue Module 1.3 — Storage Hardcore Mode”

I will take you into the deepest layer of AWS Data Engineering.

Now we enter the most dangerous and powerful knowledge zone of Data Engineering.

Most data engineers think storage = “S3 bucket + parquet files”.
But in reality:

Storage architecture decides 70–90% of Spark performance, cost, scalability, and reliability.

If you master this module, you will think like:

• Hadoop architects
• Spark engine developers
• AWS storage engineers

🧠 MODULE 1.3 — STORAGE ARCHITECTURE (HARDCORE MODE)

We will dissect:

• S3 vs HDFS vs EBS vs EFS vs FSx
• How Spark REALLY reads data
• Why small files destroy clusters
• Why Parquet is not just a format
• Why partitioning is misunderstood
• Why Data Lakes fail in real companies
• Deep performance tuning
• PhD-level interview traps

1️⃣ Fundamental Truth: Storage ≠ Disk

Most people think storage means disk.

❌ WRONG.

Storage is defined by:

• Latency
• Throughput
• Consistency
• Cost
• Access pattern
• Failure model

2️⃣ Storage Types in AWS (from Data Engineer POV)

3️⃣ HDFS — The Original Big Data Storage Engine

3.1 Why HDFS Exists?

Before Hadoop:

• Single machine disks
• Limited storage
• No fault tolerance

HDFS solved:

• Horizontal scaling
• Fault tolerance
• Data locality

3.2 HDFS Architecture (Deep)

Components:

🧠 NameNode (Brain)

• Stores metadata:
  • File names
  • Block locations
  • Permissions
• Does NOT store actual data.

💪 DataNodes (Workers)

• Store actual data blocks.

3.3 Block Architecture

Default block size: 128MB / 256MB

Example file: 1GB

It becomes:

• 8 blocks of 128MB

Replication factor: 3

So total storage = 3GB.

3.4 Data Locality (Key Idea)

Spark executors run on nodes where blocks exist.

Executor on Node A → reads block from Node A

No network overhead.

🔥 That’s why HDFS is fast.

3.5 HDFS Failure Model

If DataNode dies:

• Blocks replicated from other nodes.

If NameNode dies:

• Cluster stops.

👉 HDFS = CP system (CAP theorem).

4️⃣ S3 — The Beast of Cloud Storage

4.1 S3 is NOT a filesystem

This is the biggest misconception.

Differences:

4.2 S3 Internal Architecture (Conceptual)

When you upload a file:

1. File split into chunks
2. Stored across multiple disks
3. Replicated across AZs
4. Metadata stored separately

S3 guarantees:

• 11 nines durability (99.999999999%)

4.3 S3 Access Pattern (Spark Perspective)

Spark reads S3 using HTTP.

This means:

• No data locality
• Network overhead
• Serialization overhead

5️⃣ Why Spark on S3 is slower than HDFS?

Let’s compare.

HDFS Read Path

Executor → Local Disk → JVM Memory

Latency: ~1ms

S3 Read Path

Executor → Network → S3 → Network → Executor

Latency: ~10–100ms

🧠 Insight

Spark compensates by:

• Reading large blocks
• Parallelizing reads
• Using columnar formats (Parquet)

6️⃣ The Small Files Problem (Most Critical Issue)

6.1 What is Small Files Problem?

If you have:

• 1 TB data
• 1 million files of 1MB each

Spark suffers.

6.2 Why small files kill Spark?

Because:

1. Each file = metadata call to S3
2. Spark creates too many tasks
3. Driver overloaded
4. High scheduling overhead
5. Network latency multiplied

🔥 Interview Trap #1

❓ Why is Spark slow with many small files?

Hardcore Answer:

Because Spark creates one partition per file, causing excessive task scheduling, metadata lookups, and network overhead.

6.3 Real-world Example

Company stores logs like:

s3://logs/2026/01/24/12/34/56/log.json

Millions of tiny files.

Spark job fails or runs for hours.

6.4 Solutions

Solution 1: File compaction

Merge small files into big files.

Solution 2: Use Parquet

Columnar format with compression.

Solution 3: Partition design

Avoid too many partitions.

7️⃣ Parquet — The Secret Weapon

7.1 Row vs Column Storage

Row-based (CSV, JSON)

Row1: name, age, salary
Row2: name, age, salary

Column-based (Parquet)

Column1: name, name, name
Column2: age, age, age
Column3: salary, salary, salary

7.2 Why Parquet is faster?

Because:

• Only required columns read
• Better compression
• Vectorized processing

🔥 Interview Trap #2

❓ Why Parquet is faster than CSV in Spark?

Answer:

Because Spark reads only required columns and uses columnar compression and vectorized execution.

8️⃣ Partitioning — Most Misunderstood Concept

8.1 Partitioning in S3 (Hive-style)

Example:

s3://sales/year=2026/month=01/day=24/

This is NOT partitioning in S3.

It is just folder structure.

Partitioning is understood by Spark/Hive.

8.2 How Spark Uses Partitions

Spark creates partitions based on:

• Files
• Block size
• Split size
• User-defined repartition

8.3 Partition Explosion Problem

If you partition by:

• year
• month
• day
• hour
• user_id

You create millions of partitions.

Spark metadata load becomes slow.

🔥 Interview Trap #3

❓ Why over-partitioning is bad?

Answer:

Because it increases metadata overhead, small files, and task scheduling overhead.

9️⃣ EBS — Block Storage

9.1 What is EBS?

Disk attached to EC2.

Types:

• gp3 (general purpose)
• io1/io2 (high IOPS)
• st1/sc1 (throughput optimized)

9.2 Spark + EBS

EMR nodes use EBS for:

• HDFS storage
• Shuffle spill
• Temporary files

🔥 Interview Trap #4

❓ Why Spark spills to disk?

Answer:

Because memory insufficient → shuffle data written to disk (EBS).

10️⃣ EFS — Network File System

10.1 What is EFS?

Shared POSIX filesystem across EC2.

Latency higher than EBS.

Use cases:

• Shared config
• ML models
• Metadata

Not recommended for Spark heavy workloads.

11️⃣ FSx — High-Performance File Systems

Types:

• FSx for Lustre (HPC, ML)
• FSx for Windows

Spark + FSx used in ML pipelines.

12️⃣ Data Lake Architecture (Real World)

12.1 Bronze / Silver / Gold Layers

Bronze

Raw data (JSON, logs)

Silver

Cleaned data (Parquet)

Gold

Aggregated business data

12.2 Why Data Lakes Fail?

Because:

• No governance
• Too many formats
• No compaction
• Bad partitioning
• No schema evolution strategy

13️⃣ Delta / Iceberg / Hudi (Lakehouse)

Why they exist?

Because S3 is not transactional.

Problems:

• No ACID transactions
• No updates/deletes
• No schema evolution
• No time travel

How Delta/Iceberg solve this?

They add:

• Transaction logs
• Metadata layers
• Versioning

14️⃣ Spark + S3 Performance Tuning (PhD Level)

14.1 Tuning Spark for S3

Key configs:

• spark.sql.files.maxPartitionBytes
• spark.sql.shuffle.partitions
• spark.hadoop.fs.s3a.connection.maximum
• spark.executor.memory
• spark.executor.cores

14.2 Real Optimization Example

Problem:
Spark job reading 1TB data from S3 takes 2 hours.

Optimization:

1. Convert JSON → Parquet
2. Compact small files
3. Adjust partition size (~128MB–512MB)
4. Use broadcast joins
5. Use VPC endpoint

Result:
Job time reduced to 15 minutes.

15️⃣ Hardcore Interview Questions

Try answering mentally.

Q1

Why is rename operation expensive in S3?

✅ Answer:
Because S3 does not support in-place rename; it copies object to new key and deletes old one.

Q2

Why does Spark create too many tasks on S3?

✅ Answer:
Because each file or split becomes a partition.

Q3

Why is HDFS better for Spark than S3?

✅ Answer:
Because HDFS supports data locality and low-latency access.

Q4

Why is Delta Lake important on S3?

✅ Answer:
Because it adds ACID transactions and metadata management on top of object storage.

🧠 Ultra-Deep Insight (Most Important)

If you understand:

• HDFS vs S3
• Small files problem
• Partitioning vs file size
• Parquet internals
• Spark read path

Then:

👉 You can design data lakes that scale to petabytes.

Most engineers cannot.

🚀 Next Level (Module 1.4)

Now we go even deeper into:

⚔️ MODULE 1.4 — SPARK + AWS EXECUTION ENGINE (PHD MODE)

We will dissect:

• Spark Driver vs Executor vs Cluster Manager
• YARN vs Kubernetes vs Standalone
• EMR vs Glue vs Databricks
• Spark memory model (heap, off-heap, JVM)
• Serialization (Java vs Kryo)
• Shuffle internals
• Stage & task scheduling
• Skew handling
• Real Spark execution simulation
• AWS-specific Spark tuning
• Interview traps that only senior architects know 😈

If you say:

👉 “Continue Module 1.4 — Spark Execution Hardcore Mode”

I will take you into the deepest layer of Spark + AWS + Distributed Systems.