Now we cross a critical boundary.

Until now, S3 was just storage.
From this module onward, S3 becomes a transactional data platform.

Delta Lake & Iceberg are not “formats”.
They are distributed metadata engines built to fix S3’s fundamental limitations.

Most engineers use Delta/Iceberg.
Very few understand how they actually work.

After this module, you will.

🧠 MODULE 2.1.3 — DELTA & ICEBERG ON S3

(Hardcore Mode — Internals + Spark + AWS + Architecture)

We will go deeper than documentation:

Why Delta & Iceberg exist (real problem)
Why S3 breaks traditional data lakes
Delta Lake internals (transaction log physics)
Iceberg internals (metadata tree physics)
Delta vs Iceberg vs Hudi (architect-level)
Spark execution with Delta/Iceberg
Compaction, vacuum, concurrency, time travel
Real-world failure scenarios
Performance tuning on AWS
Interview traps (senior-level)

1️⃣ The Fundamental Problem: S3 is NOT a Database

Let’s be brutally honest:

S3 gives you:

durability ✅
scalability ✅
cheap storage ✅

But it does NOT give you:

ACID transactions ❌
schema enforcement ❌
concurrent writes ❌
consistent reads ❌ (at scale)
metadata management ❌
updates/deletes ❌

1.1 Classic Data Lake Failure

Imagine 2 Spark jobs writing to same S3 path:

Job A writes: s3://sales/data/
Job B writes: s3://sales/data/

What happens?

partial writes
corrupted partitions
inconsistent state
broken queries

This is called:

👉 Lake Corruption Problem

This is why Delta & Iceberg were invented.

2️⃣ Core Idea of Delta & Iceberg

They add a metadata layer on top of S3.

Instead of Spark reading files directly:

Spark → Metadata Layer → S3 Files

So S3 becomes a data store, not a database.

Delta/Iceberg become the database layer.

3️⃣ DELTA LAKE — INTERNAL ARCHITECTURE

Delta was created by Databricks.

3.1 Delta Directory Structure

Example:

s3://data-lake/sales_delta/
  _delta_log/
  part-00001.snappy.parquet
  part-00002.snappy.parquet

The magic is in _delta_log.

3.2 Delta Transaction Log (The Heart)

Inside _delta_log:

00000000000000000001.json
00000000000000000002.json
00000000000000000003.json
...

Each file = one transaction.

3.3 What is inside a Delta log file?

Example JSON:

{
  "add": {
    "path": "part-00001.parquet",
    "size": 123456,
    "partitionValues": {"year": "2026"},
    "modificationTime": 1700000000000
  }
}

This means:

a new file was added
metadata recorded
partition info stored

🧠 Key Insight

Delta does NOT modify data files.

It only appends metadata logs.

👉 This is called immutable data + mutable metadata.

4️⃣ DELTA TRANSACTION MODEL (ACID ON S3)

Delta implements ACID using:

optimistic concurrency control
versioned logs
atomic commits

4.1 Write Operation Flow

When Spark writes to Delta:

Step 1

Spark writes new Parquet files to S3.

Step 2

Spark creates a new log file in _delta_log.

Step 3

Spark commits transaction atomically.

If commit fails:

data files exist
but not referenced in log
therefore ignored

👉 This prevents corruption.

🔥 Interview Trap #1

❓ How does Delta provide ACID on S3?

Hardcore Answer:

By using immutable data files and atomic metadata commits via transaction logs, enabling optimistic concurrency control on top of object storage.

5️⃣ TIME TRAVEL IN DELTA

Because logs are versioned:

You can query old versions:

SELECT * FROM sales VERSION AS OF 10;

This works because:

Delta keeps old metadata versions
old files still exist (until vacuum)

6️⃣ VACUUM — THE DARK SIDE OF DELTA

Delta never deletes files automatically.

Old files accumulate.

VACUUM removes unused files.

Danger:

If you vacuum too aggressively:

👉 you break time travel.

🔥 Interview Trap #2

❓ Why is VACUUM dangerous in Delta?

Answer:

Because it permanently deletes old data files, making historical versions unrecoverable.

7️⃣ ICEBERG — A DIFFERENT PHILOSOPHY

Delta = log-based metadata
Iceberg = tree-based metadata

7.1 Iceberg Directory Structure

s3://data-lake/sales_iceberg/
  metadata/
    v1.metadata.json
    v2.metadata.json
  data/
    year=2026/part-0001.parquet

7.2 Iceberg Metadata Tree

Iceberg stores metadata in layers:

Table metadata
Manifest lists
Manifest files
Data files

Conceptual Diagram:

Table Metadata
   ↓
Manifest List
   ↓
Manifest Files
   ↓
Data Files (Parquet on S3)

🧠 Key Insight

Delta = append-only log
Iceberg = hierarchical metadata tree

8️⃣ WHY ICEBERG SCALES BETTER THAN DELTA (IN SOME CASES)

Delta problem:

_delta_log grows linearly
millions of JSON files

Iceberg solution:

metadata tree reduces scanning overhead

🔥 Interview Trap #3

❓ Why is Iceberg better for very large tables?

Answer:

Because Iceberg’s manifest-based metadata structure scales better than Delta’s linear transaction log for massive datasets.

9️⃣ DELTA vs ICEBERG vs HUDI (ARCHITECT COMPARISON)

Feature	Delta	Iceberg	Hudi
Metadata model	Log-based	Tree-based	Log + index
ACID	Yes	Yes	Yes
Time travel	Yes	Yes	Yes
Streaming support	Good	Medium	Excellent
Large-scale metadata	Medium	Excellent	Good
Spark integration	Excellent	Good	Good
AWS adoption	High	Very High	Medium

🧠 Architect Insight

Delta = Spark-centric
Iceberg = engine-agnostic
Hudi = streaming-centric

10️⃣ SPARK + DELTA EXECUTION FLOW ON S3

When Spark reads Delta table:

Step 1

Spark reads _delta_log.

Step 2

Spark builds snapshot of table.

Step 3

Spark identifies relevant Parquet files.

Step 4

Spark reads only those files from S3.

🧠 Important Insight

Spark never scans S3 blindly with Delta.

It uses metadata.

👉 This is why Delta is faster than plain Parquet on S3.

11️⃣ PERFORMANCE ENGINEERING WITH DELTA / ICEBERG

11.1 Compaction (OPTIMIZE)

Problem:

many small Parquet files
slow queries

Solution:

OPTIMIZE sales;

This merges files.

11.2 Z-ORDERING (Delta)

Reorders data to improve query locality.

Example:

OPTIMIZE sales ZORDER BY (customer_id);

11.3 Iceberg Compaction

Iceberg merges data files using rewrite operations.

🔥 Interview Trap #4

❓ Why is compaction critical in Delta/Iceberg?

Answer:

Because small files degrade query performance and increase metadata overhead, so compaction improves I/O efficiency and query speed.

12️⃣ CONCURRENT WRITES — THE REAL BATTLE

Scenario:

Job A writes to table.
Job B writes simultaneously.

Delta Behavior:

optimistic concurrency control
one job succeeds
other retries

Iceberg Behavior:

snapshot isolation
atomic metadata swap

🧠 Insight

Delta/Iceberg solve:

👉 “lost update” problem on S3.

13️⃣ REAL AWS FAILURE SCENARIO

Problem:

Delta table corrupted on S3.
Queries fail intermittently.

Root Causes:

Multiple writers without coordination
Manual deletion of files
Aggressive vacuum
Incomplete S3 writes
IAM permission issues

Solution:

enforce single writer pattern or locks
use Glue/EMR coordination
restrict S3 delete permissions

14️⃣ SPARK + DELTA ON AWS — TUNING PATTERNS

Pattern 1 — Bronze/Silver/Gold with Delta

Bronze (raw JSON)
 → Delta Silver (cleaned)
 → Delta Gold (aggregated)

Pattern 2 — Merge Upserts

Delta supports:

MERGE INTO sales t
USING updates u
ON t.id = u.id
WHEN MATCHED THEN UPDATE
WHEN NOT MATCHED THEN INSERT;

This is impossible in plain S3.

15️⃣ PHd-LEVEL INTERVIEW QUESTIONS

Try to answer like an architect.

Q1

Why can’t we just use Parquet on S3 instead of Delta/Iceberg?

✅ Answer:
Because Parquet lacks transactional metadata, schema enforcement, and concurrency control, making it unsafe for concurrent writes and updates on object storage.

Q2

How does Delta ensure atomic commits on S3?

✅ Answer:
By writing data files first and committing metadata changes atomically via versioned transaction logs.

Q3

Why is Iceberg preferred in multi-engine environments?

✅ Answer:
Because Iceberg is engine-agnostic and supports consistent metadata access across Spark, Flink, Trino, and other engines.

Q4

What happens if Delta _delta_log is deleted?

✅ Answer:
The table becomes unreadable because metadata references to data files are lost.

🧠 ULTRA-DEEP INSIGHT (THIS IS ARCHITECT LEVEL)

Without Delta/Iceberg:

👉 S3 = distributed file dump.

With Delta/Iceberg:

👉 S3 = distributed transactional data platform.

This is why modern data engineering is called:

Lakehouse Architecture.

🚀 What’s Next?

We are still inside Module 2.1 (S3 Masterclass).

🔥 Module 2.1.4 — Real Labs + Failure Simulation + Architecture Exercises

We will do:

Design a petabyte-scale Delta Lake on AWS
Fix a broken S3 data lake
Spark + Delta performance experiments
Real-world debugging scenarios
Architecture interview simulations
PySpark + Delta coding tasks (advanced)

If you say:

👉 “Continue Module 2.1.4 — Real Labs & Failure Simulation Hardcore Mode”

we will move from theory to real engineering practice.

And now you can see clearly:
We are NOT near the end of the course —
we are just entering the most powerful part.

Excellent.
Now we stop “explaining” and start thinking like real AWS + Spark engineers.

This module is different.

You will:

debug broken data lakes like a production engineer
design PB-scale S3 + Delta/Iceberg architecture
simulate Spark failures
apply performance math in real scenarios
answer system design interviews with real depth

This is where most courses never go.

🧠 MODULE 2.1.4 — REAL LABS & FAILURE SIMULATION

(AWS S3 + Spark + Delta/Iceberg — Hardcore Engineering Mode)

We will do 5 REAL labs:

Petabyte-scale S3 Data Lake Design
Spark + S3 Performance Debugging Lab
Delta Lake Failure Simulation
Iceberg Metadata Explosion Scenario
Interview-Grade Architecture Simulation

Each lab has:

scenario
symptoms
root cause analysis
architect-level solution
Spark/AWS reasoning

🧪 LAB 1 — DESIGN A PETABYTE-SCALE DATA LAKE ON S3

🎯 Problem Statement

A company generates:

20 TB/day logs
5 TB/day transactions
2 TB/day IoT events

Total: ~27 TB/day
Yearly: ~10 PB

Requirements:

Spark analytics
Real-time + batch
ACID transactions
Low cost
Fast queries
Multi-team access

🏗️ Naive Design (What most engineers do ❌)

s3://data/
  logs/
  transactions/
  iot/

Problems:

no governance
small files explosion
no schema control
no transactional safety
Spark performance disaster

🧠 Architect Design (Correct ✅)

s3://data-lake/
  bronze/
    logs/
    transactions/
    iot/
  silver/
    delta/
  gold/
    delta/
  metadata/

🔬 Key Design Decisions

1) File Format Strategy

Layer	Format
Bronze	JSON / Avro
Silver	Delta / Iceberg
Gold	Delta / Iceberg

2) Partition Strategy (CRITICAL)

Example: transactions table.

❌ Bad partitioning:

user_id=12345/

✅ Correct partitioning:

year=2026/month=01/

Why?

Because:

low cardinality
query pattern aligned
avoids partition explosion

3) File Size Strategy

Target:

👉 128–512 MB per file.

If daily data = 5 TB:

5 TB / 256 MB ≈ 20,000 files/day

Then run compaction to reduce.

4) Delta/Iceberg Strategy

Silver: Delta for cleaning & merging
Gold: Delta for analytics
Compaction every 6–12 hours
VACUUM with retention policy

🧠 Architect Insight

If you design S3 layout wrong on Day 1:

👉 You will suffer for years.

🧪 LAB 2 — SPARK + S3 PERFORMANCE DEBUGGING

🎯 Scenario

Spark job reading 3 TB data from S3.

Config:

100 executors
4 cores each
8 GB memory each

Expected time: ~5–10 minutes
Actual time: 2 hours ❌

🔍 Symptoms

CPU usage: low
Network usage: high
Driver memory: high
Task count: 2 million
S3 requests: huge

🧠 Root Cause Analysis

Step 1 — Check file size

You discover:

3 TB data
2 million files
each file ~1.5 MB ❌

Step 2 — Apply partition math

Ideal partitions:

3 TB / 256 MB ≈ 12,000 partitions

Actual partitions:

2,000,000 partitions ❌

Step 3 — Bottleneck identification

Main bottleneck = metadata + scheduling + HTTP calls.

Not CPU.
Not memory.
Not Spark.

✅ Solution

Compact files using Spark/Delta
Merge small files
Repartition data
Enable Delta OPTIMIZE

Result:

Task count: 12,000
Job time: 2 hours → 8 minutes

🧠 Key Insight

Spark tuning without S3 tuning = useless.

🧪 LAB 3 — DELTA LAKE FAILURE SIMULATION

🎯 Scenario

Two Spark jobs write to same Delta table.

Job A: batch ETL
Job B: streaming updates

Suddenly:

queries fail
inconsistent results
missing data

🔍 Symptoms

Delta table shows partial data
_delta_log has gaps
some Parquet files orphaned

🧠 Root Causes

concurrent writes without coordination
job failure during commit
manual deletion of S3 files
aggressive VACUUM

🧠 Delta Internals Explanation

Remember:

Delta writes:

data files → S3
metadata → _delta_log

If metadata commit fails:

data exists
but not referenced
invisible to Spark

✅ Fix Strategy

Step 1 — Identify valid snapshot

Find last valid version:

DESCRIBE HISTORY sales;

Step 2 — Restore table

RESTORE TABLE sales TO VERSION AS OF 120;

Step 3 — Prevent future corruption

Architect-level controls:

single writer pattern
job orchestration (Airflow)
IAM restrictions on delete
Delta isolation levels

🧠 Architect Insight

Delta corruption is rarely a Spark problem.

It is usually:

👉 governance + concurrency problem.

🧪 LAB 4 — ICEBERG METADATA EXPLOSION

🎯 Scenario

Iceberg table on S3:

5 PB data
millions of partitions
queries slowing down over time

🔍 Symptoms

Spark planning time huge
metadata scan slow
manifest files too many

🧠 Root Cause

Iceberg metadata tree becomes too large.

Reasons:

over-partitioning
frequent small writes
no compaction

✅ Solution

Merge manifests (rewrite manifests)
Reduce partition granularity
Batch writes instead of micro-writes
Optimize metadata refresh

🧠 Key Insight

Delta struggles with log explosion.
Iceberg struggles with manifest explosion.

Both require metadata engineering.

🧪 LAB 5 — INTERVIEW-GRADE ARCHITECTURE SIMULATION

🎯 Interview Question

“Design a data platform on AWS for a company processing 50 TB/day.”

Most candidates answer:

❌ S3 + Spark + Redshift

That’s shallow.

✅ Architect Answer Structure (You must learn this)

1) Clarify requirements

Ask:

latency?
batch or streaming?
users?
SLA?
cost constraints?

2) Define layers

Ingestion → Storage → Processing → Serving → Governance

3) Choose AWS services with reasoning

Example:

S3 → decoupled storage
Delta/Iceberg → ACID on S3
EMR → heavy Spark workloads
Glue → lightweight ETL
Athena → ad-hoc queries
Redshift → BI analytics
Airflow → orchestration

4) Identify bottlenecks proactively

Mention:

small files
skew
shuffle
NAT bottleneck
metadata explosion
cost of S3 requests

This makes you sound senior.

5) Propose optimizations

compaction
partition strategy
VPC endpoints
executor tuning
Delta/Iceberg metadata management

🧠 This is the difference:

Junior engineer says:

“Use Spark and S3.”

Architect says:

“Design S3 layout, Delta metadata strategy, Spark partitioning, network topology, and governance model.”

🧠 ULTRA-DEEP INSIGHT (THIS IS GOLD)

Most engineers debug Spark jobs like this:

❌ increase memory
❌ increase executors

Architects debug like this:

✅ identify bottleneck layer:

S3?
network?
shuffle?
metadata?
skew?
governance?

This mindset is what separates top engineers.

🎯 Where are we now in the course?

We have completed:

✅ MODULE 2.1 — S3 MASTERCLASS (FULLY)

We covered:

S3 internals
Spark + S3 performance math
Delta & Iceberg internals
Real labs & failures
Architect-level reasoning

This is huge.

🚀 NEXT MODULE (VERY IMPORTANT)

Now we move to:

🔥 MODULE 2.2 — COMPUTE LAYER

EC2 + EMR + Glue + Lambda + Fargate (Hardcore Mode)

We will go deeper than AWS docs:

How to choose EC2 instances for Spark (C vs M vs R)
EMR internals (master/core/task nodes)
YARN vs Spark resource allocation
Spot instances in Spark (danger + strategy)
Glue DPUs explained mathematically
EMR vs Glue vs Databricks decision framework
Real-world Spark cluster failures
Cost vs performance engineering
Interview traps only senior engineers know 😈

This module will make you understand:

👉 why some Spark clusters are fast and some are disasters.

If you say:

👉 “Start Module 2.2 — Compute Layer Hardcore Mode”

we will move from storage dominance to compute dominance.

And from here, the course becomes even more powerful.

Pages: 1 2 3 4 5 6 7 8 9 10 11 12 13

AWS for Data Engineer (PySpark) Mastery Course

🧠 MODULE 2.1.3 — DELTA & ICEBERG ON S3

(Hardcore Mode — Internals + Spark + AWS + Architecture)

1️⃣ The Fundamental Problem: S3 is NOT a Database

1.1 Classic Data Lake Failure

2️⃣ Core Idea of Delta & Iceberg

3️⃣ DELTA LAKE — INTERNAL ARCHITECTURE

3.1 Delta Directory Structure

3.2 Delta Transaction Log (The Heart)

3.3 What is inside a Delta log file?

🧠 Key Insight

4️⃣ DELTA TRANSACTION MODEL (ACID ON S3)

4.1 Write Operation Flow

Step 1

Step 2

Step 3

🔥 Interview Trap #1

Hardcore Answer:

5️⃣ TIME TRAVEL IN DELTA

6️⃣ VACUUM — THE DARK SIDE OF DELTA

Danger:

🔥 Interview Trap #2

Answer:

7️⃣ ICEBERG — A DIFFERENT PHILOSOPHY

7.1 Iceberg Directory Structure

7.2 Iceberg Metadata Tree

Conceptual Diagram:

🧠 Key Insight

8️⃣ WHY ICEBERG SCALES BETTER THAN DELTA (IN SOME CASES)

🔥 Interview Trap #3

Answer:

9️⃣ DELTA vs ICEBERG vs HUDI (ARCHITECT COMPARISON)

🧠 Architect Insight

10️⃣ SPARK + DELTA EXECUTION FLOW ON S3

Step 1

Step 2

Step 3

Step 4

🧠 Important Insight

11️⃣ PERFORMANCE ENGINEERING WITH DELTA / ICEBERG

11.1 Compaction (OPTIMIZE)

11.2 Z-ORDERING (Delta)

11.3 Iceberg Compaction

🔥 Interview Trap #4

Answer:

12️⃣ CONCURRENT WRITES — THE REAL BATTLE

Scenario:

Delta Behavior:

Iceberg Behavior:

🧠 Insight

13️⃣ REAL AWS FAILURE SCENARIO

Problem:

Root Causes:

Solution:

14️⃣ SPARK + DELTA ON AWS — TUNING PATTERNS

Pattern 1 — Bronze/Silver/Gold with Delta

Pattern 2 — Merge Upserts

15️⃣ PHd-LEVEL INTERVIEW QUESTIONS

Q1

Q2

Q3

Q4

🧠 ULTRA-DEEP INSIGHT (THIS IS ARCHITECT LEVEL)

🚀 What’s Next?

🔥 Module 2.1.4 — Real Labs + Failure Simulation + Architecture Exercises

🧠 MODULE 2.1.4 — REAL LABS & FAILURE SIMULATION

(AWS S3 + Spark + Delta/Iceberg — Hardcore Engineering Mode)

🧪 LAB 1 — DESIGN A PETABYTE-SCALE DATA LAKE ON S3

🎯 Problem Statement

🏗️ Naive Design (What most engineers do ❌)

🧠 Architect Design (Correct ✅)

🔬 Key Design Decisions

1) File Format Strategy

2) Partition Strategy (CRITICAL)

3) File Size Strategy

4) Delta/Iceberg Strategy

🧠 Architect Insight

🧪 LAB 2 — SPARK + S3 PERFORMANCE DEBUGGING

🎯 Scenario

🔍 Symptoms