Now we enter the architect’s battlefield.
This is where you stop being a Spark developer and become a true AWS Data Architect.

We will not draw toy diagrams.

We will design real-world, production-grade, petabyte-scale systems like Netflix, Uber, Amazon, and Flipkart.

We will think in terms of:

• latency
• throughput
• cost
• fault tolerance
• scalability
• data governance
• security
• operability
• evolution over time

This module will permanently change how you design data systems.

🧠 MODULE 1.5 — AWS DATA ENGINEERING SYSTEM DESIGN (HARDCORE MODE)

We will build 5 real architectures:

1. Modern Batch Data Platform (Petabyte scale)
2. Real-time Streaming Analytics System
3. Lakehouse Architecture on AWS
4. Hybrid Batch + Streaming (Lambda/Kappa architecture)
5. Interview-grade System Design Framework

And we will analyze:

• why each component exists
• what breaks at scale
• how AWS networking + storage + Spark interact
• how to optimize cost and performance
• what interviewers REALLY want

1️⃣ How Architects Think (Not Developers)

Developers think:

“Which service should I use?”

Architects think:

“What are my constraints?”

Core Constraints in Data Engineering

1. Data volume (GB → TB → PB)
2. Velocity (batch vs streaming)
3. Variety (structured, semi, unstructured)
4. Latency requirements (ms vs hours)
5. Cost constraints
6. Reliability (SLA)
7. Security & compliance
8. Future evolution

2️⃣ Architecture 1 — Modern Batch Data Platform (Petabyte Scale)

🎯 Use Case

• Company collects logs, transactions, IoT data.
• Volume: 50 TB/day.
• Users: analysts, ML teams, dashboards.

🏗️ Architecture

Sources
 (Apps, DBs, APIs)
        ↓
Ingestion Layer
 (Kafka / Kinesis / DMS / SFTP)
        ↓
Raw Storage (Bronze)
 (S3 - JSON/Avro)
        ↓
Processing Layer
 (EMR / Glue / Spark)
        ↓
Curated Storage (Silver/Gold)
 (S3 - Parquet/Delta)
        ↓
Analytics Layer
 (Athena / Redshift / BI Tools)
        ↓
Orchestration
 (Airflow / Step Functions)

🧠 Why Each Component Exists?

1. Ingestion Layer

Why Kafka/Kinesis?

Because:

• decouple producers from consumers
• handle spikes
• replay data

2. Raw Storage (S3 Bronze)

Why S3?

Because:

• infinite scalability
• cheap
• durable
• decouples compute from storage

Why JSON/Avro?

Because:

• schema evolution
• raw data preservation

3. Processing Layer (Spark on EMR/Glue)

Why Spark?

Because:

• distributed processing
• handles TB–PB data

Why EMR vs Glue?

4. Curated Storage (Parquet/Delta)

Why Parquet?

Because:

• columnar
• compressed
• Spark-friendly

Why Delta/Iceberg?

Because:

• ACID transactions
• time travel
• schema evolution

5. Analytics Layer

Athena:

• ad-hoc SQL
• cheap
• S3-based

Redshift:

• high-performance analytics
• structured queries

6. Orchestration

Why Airflow?

Because:

• dependency management
• retries
• scheduling

💣 What breaks at scale?

Problem 1 — Small files explosion

Raw data arrives every second → millions of files.

Impact:

• Spark slow
• Athena slow
• Glue slow

Solution:

• compaction jobs
• micro-batch ingestion
• partition strategy

Problem 2 — NAT Gateway bottleneck

EMR cluster reads S3 via NAT.

Impact:

• network throttling
• high cost

Solution:

• S3 VPC endpoint

Problem 3 — Spark driver overload

Too many partitions → driver OOM.

Solution:

• partition tuning
• file compaction

Problem 4 — Data skew

Some keys dominate data.

Solution:

• salting, AQE, broadcast join

3️⃣ Architecture 2 — Real-Time Streaming Analytics (Uber-like)

🎯 Use Case

• Real-time user events.
• Latency: < 1 second.
• Volume: millions of events/sec.

🏗️ Architecture

Producers
 (Mobile Apps, IoT)
        ↓
Streaming Layer
 (Kafka / Kinesis)
        ↓
Stream Processing
 (Spark Streaming / Flink)
        ↓
Serving Layer
 (DynamoDB / Redis / OpenSearch)
        ↓
Long-term Storage
 (S3 Data Lake)
        ↓
Analytics
 (Redshift / Athena)

🧠 Key Design Decisions

Why Kafka/Kinesis?

Because:

• high throughput
• partitioned logs
• replay capability

Why Spark Streaming?

Because:

• micro-batch processing
• integration with batch Spark

Alternative:

• Flink for low latency

Why DynamoDB?

Because:

• low-latency reads
• scalable key-value store

Why S3?

Because:

• historical data analysis.

💣 Failure Scenarios

Scenario 1 — Kafka partition imbalance

Some partitions overloaded.

Impact:

• lag increases
• Spark streaming slow

Solution:

• re-partition topics
• key design

Scenario 2 — Backpressure

Spark cannot process data fast enough.

Solution:

• autoscaling executors
• batch interval tuning

Scenario 3 — Exactly-once semantics

Problem:

• duplicate events.

Solution:

• idempotent writes
• checkpointing

4️⃣ Architecture 3 — Lakehouse on AWS (Modern Enterprise)

🎯 Use Case

• Unified analytics + ML platform.
• Petabyte-scale data lake.
• ACID transactions.

🏗️ Architecture

Sources → Kafka/Kinesis → S3 (Delta/Iceberg)
                         ↓
                    Spark / EMR / Glue
                         ↓
                   BI / ML / APIs

🧠 Why Lakehouse?

Because traditional data lakes lack:

• ACID transactions
• schema enforcement
• governance

Delta/Iceberg/Hudi solve this.

💣 Real-world issues

Issue 1 — Metadata explosion

Millions of partitions.

Solution:

• partition pruning
• manifest optimization

Issue 2 — Concurrent writes

Multiple Spark jobs writing same table.

Solution:

• Delta transaction logs.

5️⃣ Architecture 4 — Lambda vs Kappa Architecture

Lambda Architecture

Batch + Streaming.

Pros:

• accurate batch results
• real-time insights

Cons:

• complexity

Kappa Architecture

Only streaming.

Pros:

• simpler
• unified pipeline

Cons:

• complex reprocessing

6️⃣ Interview-Grade System Design Framework

When interviewer asks:

“Design a data platform on AWS.”

Most candidates fail because they jump to services.

Instead follow this structure:

Step 1 — Clarify Requirements

Ask:

• Data volume?
• Latency?
• Users?
• Cost?
• SLA?

Step 2 — Define Layers

1. Ingestion
2. Storage
3. Processing
4. Serving
5. Orchestration
6. Governance

Step 3 — Choose AWS Services

Explain WHY, not WHAT.

Example:

“I choose S3 because decoupling compute and storage is critical for scalability.”

Step 4 — Identify Bottlenecks

Talk about:

• small files
• skew
• network
• cost
• scaling

This is where you sound senior.

Step 5 — Propose Optimizations

Mention:

• partitioning
• caching
• autoscaling
• compression
• governance

🧠 Ultra-Deep Insight (Architect-Level)

Most people design pipelines like:

S3 → Spark → Redshift.

Architects design pipelines like:

Network + Storage + Compute + Cost + Failure model + Evolution.

🎯 PhD-Level Interview Questions (with hidden traps)

Try thinking deeply.

Q1

Design a system to process 100 TB/day logs on AWS.

What would you choose and why?

Q2

How would you design a real-time fraud detection system?

Q3

Why not use Redshift instead of S3 + Spark?

Q4

How would you reduce AWS cost of Spark jobs by 50%?

Q5

How would you redesign a failing data lake?

If you want, I will:

✅ Answer these questions like a top AWS architect
✅ Create a complete AWS Data Engineer Interview Playbook
✅ Build a step-by-step hands-on roadmap with labs + datasets + PySpark + AWS
✅ Create a PhD-level cheat sheet + mind maps + diagrams
✅ Design a 30–60 day mastery plan for you

If you say:

👉 “Build the AWS Data Engineer Master Playbook (Interview + Practice + Labs)”

I will turn this knowledge into a structured system you can actually study and master.