[Crash Course #03] Hands On Crash Course on Data Pipelines : How it Actually Works and How to Build ( with Implementation Code)

Everything you need to implement Data Pipelines

First Complete Part 1 here : [Crash Course #01] A Complete Crash Course on REST APIs : How it Actually Works - Part 1

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📚 Table of Contents

Module 1: Foundation & Mental Models

1. The Coffee Shop Analogy: Understanding Data Flow

2. The Five Pillars of Data Pipelines

3. Interactive Lab 1.1: Trace Your First Data Journey

4. User Flow: How Data Moves in Your Favorite Apps

Module 2: Sources & Ingestion Mastery

1. Source Types Deep Dive

2. Batch vs Streaming: The Decision Framework

3. Interactive Lab 2.1: Build a CSV Batch Ingestion Pipeline

4. Interactive Lab 2.2: Create a Real-Time Event Stream

5. User Flow: E-Commerce Order Processing

6. User Flow: IoT Sensor Data Collection

Module 3: Transformation Techniques

1. The Art of Data Cleaning

2. SQL Transformation Patterns

3. Interactive Lab 3.1: Clean Messy Customer Data

4. Interactive Lab 3.2: Build a Star Schema

5. User Flow: Product Analytics Dashboard Creation

Module 4: Advanced Patterns & Architectures

1. Lambda vs Kappa Architecture

2. CDC: Capturing Database Changes

3. Interactive Lab 4.1: Implement a Hybrid Pipeline

4. User Flow: Real-Time + Historical Analytics

Module 5: Orchestration & Monitoring

1. Airflow DAGs Demystified

2. Error Handling & Retry Strategies

3. Interactive Lab 5.1: Build Your First Airflow DAG

4. Interactive Lab 5.2: Implement Data Quality Tests

5. User Flow: Production Pipeline Lifecycle

Module 6: Real-World Case Studies

1. Case Study 1: Netflix Recommendation Pipeline

2. Case Study 2: Uber’s Real-Time Pricing

3. Case Study 3: Spotify’s Daily Mix Generation

4. Interactive Lab 6.1: Build a Mini Recommendation Engine

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Module 1: Foundation & Mental Models

The Coffee Shop Analogy: Understanding Data Flow

Imagine you run a coffee shop. Let’s trace the journey from raw beans to customer satisfaction:

┌─────────────────┐
│  Coffee Farm    │  ← SOURCE (Data Origin)
│  (Raw Beans)    │
└────────┬────────┘
         │ Harvest (Daily)
         ↓
┌─────────────────┐
│  Shipping Truck │  ← INGESTION (Data Movement)
│  (Transport)    │
└────────┬────────┘
         │ Delivery
         ↓
┌─────────────────┐
│   Warehouse     │  ← STORAGE (Staging Area)
│  (Raw Storage)  │
└────────┬────────┘
         │ Processing
         ↓
┌─────────────────┐
│  Roasting Room  │  ← TRANSFORMATION (Cleaning & Processing)
│  (Clean, Roast) │
└────────┬────────┘
         │ Packaging
         ↓
┌─────────────────┐
│  Display Case   │  ← DESTINATION (Ready for Use)
│  (Ready Coffee) │
└────────┬────────┘
         │ Serving
         ↓
┌─────────────────┐
│   Customer      │  ← CONSUMPTION (End User)
│   (Enjoys)      │
└─────────────────┘

         ↕
┌─────────────────┐
│  Quality Check  │  ← MONITORING (Throughout)
│   + Manager     │
└─────────────────┘

Key Insight: Just as you wouldn’t serve raw beans to customers, you never serve raw data to analysts. The pipeline is the processing facility.