Data Engineering for Beginners

Table of Contents

1. Cover
2. Table of Contents
3. Title Page
4. Foreword
5. Introduction
   1. What Does This Book Cover?
   2. Who Should Read This Book?
6. CHAPTER 1: Understanding Data
   1. A Brief History of Data
   2. Types of Data
   3. Why Is Data Important?
   4. Data and Information
   5. Summary
   6. Notes
7. CHAPTER 2: Introduction to Data Engineering
   1. Data Engineering Explained Using an Oil Refinery Analogy
   2. An Overview of the Data Engineering Life Cycle
   3. Navigating Project Requirements, Engaging Stakeholders, and Delivering Business Value
   4. The Current State of Data Engineering
   5. The Importance of Data Engineering
   6. Summary
8. CHAPTER 3: Database Fundamentals
   1. Key Concepts of Databases
   2. Types of Databases
   3. Choosing Between Relational and NoSQL Databases
   4. Summary
9. CHAPTER 4: SQL Fundamentals
   1. Introduction to SQL
   2. Comparison Operators
   3. Understanding Joins
   4. Lab: Setting Up SQL Server and Running SQL Queries
   5. Best Practices for Writing Efficient SQL Queries
   6. Summary
10. CHAPTER 5: Database Design
    1. Data Modeling
    2. Normalization
    3. Denormalization
    4. Data Modeling Best Practices
    5. Database Optimization
    6. Summary
11. CHAPTER 6: Data Warehouses, Data Lakes, and Data Lakehouses
    1. Data Warehouses
    2. Data Marts
    3. Data Lakes
    4. Data Lakehouse
    5. The Key Differences Between a Database, Data Warehouse, Data Lake, and Data Lakehouse
    6. Summary
12. CHAPTER 7: Data Pipelines
    1. Batch Pipelines
    2. Stream Pipelines
    3. Lambda Architecture
    4. Data Orchestration
    5. Lab: Building an ETL Pipeline and Automating with Apache Airflow
    6. Summary
13. CHAPTER 8: Data Quality
    1. Bad Data
    2. Dimensions of Data Quality
    3. Data Quality Hierarchy
    4. Summary ...