Nwokwu Chisom - Data Engineering for Beginners [2026, PDF, ENG] torrent

Книги и журналы » Компьютерная литература » Программирование (книги)

Data Engineering for Beginners

Год издания: 2026
Автор: Nwokwu Chisom

Издательство: John Wiley & Sons, Inc.
ISBN: 978-1-394-32543-6
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 369

Описание: Data is more than just numbers and text; it’s the foundation of modern decision-making, innovation, and intelligent systems. Data is everywhere, from the personalized recommendations we see online while shopping to the analytics that drive billion-dollar businesses. But data alone isn’t enough. Behind the scenes are professionals who collect, clean, transform, and deliver data where it needs to go. These professionals are data engineers, and this book is your invitation to learn about data engineering.
The field of data engineering is growing at lightning speed, especially with the rise of artificial intelligence systems, which rely on quality data. Many people are eager to break into this field but find it difficult to navigate their learning journey. Others may already be working in tech-or data-adjacent roles but lack the foundational understanding of how modern data systems are designed, built, and maintained.
Data Engineering for Beginners is a comprehensive, beginner-friendly guide designed to help engineers, analysts, and industry professionals grasp the fundamentals of data engineering, learn necessary concepts with real-world scenarios, and ultimately launch a career with a well-defined roadmap.
This book is unique because it offers a clear and accessible introduction to complex and often intimidating concepts. Most resources on data engineering assume a certain level of prior knowledge or experience, making it difficult for true beginners to find a starting point. I believe the strength of an engineer is understanding the rudiments of a topic, and that is what this book plans to achieve. It takes you on an intentional learning journey from understanding various data formats to designing databases, to learning how to secure data systems, to building architectures that scale.
With the rise of AI, data engineering has become even more popular. AI models also rely on vast amounts of high-quality data for training and operations, which would involve complex data processing and integration. Organizations are now keen on investing in data platforms and professionals who can manage those platforms, driving greater competition in the market.
[spoiler="Примеры страниц (скриншоты)"]

[/spoiler]
[spoiler="Оглавление"]
Chapter 1. Understanding Data
A Brief History of Data
Data in 19,000 BCE: The Great Baboon and Abacus
Data in the 1600s: Public Health Statistics
Data in the 1800s: The U.S. Census
Data in the 1900s: The Concept of Storage
Data in the 1990s: Data and the Internet
Types of Data
Structured Data
Unstructured Data
Semi-structured Data
Why Is Data Important?
Healthcare
Supply Chain
Transportation and Logistics
Artificial Intelligence
Data and Information
Summary
Notes

Chapter 2. Introduction to Data Engineering
Data Engineering Explained Using an Oil Refinery Analogy
An Overview of the Data Engineering Life Cycle
Data Storage
Data Ingestion
Data Transformation
Data Serving
Navigating Project Requirements, Engaging Stakeholders, and Delivering Business Value
Requirements Gathering
Understanding Stakeholders
Understanding System Requirements
Delivering Business Value
The Current State of Data Engineering
The Importance of Data Engineering
Summary

Chapter 3. Database Fundamentals
Key Concepts of Databases
Rows
Columns
Schema
Keys
Types of Databases
Relational Databases
NoSQL Databases
Choosing Between Relational and NoSQL Databases
Start With Your Data's Structure
Think About the Relationships in Your Data
How Fast Do You Need to Move?
How Do You Need to Query Your Data?
Scaling and Performance
Transaction and Strong Consistency Needs
Summary

Chapter 4. SQL Fundamentals
Introduction to SQL
Basic SQL Clauses
Comparison Operators
LIKE Statement
IN Statement
BETWEEN Statement
AND Statement
OR Statement
NOT Statement
IS NULL and IS NOT NULL Statements
Sorting and Limiting
Aggregate Functions
SUM()
AVG()
MAX() and MIN()
GROUP BY
HAVING
Understanding Joins
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
Subqueries
Common Table Expressions (CTEs)
Set Operations
Window Functions
Lab: Setting Up SQL Server and Running SQL Queries
Best Practices for Writing Efficient SQL Queries
Summary

Chapter 5. Database Design
Data Modeling
Why Do We Need to Model Data?
Types of Data Modeling
Normalization
Rules of Normalization
Downsides of Normalization
Denormalization
Data Modeling Best Practices
Define the Grain
Normalize Now, Denormalize Later
Choose the Right Data Types
Proper Naming Conventions
Database Optimization
Indexing
Partitioning
Sharding
Views
Summary

Chapter 6. Data Warehouses, Data Lakes, and Data Lakehouses
Data Warehouses
Extract, Transform, and Load (ETL)
Schema Design
Snowflake Schema
Slowly Changing Dimensions
Data Marts
Benefits of a Data Mart
Challenges with Data Marts
Data Lakes
How Do Data Lakes Work?
Challenges of Data Lakes
Data Lakehouse
Features of a Data Lakehouse
Data Lakehouse Architecture
The Key Differences Between a Database, Data Warehouse, Data Lake, and Data Lakehouse
Summary

Chapter 7. Data Pipelines
Batch Pipelines
Components of a Batch Pipeline
ETL Pipelines vs. ELT Pipelines
Stream Pipelines
How Would This Work?
Components of a Streaming Data Pipeline
Lambda Architecture
Components of the Lambda Architecture
Advantages of the Lambda Architecture
Challenges and Trade-offs
Data Orchestration
Directed Acyclic Graphs (DAGs)
Scheduling and Automation
Monitoring
Alerts
Lab: Building an ETL Pipeline and Automating with Apache Airflow
Requirements
Set Up Your Development Environment
Extracting Data from CSV
Transforming the Data
Load the New CSV File into a Postgres Database Instance
Schedule ETL Pipeline with Apache Airflow
Summary

Chapter 8. Data Quality
Bad Data
Dimensions of Data Quality
Accuracy
Completeness
Consistency
Validity
Uniqueness
Timeliness
Accessibility
Relevance
Data Quality Hierarchy
Data Quality Best Practices
Summary

Chapter 9. Data Security
What Is Data Security?
Common Threats to Data Security
Core Principles of Data Security
Confidentiality
Integrity
Availability
Data Encryption
Symmetric Encryption
Asymmetric Encryption
Data Masking
Understanding Network Security
Access Control
Authentication
Authorization
The Principle of Least Privilege
Access Levels
Secrets Management
Data Security and Data Privacy
Summary

Chapter 10. Data Governance
How to Think About Data Governance
Data Governance Framework
Policies
Regulatory Compliance Policy
Data Classification Policy
Data Retention and Disposal Policy
Data Sharing Policy
Processes
Metadata Management
Data Lineage
Incident Management
Master Data Management
Roles in the Data Governance Framework
Data Owner
Data Steward
Data Custodian
Chief Data Officer (CDO)
Data Management and Data Governance
Summary

Chapter 11. Big Data and Distributed Systems
The Five V's of Big Data
Volume
Velocity
Variety
Veracity
Value
Distributed Systems
Scalability
Fault Tolerance
Reliability
Concurrency
Resource Management
Consistency
Availability
Load Balancing
Latency
Distributed Data Processing
Apache Hadoop
Big Data File Types
Avro
Parquet
Optimized Row Columnar (ORC)
Choosing the File Type
Summary

Chapter 12. Data Engineering on the Cloud
Cloud Computing
On-Premises
Cloud
Making the Right Choice
Core Cloud Concepts
Storage
Compute
Networking
Cloud Service Models
Infrastructure as a Service
Platform as a Service
Software as a Service
Choosing Between IaaS, PaaS, and SaaS
A Hybrid Approach
Cloud Management Models
Serverless
Managed
Self-Managed
Putting It All Together
Cost Optimization
Understanding Cloud Pricing Models
Rightsizing Resources
Smart Job Scheduling
Storage Optimization
Shutting Down Idle Resources
Use Serverless Where Possible
Monitoring and Alerting
Summary

Chapter 13. Building a Career in Data Engineering
Types of Data Engineering Roles
Types of Data Engineers
Platform Data Engineer
Analytics Data Engineer
AI/ML Data Engineers
Landing Your First Data Engineering Role
A Typical Data Engineering Job Description
How to Build a Winning Résumé
Preparing for a Data Engineering Interview
Thinking Like a Data Engineer
Think in Systems
Learn to Prioritize Data Quality
Design for Failure
Balance Business Context with Technical Choices
Optimize for Clarity, Then Speed
Think Beyond the Tool
Master Automation
Summary

Appendix. Sample Interview Questions
SQL
Data Modeling
Data Pipelines
Apache Spark
System Design

Data Engineering Glossary
Index
[/spoiler]