Data engineering for beginners / Chisom Nwokwu.

Format:

Book

Author/Creator:

Nwokwu, Chisom, author.

Series:

Tech Today Series

Language:

English

Subjects (All):

Big data.

Database management.

Physical Description:

1 online resource (387 pages)

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Incorporated, 2026.

Summary:

A hands-on technical and industry roadmap for aspiring data engineers In Data Engineering for Beginners, big data expert Chisom Nwokwu delivers a beginner-friendly handbook for everyone interested in the fundamentals of data engineering.

Contents:

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

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

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

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

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

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

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

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

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

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

Load Balancing

Latency

Distributed Data Processing

Apache Hadoop

Big Data File Types

Avro.

Parquet

Optimized Row Columnar (ORC)

Choosing the File Type

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

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

Appendix: Sample Interview Questions

SQL

Data Pipelines

Apache Spark

System Design

Data Engineering Glossary.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-32542-8

1-394-35257-3

9781394325429

OCLC:

1546814862