Fundamentals Of Data Engineering: Mastering the Architecture of Trust — From Raw Ingestion to Generative AI, Building Systems That Power the Digital Age - Softcover

Jarvis, Dale D

Softcover

ISBN 13: 9798192295212

Publisher: Independently published, 2026

Synopsis

DATA ENGINEERING FUNDAMENTALS

A Practical Guide to Data Pipelines, Architecture, Storage, Processing, Quality, Orchestration, and Modern Data Platforms

BUILD A STRONG FOUNDATION IN MODERN DATA ENGINEERING

How does raw information become reliable, usable data?

What does it take to build data systems that are scalable, maintainable, secure, and dependable?

Which concepts should every aspiring data engineer understand before working with increasingly complex data platforms?

DATA ENGINEERING FUNDAMENTALS provides a practical introduction to the core concepts, workflows, architectures, and engineering practices used to build modern data systems.

From data ingestion and transformation to storage, orchestration, quality, security, and analytics, this guide brings the essential pieces together in one structured resource.

WHAT YOU'LL EXPLORE

• Foundations of data engineering

• The modern data lifecycle

• Data engineering roles and responsibilities

• Data sources and data ingestion

• Batch and real-time data processing

• ETL and ELT workflows

• Data transformation principles

• Data pipelines and workflow design

• Relational databases

• NoSQL databases

• Data warehouses

• Data lakes

• Lakehouse architecture

• Data modeling fundamentals

• Schema design and evolution

• Data storage and file formats

• Distributed data processing

• Streaming data concepts

• Workflow orchestration and scheduling

• Data quality and validation

• Data lineage and metadata

• Monitoring and observability

• Pipeline testing

• Error handling and recovery

• Data security and access management

• Privacy and sensitive-data considerations

• Cloud data platforms

• Performance and scalability

• Cost optimization

• CI/CD for data systems

• Infrastructure and deployment concepts

• Data architecture and engineering trade-offs

UNDERSTAND THE DATA ENGINEERING LIFECYCLE

Data engineering is more than building pipelines.

A successful data platform must reliably move information from source systems to destinations where it can be analyzed and used.

That requires thoughtful decisions about ingestion, transformation, storage, processing, orchestration, quality, security, monitoring, and access.

This guide connects those individual components so readers can understand how they work together as an engineering system.

LEARN THE CORE ARCHITECTURAL CONCEPTS

Modern organizations may rely on operational databases, APIs, object storage, data warehouses, data lakes, streaming platforms, transformation tools, and orchestration systems.

Understanding the purpose and limitations of these components helps data professionals select appropriate solutions instead of relying solely on technology-specific recipes.

Understand the data lifecycle. Design reliable pipelines. Build better data systems.

Get your copy of DATA ENGINEERING FUNDAMENTALS and develop a stronger foundation for modern data engineering.

Important Disclaimer: This is an independently authored educational resource. It is not affiliated with, sponsored by, endorsed by, or officially connected with any author, publisher, cloud provider, software vendor, or certification organization. Technology platforms, tools, and best practices change over time; readers should consult current official documentation when implementing production systems.

Edition details

• Publisher: Independently published
• Publication date: 2026
• Language: English
• ISBN 13: 9798192295212
• Binding: Paperback
• Number of pages: 196