Data Engineering Design Patterns: Building Reliable, Scalable, and Production-Ready Data Systems from First Principles

By Dale D Jarvis

DATA ENGINEERING PATTERNS AND ARCHITECTURES

A Practical Guide to Reliable Pipelines, Data Integration, Workflow Orchestration, Quality, Scalability, Streaming, and Modern Data Platforms

DESIGN DATA SYSTEMS THAT ARE RELIABLE, SCALABLE, AND MAINTAINABLE

How do you design data pipelines that continue working as data volumes and business requirements grow?

How can you make data workflows easier to monitor, test, troubleshoot, and maintain?

Which architectural patterns can help data engineers build dependable systems without creating unnecessary complexity?

DATA ENGINEERING PATTERNS AND ARCHITECTURES is a practical guide to the recurring design challenges encountered when building modern data platforms and pipelines.

It explores reusable approaches for data ingestion, transformation, orchestration, storage, quality, observability, streaming, and scalable data processing.

WHAT YOU'LL EXPLORE

Fundamentals of data engineering architecture

Data pipeline design principles

ETL and ELT patterns

Batch-processing architectures

Real-time and streaming architectures

Data ingestion patterns

API and database integration

Incremental data loading

Change data capture concepts

Data transformation strategies

Data modeling considerations

Data warehouse architectures

Data lake and lakehouse concepts

Workflow orchestration

Dependency management

Pipeline scheduling

Data validation and quality patterns

Schema management and evolution

Data lineage and metadata

Idempotency and reliable processing

Error handling and retry strategies

Monitoring and observability

Data pipeline testing

Performance optimization

Scalability and distributed processing

Event-driven data architectures

Message queues and event streams

Security and access control

Cost-aware data engineering

CI/CD and deployment practices

Common architectural trade-offs

Troubleshooting and failure recovery

Recognize the pattern. Evaluate the trade-offs. Build reliable data platforms.

Get your copy of DATA ENGINEERING PATTERNS AND ARCHITECTURES and strengthen your foundation in modern data-system design.

Important Disclaimer: This is an independently authored educational resource. It is not affiliated with, sponsored by, endorsed by, or officially connected with any particular author, publisher, software vendor, cloud provider, or technology organization. Examples and technologies may evolve over time; readers should consult current official documentation when implementing production systems.

• Format: Paperback
• Language: English
• ISBN: B0HF8TCFFJ
• ISBN13: 9798192293263
• Release Date: August 2026
• Publisher: Independently Published
• Length: 278 Pages
• Weight: 1.43 lbs.
• Dimensions: 0.6" x 8.5" x 11.0"