Building the Data Engineering of Tomorrow
By Aiman JoeHanz
Length6h 7m
About this audiobook
A comprehensive guide to data engineering for practitioners, engineering leaders, and decision-makers. Covers the current state of data engineering practice, common failure modes and mistakes, foundational principles from ETL to idempotency, core architectures including data warehouses, lakes, lakehouses, streaming, and data mesh, implementation strategies for production data systems, technology evolution from batch to streaming, real-world case studies, scaling and future-proofing practices, governance and compliance, and the trust and ethics of responsible data engineering. Thirteen chapters with practical insights, key takeaways, and a five-phase action plan for organizations building data engineering capability.
Audiobook details
GenreEducation and Learning
Length6 hrs 7 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateOct 2, 2026
LanguageEnglish
Table of contents
1Title Page
2Introduction
3Chapter 1: The Current State of Data Engineering
4Chapter 2: Where Today's Data Engineering Breaks Down
5Chapter 3: Common Mistakes in Data Engineering Programs
Show all chaptersShow less
6Chapter 4: Foundational Principles of Data Engineering
7Chapter 5: Core Data Architectures and Platforms
8Chapter 6: Implementation Strategies for Production Data Systems
9Chapter 7: Technology Deep Dive: From Batch ETL to Streaming and the Lakehouse
10Chapter 8: Case Studies: Real-World Data Engineering Deployments
11Chapter 9: Scaling and Future-Proofing Data Platforms
12Chapter 10: The Future of Data Engineering
13Chapter 11: Action Plan for Organizations Building Data Engineering Capability
14Chapter 12: Advanced Topics: Governance, Compliance, and Emerging Data Risks
15Chapter 13: Trust, Ethics, and the Responsible Data Engineering Journey
16Appendix A: Data Architecture Decision Guide
17Appendix M: Quick Reference Guide
18Index
19Appendix B: Production Data Pipeline Reference
20Appendix C: Data Governance and Compliance
21Appendix D: Cost Optimization for Data Systems
22Appendix E: Modern Data Stack Guide
23Appendix F: Streaming Data Systems
24Appendix G: Data Testing and CI/CD
25Appendix H: Cloud Data Platforms Comparison
26Appendix I: Data Engineering Case Studies
27Appendix J: Extended FAQ
28Appendix K: Best Practices Checklist
29Appendix L: The Future of Data Engineering
30Frequently Asked Questions
31Glossary
32References: Building the Data Engineering of Tomorrow
33Connect
34About the Author
35Series Info
Series
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