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Book Overview & Buying
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Table Of Contents
Data Engineering Best Practices
By :
Data Engineering Best Practices
By:
Overview of this book
Revolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines.
You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications.
By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.
Table of Contents (21 chapters)
Preface
Free Chapter
Chapter 1: Overview of the Business Problem Statement
Chapter 2: A Data Engineer’s Journey – Background Challenges
Chapter 3: A Data Engineer’s Journey – IT’s Vision and Mission
Chapter 4: Architecture Principles
Chapter 5: Architecture Framework – Conceptual Architecture Best Practices
Chapter 6: Architecture Framework – Logical Architecture Best Practices
Chapter 7: Architecture Framework – Physical Architecture Best Practices
Chapter 8: Software Engineering Best Practice Considerations
Chapter 9: Key Considerations for Agile SDLC Best Practices
Chapter 10: Key Considerations for Quality Testing Best Practices
Chapter 11: Key Considerations for IT Operational Service Best Practices
Chapter 12: Key Considerations for Data Service Best Practices
Chapter 13: Key Considerations for Management Best Practices
Chapter 14: Key Considerations for Data Delivery Best Practices
Chapter 15: Other Considerations – Measures, Calculations, Restatements, and Data Science Best Practices
Chapter 16: Machine Learning Pipeline Best Practices and Processes
Chapter 17: Takeaway Summary – Putting It All Together
Chapter 18: Appendix and Use Cases
Index
