Data Engineering Best Practices: Architect robust and cost-effective data solutions in the cloud era

by Richard J. Schiller, David Larochelle

Overview

Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms

(hfour)Key Features(/hfour)

• Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness
• Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design
• Learn from experts to avoid common pitfalls in data engineering projects
• Purchase of the print or Kindle book includes a free PDF eBook

(hfour)Book Description(/hfour)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.(hfour)What you will learn(/hfour)

• Architect scalable data solutions within a well-architected framework
• Implement agile software development processes tailored to your organization's needs
• Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products
• Optimize data engineering capabilities to ensure performance and long-term business value
• Apply best practices for data security, privacy, and compliance
• Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines

(hfour)Who this book is for(/hfour)

If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.

Product Details

ISBN-13:	9781803247366
Publisher:	Packt Publishing
Publication date:	10/11/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	550
File size:	9 MB

About the Author

Richard J. Schiller is a chief architect, distinguished engineer, and startup entrepreneur with 40 years of experience delivering real-time large-scale data processing systems. He holds an MS in computer engineering from Columbia University's School of Engineering and Applied Science and a BA in computer science and applied mathematics. He has been involved with two prior successful startups and has co authored three patents. He is a hands-on systems developer and innovator.
David Larochelle has been involved in data engineering for startups, Fortune 500 companies, and research institutes. He holds a BS in computer science from the College of William & Mary, a Masters in computer science from the University of Virginia, and a Master's in communication from the University of Pennsylvania. David's career spans over 20 years, and his strong background has enabled him to work in a wide range of organizations, including startups, established companies, and research labs.

Table of Contents

Table of Contents

1. Overview of the Business Problem Statement
2. A Data Engineer's Journey – Background Challenges
3. A Data Engineer's Journey – IT's Vision and Mission
4. Architecture Principles
5. Architecture Framework – Conceptual Architecture Best Practices
6. Architecture Framework – Logical Architecture Best Practices
7. Architecture Framework – Physical Architecture Best Practices
8. Software Engineering Best Practice Considerations
9. Key Considerations for Agile SDLC Best Practices
10. Key Considerations for Quality Testing Best Practices
11. Key Considerations for IT Operational Service Best Practices
12. Key Considerations for Data Service Best Practices
13. Key Considerations for Management Best Practices
14. Key Considerations for Data Delivery Best Practices
15. Other Considerations – Measures, Calculations, Restatements, and Data Science Best Practices
16. Machine Learning Pipeline Best Practices and Processes
17. Takeaway Summary – Putting It All Together
18. Appendix and Use Cases