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Data Engineering Best Practices
Data Engineering Best Practices

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

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Profile Icon Richard J. Schiller Profile Icon David Larochelle
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Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
Paperback Oct 2024 550 pages 1st Edition
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Arrow left icon
Profile Icon Richard J. Schiller Profile Icon David Larochelle
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Free Trial
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
Paperback Oct 2024 550 pages 1st Edition
eBook
Can$12.99 Can$50.99
Paperback
Can$63.99
Subscription
Free Trial
eBook
Can$12.99 Can$50.99
Paperback
Can$63.99
Subscription
Free Trial

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Data Engineering Best Practices

A Data Engineer’s Journey – Background Challenges

The purpose of this chapter is to explain the challenges you must face to navigate a successful journey as a data engineer. The intricate nature of managing vast data, evolving technologies, and ensuring efficient data pipelines are some of the hurdles that make data engineering a demanding field. In this chapter, we will explain why data engineering is hard, and provide a foundation for the development of principles to be discussed in later chapters. We will also provide insight when navigating these challenges. Finally, we will provide an overview of the data engineering approaches you can use to scope out the current and near-future technology landscape.

We will discuss three of the main data engineering challenges:

  • Platform architectures change rapidly
  • There is a high cost and impact on a solution’s longevity from the strategy to buy rather than build
  • The prolific evolving set of data repository...

Challenge #1 – platform architectures change rapidly

The dynamic landscape of platform architectures poses both opportunities and challenges for data engineers. As systems and technologies rapidly evolve, there’s a pressing need for professionals in the field to stay abreast of these changes. Adapting to these shifts is not merely about understanding the newest tools or frameworks; it’s about foreseeing potential bottlenecks, ensuring system compatibility, and optimizing processes to accommodate these evolutions. Moreover, these frequent changes compel data engineers to embrace a continuous learning mindset, ensuring that their skills and knowledge remain relevant in an ever-shifting landscape.

In this section, you will learn about systemic changes in platform architectures in the last two decades, such as the move from SQL to NoSQL, the rise of big data, and the migration to the cloud. Please refer to Figure 2.2:

Figure 2.2 – Platform architecture mind map

Figure 2.2 –...

Challenge #2 – Total cost of ownership (TCO) is high

Engineers are tasked with making it happen! But how they make it happen is subject to many constraints. The first is cost and the second is time. Issues such as the total cost of running and managing the solution over time and the feasibility of maintaining it operationally also come into focus. The TCO for a well-engineered data solution is affected by the extract, transform, and load (ETL)/extract, load, and transform (ELT) architecture and buy versus build tooling choices for selected adopted solutions and architecture patterns. Please refer to Figure 2.3:

Figure 2.3 – TCO mind map

Figure 2.3 – TCO mind map

After reading this section, you will have a greater understanding of ETL/ELT: what it is, its origins and historical evolution, why its costs are so high, and the advantages of build versus buy. You will also learn why legacy master data management architectures are no longer in vogue.

ETL architecture...

Challenge #3 – Evolving data repository patterns – identifying big rocks for data engineers

As data engineering terrain continually expands, a deluge of data repository patterns has flooded the market, each promising a seamless pathway to robust data architecture. However, amid this deluge, it’s imperative for data engineers, especially those embarking on this journey, to align their designs to the decided upon foundational principles. These principles will be applied to the solution as the big rocks to be handled first. These patterns must be effectively applied to the common challenges down to the smallest as the solution architecture is formulated. This section unravels these large items: data immutability, lineage tracking, data quality preservation, scalability, security and compliance, data discoverability and accessibility, and cost-efficiency (later chapters will depict the principles to be applied to them). Please refer to Figure 2.4 and Figure 2.5:

...

Summary

In this chapter, we explained the challenges data engineers will face when crafting a future-proof data engineered solution. Some core challenges have been outlined that will be faced when managing vast data, evolving technologies, and ensuring efficient data pipelines:

  • Platform architectures change rapidly based on the cloud provider’s user demands, combined with shifting technology opportunities
  • The total cost of your ownership (TCO) is high if you do not build a future-proof solution
  • The data and system architecture that your design conforms to must be rational and handle important items first and not have them appear as obstacles later so that your future-proof goals are attainable

Data engineering remains a hard task, but you are going into the effort with your eyes open. A solid foundation has been laid for the principles to be discussed later. We provided an overview of data engineering approaches so that you can scope out the current and...

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Key benefits

  • 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

Description

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.

Who is this book for?

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.

What you will learn

  • 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

Product Details

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Publication date : Oct 11, 2024
Length: 550 pages
Edition : 1st
Language : English
ISBN-13 : 9781803244983
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Product Details

Publication date : Oct 11, 2024
Length: 550 pages
Edition : 1st
Language : English
ISBN-13 : 9781803244983
Vendor :
Google
Category :
Languages :

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Table of Contents

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

Customer reviews

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Frisian Oct 29, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Data Engineering Best Practices" by Richard Schiller is a down-to-earth guide for anyone serious about building data solutions that actually stand the test of time. Schiller dives into the real-world problems data engineers face like keeping up with rapid cloud migrations, juggling Agile processes, and prioritizing data privacy all while offering practical advice on how to do it right. With a mix of technical know-how and a thoughtful, big-picture approach, this book doesn’t just throw concepts at you. Instead, Schiller walks you through each stage of a project, making sure that the solutions are realistic, sustainable, and well-aligned with business goals.The book’s structure feels natural and easy to follow, starting from identifying core business challenges to laying out the best practices for building resilient architectures that can handle whatever the future brings. Schiller’s insights on data governance, machine learning, and adapting data strategies over time make it clear that he knows his stuff. His experience shows, and he makes even complex ideas feel within reach. For data engineers and IT professionals alike, this is the kind of book you’ll keep coming back to packed with ideas and examples that resonate well beyond the technical details.
Amazon Verified review Amazon
Amazon Customer Oct 29, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have found “Data Engineering Best Practices” to be a comprehensive and engaging walk through best practices necessary to best position an engineer for success. As stated in the beginning chapter, this book applies a hands on practical approach to apply software and data engineering practices to modern use cases. As a new Data Engineer, I must say I found this book engaging and well written. I learned much as I read through each chapter and found the engaging witticisms and manner of presentation to be helpful when trying to apply the technology to my prior experience. For example, "Don't expect to create the next state of the art tool" This type of advice presents the content in a down to earth way which draws me into wanting to accept the practices presented. The author’s friendly helpful tone keep it real for me. I would recommend this book to anyone seeking to clarify their solution engineering efforts while building up an expert understanding of data engineering best practices.
Amazon Verified review Amazon
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