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

Richard J. Schiller

David Larochelle

€37.99

5 (2 Ratings)

Paperback Oct 2024 550 pages 1st Edition

Richard J. Schiller

David Larochelle

€37.99

5 (2 Ratings)

Paperback Oct 2024 550 pages 1st Edition

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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

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What is the delivery time and cost of print books?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Unfortunately, due to several restrictions, we are unable to ship to the following countries:

Afghanistan
American Samoa
Belarus
Brunei Darussalam
Central African Republic
The Democratic Republic of Congo
Eritrea
Guinea-bissau
Iran
Lebanon
Libiya Arab Jamahriya
Somalia
Sudan
Russian Federation
Syrian Arab Republic
Ukraine
Venezuela

Frisian Oct 29, 2024

"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 Customer Oct 29, 2024

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.

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

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