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Data Engineering Best Practices

Data Engineering Best Practices

By : Richard J. Schiller, David Larochelle
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Data Engineering Best Practices

Data Engineering Best Practices

5 (2)
By: Richard J. Schiller, David Larochelle

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)
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Key Considerations for Data Service Best Practices

When thinking about the complexities of data engineering, you may want to grab hold of a compelling metaphor in your mind as you dive into the details. Your goal is to press the processed information and derived knowledge out of the raw data so that you give the consumer the ability to glean new insights. It’s necessary that you create effective data services with today’s technologies and prepare to handle future technology innovation as it arises. Data services insulate data and information from the consumer. The consumer could be a subsequent processing step in a data factory’s data flow, an algorithm, or an external data analyst. Collecting curated data can be thought of as a multifaceted diamond.

The processed diamond speaks to both its inherent value and the complex craftsmanship required to unlock its potential. This craftsmanship, essential to the data service design, demands more than just an appreciation...

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Data Engineering Best Practices
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