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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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Summary

As we reach the end of our exploration into the intricate world of data services, it is instructive to reflect on our journey with the understanding that an elegant solution is essential for these services. This comes from the need to preserve trust in the data service’s promised capabilities and to service the data’s semantics via data services that do not frustrate those semantics. The result is that the technology implementation will not be easy, but it will be a large effort to deliver an elegant, simple solution for complex data. If you make your data service elegant, your data consumers will know it. They will tell then you that you have been successful in your efforts.

The right solution emerges under conditions of significant complexity and challenge, much like the formation of diamonds under the immense pressures and high temperatures deep within the Earth. This metaphor extends beyond a mere comparison of the results; it encompasses the process, the...

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