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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

In this chapter, you were exposed to a technology deep dive with lots of references to research and come up to speed on the topics of any blind spots. Then, we dove into three high level use cases. Although they may have lacked serious details, they have been portrayed descriptively enough for you to get the general idea. The challenge has always been to leverage your data in new ways and to organize it in a way that makes that possible. Data needs to be smart, fungible, and linkable.

Over time, the future will show us how trust can be enforced among the roles both within and external to your IT organization. Data silos will come down when the truth of the underliyng data is transparently preserved with formal semantics. Data consumers will know that the information presented is correct, complete, governed, and able to be used as intended. This makes the promise of an AI ecosystem a reality, because the errors are reduced during RAG processing, prompt engineering/tuning...

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Data Engineering Best Practices
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