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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, 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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