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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 explored many operational topics and were exposed to the best practices for SLA setting and management for important contract terms. Data contracts were defined, and references were provided for further reading. All of this led to the development of quality data factory outputs. Solution monitoring with observability was stressed and the necessary capabilities were elaborated upon. You were also exposed to the essential need for data anomaly detection so that you can detect data drift and gross data errors before they get into the factory’s curated data product. Lastly, you saw how blue/green deployment versus other release practices affect the data in the factory and why operational tradeoffs will have to be negotiated with the software developers as part of your data engineering solution.

In the next chapter, you will be provided with best practices to build out the framework for your data services.

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