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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

This chapter brought you on a journey where you were able to discover what goes into the definition of physical architecture with a few examples of physical reference architectures. You also discovered key aspects of the architecture driving the best practices. By forming your own physical reference architecture diagram, you learned that you can create an architecture that may be communicated to various operational staff to obtain the desired correct operational readiness state. Proper choices for physical architecture were made in a complete and comprehensive way. No corners were cut in the process so that what was released remains workable in the future. A properly engineered solution should be based on what has been proven to always work and never fail, not on what looks the most convincing at the time. The architecture you produce will be complete and, even if it is incrementally built over many Agile sprints, it will not deviate from the goal. This can easily happen as...

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Data Engineering Best Practices
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