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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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Continuous integration/continuous deployment (CI/CD)

In modern system development, the complexities and rate of change can pose a wilting set of problems to the engineering manager. It is essential that the end-to-end DevOps pipeline (or, the CI/CD system processes supporting the SDLC) implement waves of software regression testing for new check-ins. Additionally, data contract assertions are needed to ensure that new software does not negatively impact the existing state of system data. The assertion outputs are observable outputs of the system that themselves map to success or failure states. Some types of data contracts can only be enforced after a period of time, a data arrival initiated trigger event, a calendar schedule, a data sensitivity threshold, or a measure/metric trigger has been reached.

A key point here is that the assertions and the checking of those assertion outcomes form gates to be unlocked in the CI/CD pipeline. Passing these gates allows forward progress in...

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Data Engineering Best Practices
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