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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 number of areas for consideration in your designs. For data profiling, you want to really know your data, find data areas that are anomalies, and be ready to smooth the data, fill gaps, and if necessary, create temporary adjustments with synthetic data until real data can be supplied. When implementing a data factory, you need to know that the data is of the highest quality and that includes the core data, its metadata, and its trends. You should also include its data profile in that list.

You also learned that raw data is messy and has legitimate gaps and illegitimate (erroneous) gaps that need correction. It is important that you also know the shape of the data as defined by its profile so that it can be semantically maintained in downstream processing and not cause data to be misused.

You also learned about data calendars and that data has to be interpreted in context, one facet of which is the data calendar. This is essential...

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