The major goal of this book is to encourage you to move beyond viewing data engineering as a specific collection of data technologies. The data landscape is undergoing an explosion of new data technologies and practices, with ever-increasing levels of abstraction and ease of use. Because of increased technical abstraction, data engineers will increasingly become data lifecycle engineers, thinking and operating in terms of the principles of data lifecycle management.
In this chapter, you’ll learn about the data engineering lifecycle, which is the central theme of this book. The data engineering lifecycle is our framework describing “cradle to grave” data engineering. You will also learn about the undercurrents of the data engineering lifecycle, which are key foundations that support all data engineering efforts.
What Is the Data Engineering Lifecycle?
The data engineering lifecycle comprises stages that turn raw data ingredients into a useful end product, ready for consumption by analysts, data scientists, ML engineers, and others. This chapter introduces the major stages of the data engineering lifecycle, focusing on each stage’s core concepts and saving details for later chapters.
We divide the data engineering lifecycle into five stages (Figure 2-1, top):
Generation
Storage
Ingestion
Transformation
Serving data
Figure 2-1. ...
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