Data Engineering Lifecycle

Understanding the main stages in DE lifecycle and how they interact with each other

I recently started reading the “Fundamentals of Data Engineering” book and decided to share my learnings as I go along with the book. In this article, we will discuss the main stages in the Data Engineering lifecycle as mentioned in the book and some key concepts related to each one of them. Before diving into that, we can first see what Data Engineering actually is.

Simply put, the goal of Data Engineering is to take in raw data and converts it into information that can be used by down-stream use cases in a consistent manner. To achieve this goal, we need to develop, implement, and maintain a wide range of systems and processes, which is what Data Engineering is all about.

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Data Engineering Lifecycle

The data engineering lifecycle consists of the following:

• Generation
• Storage
• Ingestion
• Transformation
• Serving