Chapter 1: Fundamentals of Data Engineering
Years ago, when I first entered the data science world, I used to think data was clean. Clean in terms of readiness, available in one place, and ready for fun data science purposes. I was so excited to experiment with machine learning models, finding unusual patterns in data and playing around with clean data. But after years of experience working with data, I realized that data science in big organizations isn't straightforward.
Eighty percent of the effort goes into collecting, cleaning, and transforming the data. If you have had any experience in working with data, I am sure you've noticed something similar. But the good news is, we know that almost all processes can be automated using proper planning, designing, and engineering skills. That was the point where I realized that data engineering will be the most critical role from that day to the future of the data science world.
To develop a successful data ecosystem in any organization, the most crucial part is how they design the data architecture. If the organization fails to make the best decision on the data architecture, the future process will be painful. Here are some common examples: the system is not scalable, querying data is slow, business users don't trust your data, the infrastructure cost is very high, and data is leaked. There is so much more that can go wrong without proper data engineering practice.
In this chapter, we are going to learn the fundamental knowledge behind data engineering. The goal is to introduce you to common terms that are often used in this field and will be mentioned often in the later chapters.
In particular, we will be covering the following topics:
- Understanding the data life cycle
- Know the roles of a data engineer before starting
- Foundational concepts for data engineering
