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, ...
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