Database Design is a very interesting topic. My personal experience designing databases has been very limited professionally. Normally, I have gone by what the data looks like and designing around it. Comparing this to how the chapter describes database design, I think the overall outcomes have not been very far from what is expected, but data but this has been certainly based on good tribal knowledge and mentorship. Having read and worked on the cases for normalization, I think I have a much better understanding of why those designs worked. More importantly, I understand why normalization is so important, and why we often sacrifice it to make data more accessible.
We’re also starting to deep our toes into Data Science and how sql supports aggregation. Since I am very much interested in the monetization and targeting strategies, this is a topic that is very dear to me. I often have to look for ways to make sense of purchase trends and patterns, and look for ways to visualize that data so users can look into it. In the past, I’ve used code to aggregate and process the data, or used specialized tools like Druid. When data gets to my hands, it’s often already gone through the ETL process, so i’m very much looking forward to the next couple of weeks to better understand the whole process. But using GROUP BY and other aggregators, already helps me!