The Data Engineering Blog

I decided to shift my “tech” blog over to a more niche subject. I found myself with little side time to work on tech projects and learning due to my MBA coursework. However, I am still dedicating some time to researching data engineering topics as that is a portion of what I do. Data engineering has become a very broad role, with expectations around everything from understanding database architectures, business intelligence and reporting, data migrations, data warehousing, machine learning, and now AI. Although it is also still a specific role, there is some segregation in it based on the stack you have experience on. Data engineering roles tend to ask very specifically if you work in data warehousing and what specific tool you’ve used. As organizations sit on ever increasing piles of data, they may need to start thinking about recruiting generic data engineers and data engineers should look to also becoming tool agnostic. Although I understand each shop has its own tooling and band-aids and whatever else, in the end it is an engineering role and engineers should be allowed and expected to expand their domain expertise. Especially in the age of AI, years of experience with certain tooling that included painful trial and errors and reading through dense manuals may no longer be needed. Coupled with an enormous pool of video content from places like Udemy and Pluralsight, blogs, YouTube videos, and instant delivery of books via Kindle there is no excuse for any person wanting to expand their skillset to give that covers not doing so.

I am going to attempt to expand my own knowledge in this blog. I am specifically going to put effort into expanding my OLAP knowledge, as I am very well versed in OLTP technologies. As such, I am interested in the reporting and data analytics pieces of it as well. Much of these posts will be my research, but I also hope to include some hands on experimentation and reporting as well.