How these two data roles are similar yet very different
Once I first began as a knowledge engineer, I worked on a DevOps-focused team. While it wasn’t exactly what I desired to be doing in my first role, it taught me lots. Now looking back, if I hadn’t worked in that style of role then, I probably wouldn’t have the experience I actually have as an analytics engineer today.
Now, working as an analytics engineer, I deal with something called DataOps. While this will sound much like DevOps, they’re very different. DevOps focuses on software because the product while DataOps focuses on producing high-quality data. For those specializing in DataOps, data is the product!
While working as a DevOps data engineer, I supported software engineers making code changes to our web application. I focused on testing changes within the UI after each deployment fairly than looking into the specifics of the info. Not once did I check on the variety of rows in a table or if values in a field were populated. As a substitute, I made sure no errors were being thrown on the backend.
As an analytics engineer, any time I make a code change or push something to production, I would like to deal with the metadata—or the info in regards to the data. This involves writing validation queries to make sure things like row count, column count, and distribution of values look as they did before I pushed a change. Or, if I would like them to look different than they did before, they reflect those changes!
Although DevOps and DataOps sound similar, they serve two different purposes. In this text we are going to dive deeper into the differences, touching on the product they aim to serve and the various metrics of success.
DevOps involves the deployment and testing of software code changes. Once I worked as a DevOps engineer it often involved long deployment nights, testing code changes in many various environments, and validating the changes with the software engineers that made them.