Recently you've discovered the building blocks of a feature store. This time I would like to demonstrate a feature store in action. I've chosen Feast as a playground use case because it's Open Source, has good working examples, and implements an Apache Spark ingestion job!
Feature stores are a more and more common topic in the data landscape and they have been in my backlog for several months already. Finally, I ended up writing the blog post and I really appreciated the learning experience! Even though it's a blog post from a data engineer perspective, so maybe without a deep data science deep dive.
I wrote this blog post a week before passing the GCP Data Engineer exam, hoping it'll help to organize a few things in my head (it did!). I also hope that it'll help you too in understanding ML from a data engineering perspective!
After introducing DataOps concepts, it's a good time to share my feelings on them ?
"DataOps", this term is present in my backlog since a while already and I postponed it multiple times. But I finally found some time to learn more about it and share my thoughts with you.
Few weeks ago I got a comment asking me about the recommended data engineering books. I mentioned few of them in Becoming a data engineer - a feedback of my journey blog post but without explaining why. I will try to complete that in this blog post then.
Recently a reader asked me in a PM about the things to know and to learn before starting to work as a data engineer. Since I think that my point of view may be interesting for more than 1 person (if not, I'm really sorry), I decided to write a few words about it.
