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Modern data space is an exciting place with a lot of innovation these last years. The single drawback of that movement are all the new buzz words and the time required to understand and classify them into something we could use in the organization or not. Recently I see more and more "data contracts" in social media. It's also a new term and I'd like to see if and how it revolutionizes the data space.
PySpark has been getting interesting improvements making it more Python and user-friendly in each release. However, it's not the single Python-based framework for distributed data processing and people talk more and more often about the alternatives like Dask or Ray. Since both are completely new for me, I'm going to use this blog post to shed some light on them, and why not plan a deeper exploration next year?
And it shouldn't be, right? After all, it's "just" about using a Unit Test framework and defining the test cases. Well, that's "just" a theory!
The "Data is the new Oil" is one of popular sentences describing the huge role of data in our world. And as other resources, data must be extracted too. To find these "Oil workers", organizations look for, among others, data engineers. The task is more or less easier and this difficulty depends on various factors. From my 6-years perspective, one of the key starting elements is the job announcement.
Yesterday I shared with you the human part of my Data+AI Summit. It's time now to give you my takeaways from the technical talks.
There will be many "first times" in our lives. For me, the Data+AI Summit 2022 was the first time I've visited the USA, put the 3D dimensions to the pictures of my virtual friends and felt a huge community support in a very troubled moment. Besides, I also enjoyed the talks and walking, even though the latter one wasn't so good for my skin ;)
More and more often in my daily contact with the data world I hear this word "modern". And I couldn't get it. I was doing cloud data engineering with Apache Spark/Apache Beam, so it wasn't modern at all? No idea while I'm writing this introduction. But I hope to know more about this term by the end of the article!
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.
In my previous post I shared with you an approach called crypto-shredding that eventually can end up as a solution for the "right to be forgotten" point of GDPR. One of its drawbacks was performance degradation due to the need to fetch and decrypt every sensible value. To overcome it, I thought first about a cache but ended up by understanding that it's not the cache but something else! And I will explain this in the blog post.
Thanks to the most recent data regulation policies, we can ask a service to delete our personal data. Even though it seems relatively easy in a Small Data context, it's a bit more challenging for Big Data systems. Hopefully - under the authorization of your legal department - there is a smart solution to that problem called crypto-shredding.
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!
I wrote a lot of blog posts by chance, after losing myself on the Internet. It's also the case of the one you're currently reading. I looked for Delta Lake's learning resources and found an interesting schema depicting the Unified Data Management patterns. Since this term was something new for me, and I like everything with the "pattern" in the name, I couldn't miss the opportunity to explore this topic!
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.
In the previous blog post you discovered the first version of Data Vault methodology. But since the very first iteration, the specification evolved and a few years ago a version 2 was proposed. More adapted to the Big Data world, with several deprecation notes, and more examples adapted to the constantly evolving data world.
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.
If you hear "agile", "adapted to the changes", you certainly think about Scrum, Kanban and generally the Agile methodology. And you're correct but it's worth knowing that the agile term also applies to the data. More exactly, to the data modeling with the approach called Data Vault.
GoF Design Patterns are pretty easy to understand if you are a programmer. You can read one of many books or articles, and analyze their implementation in the programming language of your choice. But it can be less obvious for data people with a weaker software engineering background. If you are in this group and wondering what these GoF Design Patterns are about, I hope this article will help a bit.
Lambda architecture is one of the first officially defined Big Data architectures. However, after few time it was replaced by simpler approaches like Kappa. But despite that, you can still find the projects on Lambda and one of them which grabbed my attention is Project Oryx.
