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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.
Last year I wrote a blog post about a batch layer in streaming-first architectures like Kappa. I presented there a few approaches to synchronize the streaming broker with an object or distributed file systems store, without introducing the duplicates. Some months ago I found another architectural design that I would like to share with you here.
So far I played with Great Expectations and discovered the main classes. Today it's time to see how to automate our data validation pipeline.
In my previous post I presented a very simplified version of a Great Expectations data validation pipeline. Today, before going further and integrating the pipeline with a data orchestration tool, it's a good moment to see what's inside the framework.
When I published my blog post about Deequ and Apache Griffin in March 2020, I thought that there was nothing more to do with data validation frameworks. Hopefully, Alexander Wagner pointed me out another framework, Great Expectations that I will discover in the series of 3 blog posts.
I don't know whether it's a good sign or not, but I start having some convictions about building data systems. Of course, building an architecture will always be the story of trade-offs but there are some practices that I tend to prefer than the others. And in this article I will share my thoughts on one of them.
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.
Poor quality of data comes out in different forms. The incomplete datasets, inconsistent schemas, the same attribute represented in multiple formats are only some of the characteristics. Another point that I would like to address in this post, are duplicates.
Often in batch processing we give the pipeline some time to catch up late data, ie. the pipeline for 9 will be executed only at 11. One of methods to do so in Airflow is to compute delta on the tasks but there is a more "native" way with TimeDeltaSensor.
One of the greatest properties in data engineering is idempotency. No matters how many times you will run your pipeline, you will always end up with the same outcome (= 1 file, 1 new table, ...). However, this property may be easily broken when you need to evolve your pipeline. In this blog post, I will verify one possible way to manage it in Apache Airflow.
It has been a while since I didn't write about general distributed systems topics. That's the reason for this article where I will focus on the topic of an optimistic concurrency control.
One of the most powerful features of an orchestration system is the ability to ... yes, orchestrate different and apparently unrelated pipelines. But how to do so? By directly triggering a task or by using the data?
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