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Apache Airflow Big Data algorithms Big Data problems - solutions Data engineering patterns General Big Data General data engineering Graphs SQL
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As a data engineer you're certainly familiar with data skew. Yes, this bad phenomena where one task takes considerably more input than the others and often causes unexpected latency or failures. Turns out, stream processing also has its skew but more related to time.
It's technically possible to process files in a continuous way from a streaming job. However, if you are expecting some latency sensitive job, this will always be slower than processing data directly from a streaming broker. Why?
If you're interested in stream processing, I bet your thinking is technology-based. It's not wrong, after all, the ability to use a tool gives you and me a job. However, for a long-term consideration it's better to reason in terms of patterns or models. Being aware of a more general vision helps assimilate new tools.
I must admit it, if you want to catch my attention, you can use some keywords. One of them is "stream". Knowing that, the topic of my new blog post shouldn't surprise you.
Even though nowadays data processing frameworks and data stores have smart query planners, they don't take our responsibility to correctly design the job logic.
One week later than initially announced, but here it is, the second part for Data+AI Summit 2023 retrospective. I don't know how, but I managed to include some streaming-related talks here too!
Even though you may be thinking now about Data+AI Summit 2024, I still owe you my retrospective for the 2023 edition. Let's start with the first part covering stream processing talks!
In our social media and marketing-driven era, it's quite hard to get things right. For me there is one common misconception brought by the Modern Data Stack idea that everything should be now ELT. In fact no, it shouldn't but only can.
That's the conference I've heard only recently about. What a huge mistake! Despite the lack of "data" word in the name, it covers many interesting data topics and before I share with you my notes from this year's Data+AI Summit, let me do the same for Berlin Buzzwords!
Welcome to the 3rd part of the series with great streaming and project organization blog posts summaries!
After a 2-years break, I had a chance to speak again, this time at the Big Data Warsaw 2023. Even though I couldn't be in Warsaw that day, I enjoyed the experience and also watched other sessions available through the conference platform.
Welcome to the 2nd part of the series with great streaming and project organization blog posts summaries!
Having a scalable architecture is the nowadays must but sometimes it may not be enough to provide consistent performance. Sometimes the business requirements, such as consistent delivery time or ordered delivery, can add some additional overhead. Consequently, scalability may not suffice. Fortunately, there are other mechanisms like backpressure that can be helpful.
Hi and welcome to the new series. This time I won't blog about my discoveries. Instead, I'm going to see other blog posts from the data engineering space and share some key takeaways with you. I don't know how regular it will be yet but hopefully will be able to share some of the notes every month.
Welcome to the 2nd blog post dedicated to Apache Airflow 2 features. This time it'll be more about custom code you can add to the most recent version.
Apache Airflow 2 introduced a lot of new features. The most visible one is probably a reworked UI but there is more! In this and the next blog post I'll show some of the interesting new Apache Airflow features.
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.
