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Apache Airflow Big Data algorithms Big Data problems - solutions Data engineering patterns Databricks General Big Data General data engineering Graphs SQL
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Previously we learned how to control data quality with Delta Live Tables. Now, it's time to see an open source library in action, Spark Expectations.
Data quality is one of the key factors of a successful data project. Without a good quality, even the most advanced engineering or analytics work will not be trusted, therefore, not used. Unfortunately, data quality controls are very often considered as a work item to implement in the end, which sometimes translates to never.
Data contracts was a hot topic in the data space before LLMs and GenAI came out. They promised a better world with less communication issues between teams, leading to more reliable and trustworthy data. Unfortunately, the promise has been too hard to put into practice. Has been, or should I write "was"?
If you have been working with Apache Airflow already, you certainly met XComs at some point. You know, these variables that you can "exchange" between tasks within the same DAG. If after switching to Databricks Workflows for data orchestration you're wondering how to do the same, there is good news. Databricks supports this exchange capability natively with Task values.
For over two years now you can leverage file triggers in Databricks Jobs to start processing as soon as a new file gets written to your storage. The feature looks amazing but hides some implementation challenges that we're going to see in this blog post.
Welcome to the second blog post dedicated to the previous Data+AI Summit. This time I'm going to share with you a summary of Apache Spark talks.
Welcome to the first Data+AI Summit 2024 retrospective blog post. I'm opening the series with the topic close to my heart at the moment, stream processing!
Last May I gave a talk about stream processing fallacies at Infoshare in Gdansk. Besides this speaking experience, I was also - and maybe among others - an attendee who enjoyed several talks in software and data engineering areas. I'm writing this blog post to remember them and why not, share the knowledge with you!
The blog shares the last fallacies for my 7 years stream processing journey.
Last week I was speaking in Gdansk on the DataMass track at Infoshare. As it often happens, the talk time slot impacted what I wanted to share but maybe it's for good. Otherwise, you wouldn't read stream processing fallacies!
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!
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