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Review of Prefect for Data Engineers

Not going to lie, I do enjoy the vendor wars that this marketing craze called “The Modern Data Stack” has created. I like to keep just about everything in life at arm’s length. Kinda like the way you look at your crazy third cousin out of the corner of your eye at the family reunion. I mean it’s nice to have all these options to choose from these days when building data pipelines.

One tool I haven’t been able to poke the tires on yet is Prefect. It appears to be another data orchestration tool for Python, but we shall find out. I want this to be an introduction to Prefect, we shall just try it out and let the chips fall where they may.

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Reducing Complexity with Databricks + Delta Lake COPY INTO

As the years drag by in Data Engineering, there are a few things that I have come to appreciate more and more. One of those topics that is close to number one on the list is complexity reduction. Today’s modern data stacks are filled to the brim with technologies and tools, full to the brim, and overflowing. So many tools with such wonderful features, sometimes all the magic comes with a downside. Complexity. Complexity can turn something wonderful into a nightmare.

Reducing (not avoiding) complexity seems to be one of the main tenets I work on these days when designing resilient, reliable, and repeatable data pipelines that can process terabytes of data. One of those tools is COPY INTO feature of Databricks + Delta Lake.

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Data Pipelines 101 – The Basics.

I’ve been getting a lot of questions lately about data pipelines, how to design them, what to think about, and what patterns to follow. I get it, if you’re new to Data Engineering it can be hard to know what you don’t know. There is a lot of content specific to certain technologies, but not as much around some basics, especially data pipelines. Where do you even start? Are there common patterns that can be followed and used in all data pipelines regardless of tech stack?

Let’s dive into data pipelines 101, and call it an “Introduction to Data Pipelines.” What to know where to start and what to look out for? Start here.

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