I mean, I’m sort of being facetious and sort of not. I mean there is some truth that rings out in those words. I’m sure someone selling Data Observability tools, or writing Great Expectations all-day will not like the idea of relying on only 2 data validations. But honestly, these two are probably more than 80% of Data Teams are using today for validation, which is none. What 2 are you? Glad you asked.
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I think Delta Lake is here to stay. With the recent news that Databricks is open-sourcing the full feature-set of Delta Lake, instead of keeping the best stuff for themselves, it probably has the most potential to be the number one go-to for the future of Data Lakes, especially within those organizations that are heavy Spark users.
One of the best parts about Delta Lake is that it’s easy to use, yet it has a rich feature set, making it a powerful option for Big Data storage and modeling. One of those features that promise a lot of performance benefits is something called ZORDER. Today I want to explore more in-depth what ZORDER is, when to use it, when not to use it, and most importantly test its performance during a number of common Spark operations.
Just when I think it cannot get more popular, it does. I have to admit, PySpark is probably the best thing that ever happened to Big Data. It made what was once a myth, approachable to the average person. No need for esoteric Java skills, no more MapReduce, just plain old Python. Another amazing thing about Spark in general, and by extension PySpark, is the sheer amount of out-of-the-box capabilities. I wanted to dedicate this post to a few amazing and wonderful features of PySpark that make Data Engineering fun and powerful.
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It still seems like the wild west of Data Quality these days. Tools like Apache Deque are just too much for most folks, and Data Quality is still new enough to the scene as a serious thought topic that most tools haven’t matured that much, and companies dropping money on some tool is still a little suspect. I’ve probably heard more about Great Expectations as a DQ tool than most.
With the popularity of PySpark as a Big Data tool, and Great Expectations coming into its own, I’ve been meaning to dive into what it would actually look like to to use Great Expectations at scale and answer some simple questions. How easy is it to get up and running with Spark, what’s the path of least resistance to getting some basic Data Quality checks in place in a data pipeline.
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My newsfeed these days is chock-full of “how to break into Data Engineering” these days. It’s made me a bit nostalgic, to say the least. I’ve been dreaming about those days gone by when I started out in the data world. I would say my experience was not so much “breaking in”, but more of a “weasel my way into” Data Engineering.
I didn’t get a Computer Science degree, not even close. I think there are many ways to get into Data Engineering, it’s probably easier than it ever has been in the past. We will fulfill our destiny in different ways, and that journey gives us a unique perspective and makes us “good” at certain things. This is my story.
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Probably one of the hardest hurdles to jump over when starting out in anything new, including Data Engineering and Data Pipelines, is knowing where to start. It always can be a little daunting. One aspect that can make or break any project, giving you the confidence to move forward like Sparticus to conquer, is having a good project template for your repository of code and logic that will encapsulate and present your code to others.
I’ve created a free and hopefully helpful Python blank GitHub project template that you can clone, change, and steal to your heart’s desire. I hope it will be helpful and set you going in the right direction for your next project.
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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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.
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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