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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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Golang – Useful for everyday Data Engineering?

I periodically try to pick up a new programming language on my journey through Data Engineering life. There are many reasons to do that, personal growth, boredom, seeing what others like, and helping me think differently about my code. Golang has been on my list for at least a year. I don’t hear much about it in the Data Engineering world myself, at least in the places I haunt like r/dataengineering and Linkedin.

I know tools like Kubernetes and Docker are written with Go, so it must be powerful and wonderful. But, what about Data Engineering work … and everyday Data Engineering work at that, is Go useful as an everyday tool for everyday simple Data Engineering tasks? Read on my friend.

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Data Quality – Great Expectations for Data Engineers

Mmmm … Data Quality … it is a thing these days. I look forlornly back to the ancient days of SQL Server when nobody cared about such things. Alas, we live in a different world, where hundreds of terabytes of data are the norm, and Data Quality becomes a thing. I’ve been meaning to give Great Expectations a poke for like a year, but just haven’t had the time or inclination to do so, but times are changing, and so should I.

I’m not really planning on giving an in-depth guide to Data Quality with Great Expectations, what I’m more interested in are topics like, how easy is it to set up and use, what’s the overhead, what are the main features and concepts and are they easy to understand. I find this sort of review of Data Engineering tools to be more helpful than simply a regurgitation of the documentation.

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Databricks Access Control – The 3 Most Important Steps

It’s not often I yearn for the good old days of SQL Server, but I’ve had a few of those moments lately. Some things I miss, some I don’t, and it’s probably because I’m getting old and crusty, stuck in my ways, by permissioning is one of those topics where I think about the good old days. Data access control and permissions are topics that we all kinda ignore as not that important … until we actually are trying to do something with them. Then all of sudden we start complaining about complexity and why this isn’t easier. That’s a good way to describe my reaction to having to work on Databricks Access control.

But, I learned a few things and I think they will be helpful for someone. Read on for the basics of handling permissions and access control in Databricks and Delta Lake.

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boto3 or aws cli for s3 … Python vs Bash and other thoughts.

For any Data Engineer working on aws for any length of time, there is one task that always seems to come up and never go away. Manipulating files on s3 a bucket on aws is something I’ve had to do for years, it just never goes away. It’s always something … listing files, moving files, copying files, checking for files, getting the last modified file, checking file sizes, downloading files … it pretty much never ends.

Luckily aws provides a few tools to make these easy, their handy cli for command-line work, or the trusty boto3 Python package. I want to give an introduction to the common commands Data Engineers have to run with both the aws cli and boto3 to perform various common tasks. We will then compare and contrast which tool to use in our pipelines and the pros and cons of each.

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Part 4 – Keys To Success – Idempotency and Partitioning.

As the road winds on we come to Part 4, of our 5 Part Series on Data Warehouses, Lakes, and Lake Houses. Finally, we are getting to some fun topics after all the boring stuff. Today I want to talk about the two keys to success in your Data Lakes … Idempotency and Partitioning. I firmly believe these two concepts are the cornerstones of the new exciting, or not-so-exciting world of Data Lakes and Lake Houses, without which your data and pipelines go the way of the dodo.

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Databricks + Delta Lake MERGE duplicates – Deterministic vs Non-Deterministic ETL.

Is there any problem more classic to the Data Lakes and Data Warehouses than duplicate records? You would think after doing the same ETL for over a decade I could avoid the issue, apparently not. It’s good never to think too highly of one’s self, the duplicates can get us all. Today I want to talk about a wonderful feature of Databricks + Delta Lake MERGE statements that are perfect for quietly and insidiously injecting duplicates into your Data Warehouse or Data Lake. This is a great trick to play on your unsuspecting coworkers.

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End-to-End Pipeline Integration Testing for Databricks + Delta Lake.

The testing never ends. Tests tests tests, and more tests. When it comes to data engineering and data pipelines it seems good practices are finally catching up after years. In the past, the data engineering community took a lot of heat, and rightly so, for not adopting good software engineering principles, especially in data pipelines.

In the defense of many data engineers, because of the varied backgrounds people come from, some were never taught or realized the importance of good software design and testing practices. Sure, it always “takes more time” upfront to design data pipelines with code that is functional and unit-testable, and worse, able to be integration tested from end to end. It requires some foresight and thought in both data architecture and pipeline design to enable complete testability.

Integration testing end-to-end in an automated manner is a tough nut to crack. How can you do such a thing on massive pipelines that crunch hundreds of TBs of data? With a little creativity.

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