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New to PySpark? Do this, not that.

Photo by Aziz Acharki on Unsplash

Do this, not that. Well, I’ve got my own list. With everyone jumping on the PySpark / Databricks / EMR / Glue / Whatever bandwagon I thought it was long overdue for a post on what to do, and not to do when working with Spark / PySpark. I take the pragmatic approach to working with Spark, it’s honestly very forgiving well and far into the 10s of TBs of data. Once you wander past that point things tend to get a little spicy if you don’t have it all dialed in. As with most things in life if you get a few things right, and of course don’t do some things, that will get you a long way, the same applies to Spark.

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Introduction to dbt … for Data Engineers.

Photo by Josh Rakower on Unsplash

So, you’ve heard about dbt have you. I honestly can’t decide if it’s here to stay or not, probably is, enough folks are using it, and preaching about it. I personally have always been a little skeptical of dbt, not because it can’t do what it says it can do, it can, but because I’m old and bitter from my many years of Data Engineering, and I always see the problems in things.

But, I will let you judge that for yourself. Today I want to give a brief overview of dbt, kick the tires, muse about its features, and most importantly, look at dbt from a Data Engineering perspective, ferret out the good, the bad, and the ugly. I will try my best to be nice but don’t count on it. Code is on GitHub.

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Soda-Core. Data Quality at Scale.

Photo by Tim Schmidbauer on Unsplash

Ever since playing with Great Expectations with Spark some time ago, I’ve been on the lookout for more Data Quality at-scale tools. The market still has a long way to go with these tools, not enough options, hard to use, and the typical Data Engineering travails. I came across soda-core recently, a self-proclaimed…

“Data reliability testing for SQL- and Spark- accesssible data.“

soda-core docs

Doing anything at scale, well … that’s usually the problem. Data Quality and Observability are topics were hear a lot about these days. The reality often doesn’t meet the expectations most of the time. Even Great Expectations, being awesome, can get complicated real quick-like. Let’s hope that soda-core pair with Spark can show us some real promise. Code available on GitHub.

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DataFusion courtesy of Rust, vs Spark. Performance and other thoughts.

Photo by Benjamin Wedemeyer on Unsplash

I think it’s funny that DataFrames are so popular these days, I mean for good reason. They are a wonderful and intuitive way to work with and on datasets. Pandas … the nemesis of all Data Engineers and the lover of Data Scientists. Apache Spark is really the beast that brought DataFrames to the masses. Even those little buggers over at Apache Beam give you DataFrames.

Of course, when anything gets popular, you start getting little things that start to pick and peck at the heels. I would probably say that is what DataFusion with Rust seems to be. Seems more like a contender against Pandas rather than Spark to me. I guess if you’re just using Spark locally or on a single node, sure you could consider using DataFusion. Code available on GitHub.

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5 things I wish I knew about Databricks … before I started.

Photo by Saad Chaudhry on Unsplash

How many times in your life, that is but a mist, have you thought, “If I had only known that in the beginning?” I feel as if I’ve committed that cardinal sin as a developer and Data Engineer … falling in love with a tool to the exclusion of all else. I mean truly, Databricks has brought Big Data to the masses, all you need is your laptop and 10 minutes of PySpark training before your spending gobs of money, processing massive amounts of data. Where else, and with what else can you do such things? Try it with EMR, good luck to you.

That being said, when you love something you start to notice the slight imperfections and problems with that something. You get kinda nit-picky. Such is life. I want to save some poor soul out there some heartache, that moment when you’ve been writing code for hours or days, and come upon a little surprise that makes your heart drop into your shoes, and the blood runs to your face. Here are 10 things I wish I knew about Databricks before I started. Maybe it will save you time, help you, who knows.

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Exploring Delta Lake’s ZORDER, and Performance. On Databricks.

Photo by Joel Holland on Unsplash

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.

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Great Expectations with Databricks and Apache Spark. A Tale of Data Quality.

Photo by Joshua Sortino on Unsplash

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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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 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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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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