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Delta Lake without Spark (delta-rs). Innovation, cost savings, and other such matters.

The intersection of Big Data and Not Big Data. An interesting topic of late that has been rattling around in my overcrowded head is the idea of Big Data vs Not Big Data, and the intersection thereof. I’ve been thinking about SAAS vendors, the Modern Data Stack, costs, and innovation. A great real-life example of […]

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New to PySpark? Do this, not that.

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 […]

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5 Years of Blogging – Most Popular Articles, Traffic Stats, and other Thoughts.

Sometimes I feel like I’ve been doing this too long, life gets busy, and I don’t have much to say … but here I am 5 years later. I’m still making people mad and making a fool of myself, some things never change. This will probably be short and sweet. I will cover the top […]

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Introduction to dbt … for Data Engineers.

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 […]

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Introduction to Designing Data Load Patterns.

When I think back many moons ago, to when I started in Data Engineering world … even though it went by many different names back in the olden days … I didn’t know what I didn’t know. All those years ago Kimball’s Data Warehouse Toolkit was probably the only resource really available at the time […]

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Machine Learning from the viewpoint of an average Data Engineer.

I’ve been thinking more about the topic of ML and MLOps lately. To me, it seems like the buzz has quieted down over the last few years about ML and MLOps, at least somewhat, in favor of other topics like Data Quality, Data Lakes, Data Contracts, and the like. I’ve been wondering why this is […]

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Gandalf’s Beard! DataFrames in Golang.

I’m not sure if DataFrames in Golang were created by Gandalf or by Saruman, it is still unclear to me. I mean, if I want a DataFrame that bad … why not just use something normal like Python, Spark, or pretty much anything else but Golang. But, I mean if Rust gets DataFusion, then Golang […]

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8 Data Engineering Best Practices

Best practices are always a touchy subject, I’m going to forget someone’s pet best practice, I can already feel it. I’ve always been a firm believer in the basics, keeping things simple. I also ascribe to the 80/20 rules, and I don’t think Data Engineering is any different in that respect. Learning to do a […]

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Soda-Core. Data Quality at Scale.

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 […]

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

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. […]

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