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Top 10 Data Engineering Blogs

I’ve always been surprised with the rise of data engineering and big data, how hard it is to find good data engineering content that is somewhat regular. Tech moves fast and I feel like data engineering moves even faster. There are always new tools and systems coming out with regular frequency, it’s hard to keep up with what’s hot and whats not. But, I still think it’s important to keep a finger on the pulse of what tech stacks are starting to take over (Spark) and what is fading into oblivion. So here is my top ten list of data engineering blogs, these are the places that I frequent so I at least know what’s going on in the world of data engineering.

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Musings on Python’s map() and filter()

I’ve always been surprised at the distinct lack of most Python code I’ve seen using the map() and filter() methods as standalone functions. I’ve always found them useful and easy to use, but I don’t often come across them in the wild, I’ve even been asked to remove them from my MR/PR’s, for no other reason then that they are supposedly ambiguous to some people? That’s got me thinking a lot about map() and filter() as related to readability, functional programming, side effects and other never ending debates where no one can even agree on the “correct” definition. Seriously. But, I will leave that rant for another time.

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4 DataWarehouse-ish Functions For Your PySpark Dataframes

Ever felt like just exploring documentation… seeing what you can find? That’s what you do on a cold, first snowstorm of the year Sunday afternoon. After the initial fun has warn off, the kids don’t want to go outside anymore, and Netflix has nothing new to offer up. So I thought I might as well spend some time poking around the PySpark Dataframe API, seeing what strange wonders I can uncover. I did find a few methods that took me back to my SQL Data Warehouse days. Memories of my old school Data Analyst and Business Intelligence days in Data Warehousing… the endless line of SQL queries being written day after day. Anyways lets dive into the 4 analytical methods you can call on your PySpark Dataframe, buried in the documentation like some tarnished gem.

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Intro to Apache Cassandra for Data Engineers

Who who? Apache Cassandra, who?

Hmm… yet another distributed database …. will it ever end? Probably not. It’s hard to keep up with them all, even the old ones. That brings me to Apache Cassandra. Of all the popular big data distributed databases Cassandra seems to be kind of that student who always sits in the back row and never says anything… you forget they are there…. until someone says their name….. Apache Cassandra. I honestly didn’t even know what space Cassandra fit in before trying to install and use it… so this should fun. What Is Cassandra? Distributed NoSQL.

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Database/SQL Fundamentals for Data Engineers

I’ve meet my fair share of snooty people who poo poo SQL and databases as second class hand-me-downs. I still remember talking to an academic computer science grad who was explaining to me how he refused to teach database classes, he was just too good for that. Whatever. Apparently refusing to accept how 90% of companies are able to operate as data driven businesses just isn’t important to some people. There is probably nothing more important in the tool belt of a data engineer than being above average at SQL and databases. Tuning queries, writing queries, indexing, designing data warehouses. I’m sure there are some Hadoop data engineers who skipped this step of RDBMS world, but that is not the normal path of a data engineer. Let’s dive into the fundamentals of SQL and databases.

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