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PySpark SQLContext….tired of your decades old ETL process?

Seriously. Haven’t you had enough of SSIS, SAP Data Services, Informatica, blah blah blah? It’s been the same old ETL process for the last 20 years. CSV files appear somewhere, some poor old aged and angry Developer soul in a cubicle pulls up the same old GUI ETL tool, maps a bunch of columns to some SQL Server, if you’re in a forward thinking shop…maybe Postgres. This is after painstakingly designing the Data Warehouse with good ole’ Kimball in mind. Data flows from some staging table to some facts and dimensions. Eventually some SQL queries are run and a Data Mart is produced summarizing a years worth of data for a crabby Sales or Product department. Brings a tear to my eye. And this is all because Apache Spark sounds scary to some people?

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Create Your Very Own Apache Spark/Hadoop Cluster….then do something with it?

I’ve never seen so many posts about Apache Spark before, not sure if it’s 3.0, or because the world is burning down. I’ve written about Spark a few times, even 2 years ago, but it still seems to be steadily increasing in popularity, albeit still missing from many companies tech stacks. With the continued rise os AWS Glue and GCP DataProc, running Spark scripts without managing a cluster has never been easier. Granted, most people never work on datasets large enough to warrant the use of Spark.. and Pandas works fine for them. Also, very annoyingly it seems most videos/posts on Spark about shuffling/joins blah blah that make no sense to someone who doesn’t use Spark on daily basis, or they are so “Hello World” as to be useless in the real world. Let’s solve that problem by setting up our own Spark/Hadoop cluster and doing some “real” data things with it.

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Learning to do HTTP in Scala. – Part Trois.

Man. Every time I open IntelliJ to write/learn some more Scala I have to take a deep breath. Yes, it’s been fun and good for my to my brain feel like in a Doctor Strange movie, but it’s also challenging and frustrating at times. One of the things I find myself doing a lot as a Data Engineering is HTTP stuff, mostly pulling files or data from APIs. Doing this work in Python is most enjoyable and easy, I’ve been curious to see how Scala handles HTTP stuffy stuff.

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Deploying Apache Airflow inside Kubernetes.

Has anyone else noticed how popular Apache Airflow and Kubernetes have become lately? There is no better tool than Airflow for Data Engineers to built approachable and maintainable data pipelines. I mean Python, a nice UI, dependency graphs/DAGs. What more could you want? There is also no better tool than Kubernetes for building scalable, flexible data pipelines and hosting apps. Like a match made in heaven. So why not deploy Airflow onto Kubernetes? This is what you wish your mom would have taught you. It’s actually so easy your mom could probably do it….maybe she did do it and just never told you?

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SQL Database (RDBMS) Design for Data Engineers

Database design… hmmm. There is probably nothing more all over the board in tech. Data warehousing, analytics, OLTP… everyone with their own “defend this hill to the death” ideas. Kimball vs Inmon. Hmmm.. what to do, what to do? After defending my own hills to the death over the years and arguing over whiteboards I’ve come to a conclusion. The right answer is somewhere in the middle. Understanding a few basic design principals will help any data engineer master writing DDL for anything from a Data Warehouse to a high load OLTP systems… across all RDBMS platforms.

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Httpx vs Requests in Python. Performance and other Musings.

Comparing the pypi requests vs httpx packages, who will fall on their face?

Someone recently brought up the new kid on the block, the httpx python package for http work of course. I mean the pypi package
requests has been the de-facto standard forever. Can it really be overthrown? Is this a classic case of “oh how the mighty have fallen”? I want to explore what the new httpx package has to offer, but mostly just …. which one is faster. That is what data engineers really care about.

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How Chuck Norris Proved Async in Python isn’t Worthy.

Chuck Norris proved that Async Python is slower and suckier than Process and Thread Pools.

There are some things I will never understand. Async in Python is one of them. Yes, sometimes I use it, but mostly because I’m bored and we all should have some kind of penance. Async in mine. It’s slow, confusing, other people get mad at you when they have to debug your Async code. I’ve always wondered why anyone would choose a single Python process to do a bunch of work instead of …… more then one? I decided there is only one person who could solve the interwebs Async arguments, Chuck Norris.

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My Journey from Python to Scala – Part Deux

In Part 1 of my laborious journey from Python to Scala, I did some work with file operations, CSV files, and messing with the data. It took me a little longer then I expected to wrap my head around the Scala functional/object/immutable approach to software design. But, in the end if felt satisfying and I’m starting to be a convert. Scala makes you think a little harden then Python, is less forgiving, and requires more of you as the developer. In part deux, I figured the next topic to grapple with some simple retrieval of remote files and writing those files to disk. Also, I wanted to take a crack at Classes in Scala.

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Solving the Memory Hungry Pandas Concat Problem.

One of the greatest tools in Python is Pandas. It can read about any file format, gives you a nice data frame to play with, and provides many wonderful SQL like features for playing with data. The only problem is that Pandas is a terrible memory hog. Especially when it comes to concatenating groups of data/data frames together (stacking/combing data). Just google “pandas concat memory issues” and you will see what I mean. Basically what it comes down to is that Pandas becomes quickly useless for anyone wanting to work on “big data” unless you want to spend $$$ on some machines in the cloud with tons-o-ram.

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My Journey from Python to Scala – Part 1

UPDATE: If you want to know how my Scala SHOULD have been written. Check out this link!

I feel like a frontiersmen heading west, into the unknown. I’ve been successful using Python as a Data Engineer for some time, processing terabytes of data with what “real” programmers sneer at as barely even a real language. Whatever. But, some of my favorite tools, like Spark, are written in Scala, and it’s on the rise, so I should probably join the lemmings in their mad dash. If for no other reason then to expand my horizons.

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