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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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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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Hey Google Cloud, ever heard of Boto3? Come on.

Why can’t GCP come up with their own Boto3?

First, let’s set the record straight. GCP is better than AWS. This will be clear to anyone who has used both services for a reasonable amount of time. GCP was built with the developer in mind, the services and tools offered work better, are cleaner, and way simplier. But, there is one thing that is totally annoying. Where is GCP’s answer to AWS’s Python Boto3 library? I mean seriously. Boto3 is the one stop shop to plugin and interact with pretty much every AWS service available, and the documentation is reasonable. Seriously GCP, where you at?

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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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Big Data File Showdown – Avro vs Parquet with Python.

Apache Parquet vs Apache Avro

There comes a point in the life of every data person that we have to graduate from csv files. At a certain point the data becomes big enough or we hear talk on the street about other file formats. Apache Parquet and Apache Avro are two of those formats that been coming up more with the rise of distributed data processing engines like Spark.

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Challenges of Machine Learning Pipelines at Scale… When You Don’t Work at Google.

Complexity is in the eye of the beholder.

ml pipelines

Building Machine Learning (ML) pipelines with big data is hard enough, and it doesn’t take much of a curve ball to make it a nightmare. Most of what you will read online are tutorials on how to take a few CSV files and run them through some sklearn package. If you are lucky, you might find some “big data” ML stories on Medium where someone uses Spark to crunch a bunch of JSON, Parquet, or CSV files at scale of 10 to a few hundred gigabytes of data. Usually they are simplistic and ambiguous. Unfortunately that isn’t how it works in the real world.

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