Apache Spark on Google Colaboratory

Google recently launched a preview of Colaboratory, a new service that lets you edit and run IPython notebooks right from Google Drive – free! It’s similar to Databricks – give that a try if you’re looking for a better-supported way to run Spark in the cloud, launch clusters, and much more.

Google has published some tutorials showing how to use Tensorflow and various other Google APIs and tools on Colaboratory, but I wanted to try installing Apache Spark. It turned out to be much easier than I expected. Download the notebook and import it into Colaboratory or read on…

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Writing Huge CSVs Easily and Efficiently with PySpark

I recently ran into a use case that the usual Spark CSV writer didn’t handle very well – the data I was writing had an unusual encoding, odd characters, and was really large.

I needed a way to use the Python unicodecsv library with a Spark dataframe to write to a huge output CSV file.

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Jupyter Notebooks with PySpark on AWS EMR

One of the biggest, most time-consuming parts of data science is analysis and experimentation. One of the most popular tools to do so in a graphical, interactive environment is Jupyter.

Combining Jupyter with Apache Spark (through PySpark) merges two extremely powerful tools. AWS EMR lets you set up all of these tools with just a few clicks. In this tutorial I’ll walk through creating a cluster of machines running Spark with a Jupyter notebook sitting on top of it all.

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Vowpal Wabbit – Ramdisk vs. EBS-Optimized SSD

Recently I started playing around with Vowpal Wabbit and various data sets. Vowpal Wabbit promises to be really fast, so much so that disk IO is one of the most common bottlenecks according to the author. I did a quick test to see if using a RAM disk would make Vowpal Wabbit’s training faster. However, a RAM disk is not a silver bullet that will make Vowpal Wabbit faster, at least in my quick testing.

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Data Science from Scratch – Microreview

I recently finished reading Data Science from Scratch by Joel Grus. This book is a great introduction to data science concepts. It uses real code to demonstrate complex Python, data analytics, data science, and machine learning concepts.

I’m really glad I picked up this book as the first book I’ve read about machine learning. There was a great combination of mathematics, statistics, and real applications of machine learning algorithms.

The book starts out with a quick introduction to Python, followed by an in-depth review of all the math you need for the code to make sense.

If you’re looking for a book that’ll show you how to use Tensorflow or scikit-learn, this book is not for you. I’d recommend reading this book before diving into those. You’ll learn about the math behind popular machine learning libraries and implement basic versions of some of the most popular algorithms from scratch.

I think the next book I’ll pick up after this one is Python Data Science Handbook which will go into more detail on using a bunch of Python libraries to do some of this machine learning for me.

Jekyll in Docker

Recently I’ve been playing around with Jekyll to create some simple websites. I’ve used Jekyll in the past and I remember that the set-up was a multi-step process.

Jekyll is a Ruby application that uses several Gems and Bundler. That means installing several dependencies. In my case I don’t have a Ruby development environment already set up, so I would have to install all these packages just to use a static site generator.

Then I found the official Jekyll Docker image.

I already have Docker installed to play around with other containers, so downloading a Jekyll container and using it was as easy as:

docker run --rm --label=jekyll --volume=$(pwd):/srv/jekyll \
  -it -p 127.0.0.1:4000:4000 jekyll/jekyll jekyll serve

That’s all there is to it. This command will download the latest Jekyll image and start serving your site. No need to install Ruby, Gem, Bundler, or a bunch of other dependencies.