Big Data, Machine Learning, and Deep Learning Command Line Tools
Big Data, Machine Learning, and Deep Learning Command Line Tools
Even if you are not only running your pipeline through the command line, you can call these tools can be incredibly helpful for processing.
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Keep those hands on the keyboard! We can do a lot on OSX and Linux without touching a mouse or GUI. Awesome command line tools for *N*X derivatives have been around since day one, and have expanded to include Python, Go, NodeJS, and hybrid tools. Even if you are not only running your pipeline through the command line, you can call most of these tools from Apache NiFi for processing.
The book Data Science at the Command Line and GitHub offer an amazing set of quality tools to do a lot of pre- and post-processing and allow for a lot of transformations. I highly recommend looking at all of these amazing tools.
CSVKit is amazing! It does everything you need with comma-separated values. You can grab columns via cvs cut, filter by column with cvsgrip, extract data from Postgresql to CSV via sql2csv, grab a subset of columns with cols , and convert MS Excel to CSV with in2cv.
Quick Tool List
ImageMagick (edit, create, convert, flip, and alter images from the command-line).
XML2JSON via NodeJS.
Gatling for Testing with Scala/JVM.
CURL.
WGET.
MQTT CLI (NPM.JS).
You can also write some very short Python scripts to process data from the command line.
from nltk.sentiment.vader
import SentimentIntensityAnalyzer
import sys
sid = SentimentIntensityAnalyzer()
ss = sid.polarity_scores(sys.argv[1])
print('Compound {0} Negative {1} Neutral {2} Positive {3} '.format(ss['compound'], ss['neg'], ss['neu'], ss['pos']))Five line Python script for sentiment analysis.
You can even now debug TensorFlow via the command line (though this is really beta, so expect an occasional hiccup).
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