• Version: 0.8
  • Released: Aug 2018

Eskapade is a light-weight, python-based data analysis framework, meant for modularizing all sorts of data analysis problems into reusable analysis components. For documentation on Eskapade, please go to this link.

Eskapade-Spark is the Spark-based extension of Eskapade. For documentation on Eskapade-Spark, please go here.

Release notes

Version 0.8

Version 0.8 of Eskapade-Spark (August 2018) is a split off of the spark-analysis module of Eskapade v0.7 into a separate package. This way, Eskapade v0.8 no longer depends on Spark. This new package Eskapade-Spark does require Spark to install, clearly.

In addition, we have included new analysis code for processing (“flattening”) time-series data, so it can be easily used as input for machine learning models. See tutorial example esk611 for details.



Eskapade-Spark requires Python 3.5+, Eskapade v0.8+ and Spark v2.1.2. These are pre-installed in the Eskapade docker.


To install the package from pypi, do:

$ pip install Eskapade-Spark


Alternatively, you can check out the repository from github and install it yourself:

$ git clone eskapade-spark

To (re)install the python code from your local directory, type from the top directory:

$ pip install -e eskapade-spark


After installation, you can now do in Python:

import eskapadespark

Congratulations, you are now ready to use Eskapade-Spark!

Quick run

To see the available Eskapade-Spark examples, do:

$ export TUTDIR=`pip show Eskapade-Spark | grep Location | awk '{ print $2"/eskapadespark/tutorials" }'`
$ ls -l $TUTDIR/

E.g. you can now run:

$ eskapade_run $TUTDIR/

For all available examples, please see the tutorials.

Contact and support

Contact us at: kave [at] kpmg [dot] com

Please note that the KPMG Eskapade group provides support only on a best-effort basis.