Python ETL and Data Warehouse
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README.rst

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Metrique

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Python Data Warehouse and Information Platform

metrique provides a simple python API to support ETL workloads for extracting data from disperate sources, iteratively, rapidly and reproducibly, with transparent, historical object persistence and tight clientside integration with popular python scientific computing libraries to faciliate creation and publication of a wide variety of analysis and reports, large and small.

Backends currently supported are as follows:
  • PostgreSQL (sqlalchemy)
  • SQLite (sqlalchemy)

Author: "Chris Ward" <cward@redhat.com> Sources: https://github.com/kejbaly2/metrique

Quick Install (auto-deploy -> virtenv)

The instructions given below assume fedora rpm package names:

# prerequisite *os* packages
sudo yum install python python-devel python-setuptools python-pip
sudo yum install openssl git gcc gcc-c++ gcc-gfortran
sudo yum install freetype-devel libpng-devel # matplotlib deps

# optional PostgreSQL
sudo yum install postgresql postgresql-devel postgresql-server

# make sure our core package managers are up2date
sudo pip install -U distribute setuptools

# our installation directory should always be a py virtualenv
sudo pip install virtualenv

# get metrique sources
git clone https://github.com/kejbaly2/metrique.git
cd metrique

# deploy metrique master branch into a virtual environment,
# including dependencies.
# NOTE this can take ~5-10 minutes to compile everything from source!
./metrique.py -V ~/metrique.master deploy --all --develop

# activate the virtual environment
source ~/metrique.master/bin/activate

./metrique.py firstboot metrique

# optional: setup default postgresql environment and start
./metrique.py firstboot postgresql
# optional: edit ~/.metrique/postgresql_db/*.conf
./metrique.py postgresql start

# launch ipython and start mining!