https://github.com/DataBrewery/cubes
[NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
https://github.com/DataBrewery/cubes
cube data data-analysis data-warehouse multidimensional-analysis olap sql
Last synced: 3 months ago
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[NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
- Host: GitHub
- URL: https://github.com/DataBrewery/cubes
- Owner: DataBrewery
- License: other
- Created: 2011-01-10T22:30:20.000Z (over 14 years ago)
- Default Branch: master
- Last Pushed: 2022-04-29T06:53:17.000Z (about 3 years ago)
- Last Synced: 2024-10-29T21:59:14.293Z (8 months ago)
- Topics: cube, data, data-analysis, data-warehouse, multidimensional-analysis, olap, sql
- Language: Python
- Homepage: http://cubes.databrewery.org
- Size: 9.19 MB
- Stars: 1,491
- Watchers: 106
- Forks: 315
- Open Issues: 138
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES
- License: LICENSE
Awesome Lists containing this project
README
Cubes - Online Analytical Processing Framework for Python
=========================================================
[](https://gitter.im/DataBrewery/cubes?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://flattr.com/submit/auto?user_id=Stiivi&url=https://github.com/databrewery/cubes&title=Cubes&language=&tags=github&category=software)
Cubes is a light-weight Python framework and set of tools for Online
Analytical Processing (OLAP), multidimensional analysis and browsing of
aggregated data.
*Focus on data analysis, in human way*
Overview
========
Purpose is to provide a framework for giving analyst or any application
end-user understandable and natural way of presenting the multidimensional
data. One of the main features is the logical model, which serves as
abstraction over physical data to provide end-user layer.
Features:
* OLAP and aggregated browsing (default backend is for relational databse -
ROLAP)
* multidimensional analysis
* logical view of analysed data - how analysts look at data, how they think of
data, not not how the data are physically implemented in the data stores
* hierarchical dimensions (attributes that have hierarchical dependencies,
such as category-subcategory or country-region)
* localizable metadata and data
* SQL query generator for multidimensional aggregation queries
* OLAP server – HTTP server based on Flask Blueprint, can be [easily
integrated](http://pythonhosted.org/cubes/deployment.html) into your
application.
Download
========
Current recommended version is 1.1.x. It hasn't been yet tagged so please use the *master* branch.
This version includes SQL backend support out of the box, and other backends have been moved to separate
projects (ie. MongoDB). This branch (currently *master*) will be soon tagged as 1.1 release.
Previous stable version was 1.0.1. This version included all backend types, but no further
development will be done on this branch.
Documentation
=============
[Latest documentation](http://cubes.readthedocs.org/en/latest)
Examples
--------
See `examples` directory in the source code repository
for simple examples and use-cases.
See https://github.com/DataBrewery/cubes-examples
for more complex examples.
Models
------
For cubes models see
https://github.com/DataBrewery/cubes-models
Development
============
Source code is in a Git repository [on GitHub](https://github.com/DataBrewery/cubes)
git clone git://github.com/DataBrewery/cubes
After you've cloned, you might want to install all of the development dependencies.
pip install -e .[dev]
Build the documentation like so. ::
cd doc
make help
make html
Outputs will go in ``doc/_*``.
Requirements
------------
Python >= 2.7 and Python >= 3.4.1
Most of the requirements are soft (optional) and need to be satisfied only if
certain parts of cubes are being used.
* SQLAlchemy from http://www.sqlalchemy.org/ version >= 0.7.4 - for SQL
backend
* Flask from http://flask.pocoo.org/ for Slicer server
* Jinja2 from http://jinja.pocoo.org/docs/ for HTML presenters
Support
=======
If you have questions, problems or suggestions, you can send a message to the
[Google group cubes-discuss](http://groups.google.com/group/cubes-discuss).
IRC channel #databrewery on server irc.freenode.net
Report bugs using [github issue
tracking](https://github.com/DataBrewery/cubes/issues).
Development
-----------
If you are browsing the code and you find something that:
* is over-complicated or not obvious
* is redundant
* can be done in better Python-way
... please let it be known.
Authors
=======
Cubes is written and maintained by Stefan Urbanek (@Stiivi on Twitter)
and various contributors. See AUTHORS file for more
information.
License
=======
Cubes is licensed under MIT license. For full license see the LICENSE file.