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Light-weight Python OLAP framework for multi-dimensional data analysis
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Merge pull request #303 from robin900/rbt-fix-sqlite-travis

travis, sqlite3 version, 2.7 compat fixes for tests and ci.
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bin [slicer] moved into the package
cubes Fixed typo. Closes #296
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goodies Added some syntax candy
incubator/modeler Separated dictionary metadata management
models Renamed date/time to base_date/base_time and moved to internal dir
tests fixed typo that had preserved multirow insert in one test.
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LICENSE moved cross_table to aggregation results; minor fixes Merged sqlrefactor Added brief roadmap of major features Added visualizer instructions
requirements-optional.txt Updated requirements (added expressions, grako, flask)
requirements.txt [change] slicer is now using Click library instead of argparse [new] Added InconsistencyError, handle exceptions nicely in the CLI, …

Cubes - Online Analytical Processing Framework for Python

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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


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.


  • 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 into your application.


Latest documentation


See examples directory in the source code repository for simple examples and use-cases.

See for more complex examples.


For cubes models see


Source code is in a Git repository on GitHub

git clone git://

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/_*.


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.


If you have questions, problems or suggestions, you can send a message to the Google group cubes-discuss.

IRC channel #databrewery on server

Report bugs using github issue tracking.


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.


Cubes is written and maintained by Stefan Urbanek (@Stiivi on Twitter) and various contributors. See AUTHORS file for more information.


Cubes is licensed under MIT license. For full license see the LICENSE file.

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