Cubes - Light-weight OLAP Framework and Server

Cubes is a light-weight Python framework for Online Analytical Processing (OLAP), multidimensional analysis and (in the future) pre-aggregated cube computation. Main features are:

• aggregation browser of multidimensional hierarchical data
• logical model metadata (end user layer) description of how data are being analysed and reported, independent of physical data implementation
• hierarchical dimensions (attributes that have hierarchical dependencies, such as category-subcategory or country-region)
• localizable metadata and data Localization
• OLAP server
• modular back-end architecture

Intended use is aggregated browsing, slicing and dicing the data.

I would like to introduce the OLAP briefly and then the framework, explain the reason of the framework (alternative to corporate tools and over-bloated tools), explain basic principles, show how it can be used for multi-dimensional aggregations and to demonstrate how can one set-up OLAP HTTP server “in 15 minutes”.

References:

• Github
• Documentation
• Blog
• Project home
• Example of an application using Cubes Slicer Server as backend
• Project is using SQLAlchemy for relational database access and Werkzeug for the HTTP server.