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./devel/py-cubes, Lightweight framework for Online Analytical Processing

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Branch: CURRENT, Version: 1.1, Package name: py27-cubes-1.1, Maintainer: pkgsrc-users

Cubes is a light-weight Python framework and set of tools for Online Analytical
Processing (OLAP), multidimensional analysis and browsing of aggregated data.

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
into your application.

Required to run:
[time/py-dateutil] [databases/py-sqlalchemy] [www/py-werkzeug]

Master sites:

https://files.pythonhosted.org/packages/source/c/cubes/ (Download)

SHA1: d28d3a88f59542c1a0d3e0e37b96c37cdb5f2fc2
RMD160: a01cf288ba56341abae7aba1e61810a686e6ec0b
Filesize: 125.725 KB

Version history: (Expand)

(2021-03-19) Package deleted from pkgsrc
(2017-11-23) Package has been reborn
(2017-06-01) Package added to pkgsrc.se, version py27-cubes-1.1 (created)

CVS history: (Expand)

2021-03-18 10:16:54 by Adam Ciarcinski | Files touched by this commit (9) | Package removed

Log message:
py-clint, py-cubes: removed

2017-06-01 14:05:28 by Adam Ciarcinski | Files touched by this commit (5)

Log message:
Cubes is a light-weight Python framework and set of tools for Online Analytical
Processing (OLAP), multidimensional analysis and browsing of aggregated data.

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
  into your application.