Cubes 0.6 released

New version of Cubes - Python OLAP framework and server - was released.

Cubes is a framework for:

• Online Analytical Processing - OLAP, mostly relational DB based - ROLAP
• multidimensional analysis

• star and snowflake schema denormalisation

• Source: https://bitbucket.org/Stiivi/cubes

• Documentation: http://packages.python.org/cubes
• Python Package page: http://pypi.python.org/pypi/cubes

Notable changes:

• added ‘details’ to cube metadata - attributes that might contain fact details which are not relevant to aggregation, but might be interesting when displaying facts (such as contract name or notes)
• added ordering of facts in aggregation browser

SQL

• SQL denormalizer can now, by request, automatically add indexes to level key columns
• one detail table can be used more than once in SQL denomralizer (such as an organisation for both - supplier and requestor), added key alias to joins in model description, see joins documentation for more information.

Slicer server

• added log a and log_level configuration options (under [server])
• added format= parameter to /facts, accepts json and csv
• added fields= parameter to /facts - comma separated list of returned fields in CSV (see API)
• limit number of facts returned in JSON (configurable by json_record_limit in [server] section), CSV can return whole dataset and will do it iteratively (we do not want to consume all of our memory, do we?)

Also many bugs were fixed, including localization in fact(s) retrieval and pagination. Sharing of single SQLAlchemy engine and model within server thread was added for performance reasons.

Enjoy.

Introduction

Freshly brewed clean data with analytical taste – that is what Data Brewery is for. The Python framework will allow you to:

• stream structured data from various sources (CSV, XLS, SQL database, Google spreadsheet) to various structured targets
• create analytical streams using flow-based programming: connect processing nodes together and let the structured data flow through them
• measure data properties, such as data quality or numerical statistics
• do advanced data mining in the future such as clustering or classification

You can use Brewery for analytical automation or just for ad-hoc analytical processing.

Project page is at databrewery.org. Source repository can be found at:

• github
• bitbucket

Documentation with examples and node reference can be found here.

Happy brewing!