Light-weight Python framework and OLAP HTTP server for easy development of reporting applications and aggregate browsing of multi-dimensionally modeled data.


business and analyst's point of view on data

  • Dimensions with multiple hierarchies
  • User oriented metadata
  • Dimension templates - define complex dimensions
  • Localization of model and data

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Aggregated Browsing

easy development of exploration tools

  • Slice and dice through dimensions
  • Drill-down through any hierarchy
  • Automatic next level selection, if desired
  • Get dimension values or all facts within a cut

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Slicer is a HTTP OLAP cube server for aggregation queries.

  • Easy drilling-down
  • Slicing and dicing
  • Serves aggregates, dimension details, facts
  • Provides all necessary metadata for a reporting application

Structured responses are in JSON format with rich metadata for easier reporting application development or reporting integration.

Example queries:

GET /aggregate?drilldown=date\
GET /facts?cut=date:2010

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Backend and SQL

One of the backends shipped with the framework is SQL. It is powered by the SQLAlchemy which supports multiple databases including PostgreSQL, MySQL, Oracle, simple sqlite and many others.


Easy to prototype on top of existing, arbitrary star or snowflake looking schemas.

Logical-to-physical mapping that supports multiple database schemas in databases such as PostgreSQL or Oracle.

Denormalization by view or materialized by table.