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.

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.

data_cubes

pip freeze requirements.txt

Working with Transactions and the DBAPI — SQLAlchemy 2.0 Documentation