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https://databrewery.org

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Data Brewery Blog

2014-09-02 by Stefan Urbanek. Cubes 1.0 Released - Pluggable Data Warehouse. Finally it is here: Cubes 1.0. Many of you are already using it from Github. Or from PyPi, it just has not been officially released, so here we go. Cubes now brings a light-weight way to create concept-oriented pluggable data warehouse from multipe sources. Analytical Workspace and Model Providers. New SQL Backend Features. Detailed list of changes. Multiple models per workspace/server instead of only one. Measure represents a n...

bubbles.databrewery.org bubbles.databrewery.org

Bubbles | Data Brewery

Bubbles is a Python. Framework for data processing and data quality measurement. Basic concept are abstract data objects, operations and dynamic operation dispatch. Understandability of the process. Auditability of the data being processed (frequent use of metadata). Bubbles is performance agnostic at the low level of physical data implementation. Performance is assured by the data technology and appropriate use of operations. When you might consider using Bubbles? Learn more about unknown datasets.

cubes.databrewery.org cubes.databrewery.org

Cubes | Data Brewery

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. Dimension templates - define complex dimensions. Localization of model and data. Easy development of exploration tools. Slice and dice through dimensions. Drill-down through any hierarchy. Automatic next level selection, if desired. Slicer is a HTTP OLAP cube.

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Open Source

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Follow me on Github:. Is a collection of open-source data warehouse libraries and tools. Lightweight distributed online analytical processing ( OLAP. Framework and server. Used for analytical modelling and aggregated browsing. Serves as a foundation for reporting and analytical applications. Experimental data integration framework based on metadata composition of abstract data objects. Keeps the data in their natural. Various contributions to the GNUstep. San Francisco, CA, USA.

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Data Brewery Blog

http://blog.databrewery.org/tag/release.html

2014-09-02 by Stefan Urbanek. Cubes 1.0 Released - Pluggable Data Warehouse. Finally it is here: Cubes 1.0. Many of you are already using it from Github. Or from PyPi, it just has not been officially released, so here we go. Cubes now brings a light-weight way to create concept-oriented pluggable data warehouse from multipe sources. Analytical Workspace and Model Providers. New SQL Backend Features. Detailed list of changes. Multiple models per workspace/server instead of only one. Measure represents a n...

blog.databrewery.org blog.databrewery.org

Introducing Expressions for Python

http://blog.databrewery.org/posts/python-expressions-0-1-released.html

2013-08-02 by Stefan Urbanek. Introducing Expressions for Python. Expressions is a lightweight arithmetic expression parser for creating simple arithmetic expression compilers. Goal is to provide minimal and understandable interface for handling arithmetic expressions of the same grammar but slightly different dialects (see below). The framework will stay lightweight and it is unlikely that it will provide any more complex gramatical constructs. Numbers and strings (literals). Binary and unary operators.

blog.databrewery.org blog.databrewery.org

Data Brewery Blog

http://blog.databrewery.org/tag/tutorial.html

2011-11-28 by Stefan Urbanek. How-to: hierarchies, levels and drilling-down. In this Cubes OLAP how-to we are going to learn:. How to create a hierarchical dimension. How to do drill-down through a hierarchy. In the previous tutorial. We learned how to use model descriptions in a JSON file and how to do physical to logical mappings. Data used are similar as in the second tutorial, manually modified IBRD Balance Sheet. Taken from The World Bank. In our example we have the. You can see a slight difference ...

blog.databrewery.org blog.databrewery.org

Data Brewery Blog

http://blog.databrewery.org/index2.html

2013-04-08 by Stefan Urbanek. Site Redesign, Leaving Tumblr. Data Brewery home page. Was redesigned. I would like to thank Andrej Sykora. Who did a great job with the new look and migration of the old blog posts. The main reason for redesign was providing more content for each project. Another one was to have it designed in a way that future projects can be easily added by having one subdomain for each project. To static pages. The base URL will stay the same: blog.databrewery.org. Here is a link. Reinie...

blog.databrewery.org blog.databrewery.org

Data Brewery Blog

http://blog.databrewery.org/tag/rfc.html

2012-07-27 by Stefan Urbanek. Using Pandas as Brewery Backend. Added info about caching. First time I looked at Pandas. Python data analysis framework) I thought: that would be great backend/computation engine for data Brewery. To recap core principle of Brewery: it is flow based data streaming framework with processing nodes connected by pipes. A typical node can have one or multiple inputs and has output. Source nodes have no inputs, target nodes have no outputs. Follow data modeller's workflow. Repres...

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