madIS is an extensible relational database system built on top of the SQLite database with extensions implemented in Python (via APSW SQLite wrapper).
madIS feels like Hive (Hadoop's SQL with User Defined Functions language), but without the overhead but also without the distributed processing capabilities.
However, madIS can easily handle millions of rows on a single desktop/laptop computer.
The aim of madIS is to promote the handling of data related tasks within an extended relational model.
This way, it upgrades the database, from having a support role (storing and retrieving data), to being a full data processing system on its own. madIS comes with functions for file import/export, keyword analysis, data mining tasks, fast indexing, pivoting, statistics and workflow execution.
madIS is cross-platform and it works on Mac OS X, Windows and Linux.
Detailed instructions on how to install and use the madIS utility on your Mac are available HERE.
Here are some key features of "madIS":
Complex data analysis tasks:
· With madIS it is very easy to create additional relational functions, or join against external files without first importing them into the database.
· madIS also offers a very fast multidimensional index that greatly speeds up multi-constraint joins, even when joining against external sources.
Data transformations:
· madIS can already use the file system or network sources as tables. In addition, with a little knowledge of Python, complex data transformation functions can be created and used inside the database. All these can be achieved without having to import anything inside the database.
· In addition madIS offers an easy to work with, workflow engine that automates data transformation steps.
Database research:
· You can easily develop and test a new indexing structure with madIS, as it uses Python for its extensions and already has plenty of code to start from.
Requirements:
· Python
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