Creating and Querying Data Cubes in Python using pyCube
CoRR(2023)
摘要
Data cubes are used for analyzing large data sets usually contained in data
warehouses. The most popular data cube tools use graphical user interfaces
(GUI) to do the data analysis. Traditionally this was fine since data analysts
were not expected to be technical people. However, in the subsequent decades
the data landscape changed dramatically requiring companies to employ large
teams of highly technical data scientists in order to manage and use the ever
increasing amount of data. These data scientists generally use tools like
Python, interactive notebooks, pandas, etc. while modern data cube tools are
still GUI based. This paper proposes a Python-based data cube tool called
pyCube. pyCube is able to semi-automatically create data cubes for data stored
in an RDBMS and manages the data cube metadata. pyCube's programmatic interface
enables data scientist to query data cubes by specifying the expected metadata
of the result. pyCube is experimentally evaluated on Star Schema Benchmark
(SSB). The results show that pyCube vastly outperforms different
implementations of SSB queries in pandas in both runtime and memory while being
easier to read and write.
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