DBgen: A Python Library for Defining Scalable, Maintainable, Accessible, Reconfigurable, Transparent (SMART) Data Pipelines

30 July 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In this work, we present DBgen, a Python library that provides a framework for defining extract-transform-load (ETL) pipelines to create and populate SQL databases. DBgen is most useful when the underlying data has complex relationships, requires multi-step analysis, is large-scale, and the type of data being collected changes frequently. Scientific data often fits this description. With current tooling, defining ETL pipelines for this particularly difficult- to-manage data is so onerous that a great deal of it does not end up being stored in a database and is opaque. DBgen is designed to fill the gap in the current tooling and reduce the barrier to defining ETL pipelines such data.

Keywords

Database
ETL
Python
Data Management

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