Designing an analytics database for scientific data using SQLAlchemy

Abstract
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The data warehouse database, along with a data lake, makes up a key component of modern analytics infrastructure. A common design pattern for such analytics databases is the star schema that models the business measurement process as measurements (facts) and the context for those measurements (dimensions). The same schema can also be used to model scientific measurements. In particular, the sqlalchemy ORM provides a useful framework for creating many of these schema, particularly for a smaller biotech manufacturing company with lower data volumes.

Biography
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Aaron Wiegel is a data engineer at Synthego, a biotech manufacturing startup. He obtained his PhD in physical chemistry from UC Berkeley where he first learned Python to create simulations of collisions between atoms and molecules using numpy and scipy. He now uses Python to create ETL pipelines and analytics databases for an automated chemistry and biology laboratory. In addition to his professional work, Aaron also volunteers teaching community college math, statistics, and science courses to California state prison inmates. For fun, he brews his own beer at home, where he performs much tastier experiments than the lab.