  Epidemiologist, Scientists, Statisticians, Historians, Data engineers and
Data scientists are working on finding descriptive models and theories to
explain COVID-19 expansion phenomena or on building analytics predictive models
for learning the apex of COVID-19 confimed cases, recovered cases, and deaths
evolution curves. In CRISP-DM life cycle, 75% of time is consumed only by data
preparation phase causing lot of pressions and stress on scientists and data
scientists building machine learning models. This paper aims to help reducing
data preparation efforts by presenting detailed schemas design and data
preparation technical scripts for formatting and storing Johns Hopkins
University COVID-19 daily data in HBase NoSQL data store, and enabling HiveQL
COVID-19 data querying in a relational Hive SQL-like style.
