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Web Application Development with R Using Shiny - Third Edition by Shitalkumar R. Sukhdeve, Chris Beeley

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Persistent data storage

In data science product development, one of the most important steps is to bring data from various sources and keep it on storage systems. Mostly, data-storage management for data science projects is done with a data warehouse. Nowadays, various technologies have been developed to store and process various types of data, which can be structured, semi-structured, or unstructured. Using Hadoop, HDFS, Hive, MongoDB, SQLite, or MySQL-like tools and technologies are coupled up to develop an ecosystem to make for easy availability and fast processing of data.

In normal software, the data sources are usually RDBMS and meant to deal with online transactional requirements. But in data science projects, the scenarios are quite ...

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