Much information is available over the internet, but some of the more sensitive or private information is available in corporate and government databases only. Fortunately, many of the languages in Jupyter provide mechanisms to access data from a database.
For this example, we will be using R for scripting. R has a database connection library, dbplyr, that can be used to access some of the more common databases. As with the file loads elsewhere in this chapter, the result of a database read would be a DataFrame. Once there, you can operate on the DataFrame like any other.
Some of the other Jupyter languages have database support through a library as well. In particular, Python and Scala have database access. In general, they work in the same manner:
- Make a connection to the database
- Run a query on the database that returns a result set
- The library converts the result set into a usable data type for the language
Note: some libraries allow you to work as much as possible on...
