Introduction to SQL Databases for Financial Data Analysis — Theory.

Data science in general and financial data science require explicitly a place to store data in order to be retrieved and analyzed. The first place one could think of storing financial data might be a single “.txt” or even a “.csv” file. Unlike those solutions, databases offer much more functionality and can be easily used for storing, managing, and protecting valuable data.

This article would be an introduction to the world of data storage and databases.

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In order to extend on the previous statement about databases, they can offer the following, against storing data in files:

• Evaluating and storing different data sets.
• Can support large amounts of data.
• Can grate different access rights to its different users.
• Securing data in general.

Overview

Edgar Frank Codd, in his paper “A Relational Model of Data for Large Shared Data Banks”, initiate the development of the relational database model. As it is known today, Standard Database Management Systems (DBMSs) define themselves through Codd’s 12 rules in order to store data in a tabular form inside a database, meaning rows and columns, and use rational operators to manipulate its contents.