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.
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.
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.
The DBMSs use the Standard Query Language (SQL) to Create, Read, Update and Delete (CRUD) the data stored.
Objects (rows) and Attributes (columns) are stored in the above-mentioned tabular form, visually similar to an excel spreadsheet, and share the same relation (customers, suppliers, address book entries, etc.).
Some of the most notable mentions of DBMSs might include:
- Oracle Database
- IBM DB2
- Microsoft SQL Server
On a macro level, a database can be used for everyday operations by replacing spreadsheets. A data warehouse is a collection of databases…