Relational databases have tables that can be written and accessed in many different ways without having the need to reorganize the database structure. They usually use Structured Query Language (SQL). The most widely used databases are Microsoft SQL Server, PostgreSQL, MySQL, and Oracle.

Python has many libraries capable of using any of these databases. We will use PostGresSQL as an example. The PostGresSQL library, Psycopg2, is used by Python to handle any SQL queries:

1. We will use the GOOG data prices to create the database for GOOG data:

goog_data.head(10)
                  High         Low        Open       Close     Volume   Adj Close
Date                                                                       2014-01-02  555.263550  550.549194  554.125916  552.963501  3666400.0  552.963501
2014-01-03  554.856201  548.894958  553.897461  548.929749  3355000.0  548.929749
2014-01-06  555.814941  549.645081  552.908875  555.049927  3561600.0  555.049927
2014-01-07  566.162659  556.957520  558.865112  565.750366  5138400.0  565.750366
2014-01-08  569.953003  562.983337  569.297241  566.927673  4514100.0  566.927673
2014-01-09  568.413025  559.143311  568.025513  561.468201  4196000.0  561.468201
2014-01-10  565.859619  557.499023  565.859619  561.438354  4314700.0  561.438354
2014-01-13  569.749329  554.975403  559.595398  557.861633  4869100.0  557.861633
2014-01-14  571.781128  560.400146  565.298279  570.986267  4997400.0  570.986267
2014-01-15  573.768188  568.199402  572.769714  570.598816  3925700.0  570.598816

2. To create a table in SQL, we will use the following command. You will need to install PostGresSQL on your machine. Then, you will need to insert the following content:

CREATE TABLE "GOOG"
 (
   dt timestamp without time zone NOT NULL,
   high numeric NOT NULL,
   low numeric NOT NULL,
   open numeric NOT NULL,
   close numeric NOT NULL,
   volume numeric NOT NULL,
   adj_close numeric NOT NULL
   CONSTRAINT "GOOG_pkey" PRIMARY KEY (dt)
 );

This command will create a SQL table named GOOG. The primary key of this table will be the timestamp, dt.

3. As an example, we will run the following query to get the GOOG data from 2016-11-08 to 2016-11-09:

SQL = '''SELECT  
     dt,high,low,open,close,volume, adj_close
 FROM "GOOG"  
 WHERE dt BETWEEN '2016-11-08' AND '2016-11-09'  
 ORDER BY dt  
 LIMIT 100;'''

The Python code will be the following:

import psycopg2
conn = psycopg2.connect(database='name_of_your_database')  # set the appropriate credentials
cursor = conn.cursor()
def query_ticks():
     cursor.execute(SQL)
     data = cursor.fetchall()
     return data

The query_ticks function will return the GOOG data.

The main issue with a relational database is speed. They are not made to work with large amounts of data indexed by time. To speed up, we will need to use non-relational databases.