Data-Driven Decision Making in SQL

In this course, you will learn how to use SQL to support decision making. It is based on a case study about an online movie rental company with a database about customer information, movie ratings, background information on actors and more. You will learn to apply SQL queries to study for example customer preferences, customer engagement, and sales development. This course also covers SQL extensions for online analytical processing (OLAP), which makes it easier to obtain key insights from multidimensional aggregated data. 

3. Data driven decision making with advanced SQL queries

• Queries with EXISTS
  • Ex. Movies with at least one rating
  • Remark. There is a NOT EXISTS clause that works exactly as the opposite of EXISTS.

4. Data driven decision making with OLAP SQL queries

Online Analytical Processing (OLAP), interactively annalyze the data and to summarize it an visualize it

• CUBE operator
  It is always used before the GROUP BY operator and is used to produce a PIVOT TABLE.
  
  • Ex. Number of observations (rentings)
  • Ex. Number of ratings per observatio
• ROLLUP operator
  It is similar to CUBE, the only difference is that here we don't give aggregation for the second variable alone.
  • Ex. 
  • Remark. We can put as many aggregated values as we want; in the last example COUNT(*) and COUNT(rating)
• GROUPING SETS operator
  It is a generalization of the rpevious operators; here we decide at which levels we want to aggregate