When a rich club in Germany goes through a bad performance phase or loses an important match, we like to use the phrase “Geld schießt eben keine Tore”. What means more or less, that big money doesn’t ensure goals. But the overall acceptance is of course, that richer clubs are expected to win more often as they got the money to buy the best players. This inspired me to start a data journey about market values in the big 5 European leagues: What do the market values tell about the development in the different leagues? How do teams perform in relation to the money they spent? Does the market value of a team has a predictive significance?
In the first part Prepare data: football-data.co.uk (part 1) I described how the Data Vault model for the data of football-data.co.uk looks like. In the second part I will now focus on loading data into the Data Vault model. With the overall analytical architecture in mind this equates the data integration process between the stage layer and the raw data layer.
In the post Gather data: football-data.co.uk I described, how you can load CSV data into the Exasol database. As the data is now available at the Stage Layer in the database, I must now prepare the data and persist it at the Raw Data Layer, so that I can easily use it for building predictive models.
With part 1 of this post I want to explain, what Data Vault modeling is and how the Data Vault model for the data structure of football-data.co.uk looks like. With part 2 I will explain, how you load data into the developed Data Vault model.