Drag & Drop Data Preprocessing: Titanic Dataset Cleaning with Tableau Prep

Tableau Prep, how powerful it is, where it fails and where its greatest advantages lie.

In my job as a BI consultant with Tableau, I’ve heard quite a lot of the phrase “Tableau is not an ETL” where I’ve had to agree most of the time. Regardless of the recent changes, Tableau has made to its Tableau Desktop product in order to improve performance with large volumes of data, in most cases, it is necessary to add an ETL process such as Talend or Pentaho prior to analysis and reporting with Tableau Desktop.

Conscious of the previous, last year (2018) Tableau released to the public the product Tableau Prep Builder with the intention of providing a Drag & Drop tool prior to data exploration with Tableau Desktop. As a consultant of this tool, I was then in the duty to explore its potential, to know its advantages and its real capacity in order to evaluate if it is viable to present it to the clients within their BI projects. To do this, I decided to replicate the cleaning process that I once did in Python to the popular Titanic dataset being careful to the point where the tool may fall short and if it is really compliant enough to apply to a larger project.