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1. Very Good Instruction Manual
   This Book is as described and is a very well written instruction manual. It helped me much more than trying to do the free training on the Tableau website. This manual has hands on assignments that are very helpful in learning all that Tableau Prep is capable of doing. I am a novice at Tableau Prep and have found it very helpful.

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2. Take Data Prepping to the next level
   This book does get you up and running with Tableau Prep. Having used this tool several years myself, the book revealed quite a lot of tricks to get my prepping to the next level. More advanced ways of joining, scaffolding and cleaning up strings with some hidden features in Prep.The book is chopped up in a lot of small chapters, maybe a few too many, and Carl Allchin refers to his blog regularly where he could have included a bit more in the book itself. The texts are clearly written and contain screenshots when needed, so if you have found a new trick to prep your data, you can be certain that you are able to carry it out.The book is definitely worth its money as it will speed up your data cleaning process and it will enable you to clean and enrich your data in ways you couldn’t before.

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3. The book layout and content are fantastic: text, sample tables combined with images from Tableau Prep.My personal highlights:- (chapter 4): I loved the whole chapter about shaping data.- (Kindle page 1534): Two reasons why a flat data structure isn’t great. That would be a nasty interview question.- (Kindle page 1594): When to use a rows-to-columns pivot. That’s a deceptively tricky case.- (Kindle page 1618): One of the major challenges with Tableau Prep is aggregating and then adding back into Desktop. While this is hard in a programming language as well, I personally find it harder in Tableau, as I don’t see each step in one place (code).- (Kindle page 2487): One of the best explanations on join duplicates (a.k.a. join risk) and how to avoid them.- (Kindle page 3022): When is a null OK? How to deal with missing data or nulls? A topic that can get quickly really complicated (statisticians spent a huge amount of time on imputation techniques).- (Kindle page 3320): How to handle duplicates.- (Kindle page 3473): Completing advanced joins with multiple join conditions. The author covers “and” and “or” conditions with multiple join conditions. Nice.- (Kindle page 3385): How to write LOD calculation in Prep Builder. What??? I missed this. LOD’s in Prep were released in Feb/2020.FrancoPS: Tableau + R/Python:If you’ve learned data wrangling in a programming language (e.g. R or Python), you will be happy to hear, that Tableau Prep data structure works the same way.For example, in R tidyverse, there is a famous concept of “tidy data” by Hadley Wickham. “tidy data” simply refers to:- Each variable must have its own column.- Each observation must have its own row.- Each value must have its own cell.The same “rules” apply to Tableau Desktop, Tableau Prep, Python, and of course to R. This concept makes it relatively easy to go from R/Python to Tableau Prep.

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