Raw data for problems often comes from multiple sources with different and often incompatible formats. The beauty of the Spark programming model is its ability to define data operations that process the incoming data and transform it into a regular form that can be used for further feature engineering and model building. This process is commonly referred to as data munging and is where much of the battle is won with respect to data science projects. We keep this section intentionally brief because the best way to showcase the power--and necessity!--of data munging is by example. So, take heart; we have plenty of practice to go through in this book, which emphasizes this essential process.