(RDBMS) by design are inefficient and rigid to store and handle big data. Over the years researchers have developed big data processing frameworks and storage systems to handle these challenges. Like any new system, it is crucial to model and understand its performs under various conditions. This could be a tedious and challenging task because big data application under the hood runs on frameworks comprising of complex and complicated pipeline of phases and operations. For example, applications deployed using MapReduce [@dean2008mapreduce] and Apache Spark [@zaharia2012resilient] frameworks go through several computation or communication stages. Phases and stages on a basic level are the transitions that the data goes through while being processed. More details on MapReduce phases are discussed in Section \[background\]. Considering this challenge, we argue that a phase by phase modelling approach can gain a better understanding of the performance drivers of such frameworks. Some of the previous works in this area focuses only on high-level performance drivers like data size [@glushkova2017mapreduce; @herodotou2011hadoop]. This approach may work well for some applications. For example, from sort operations shown in Figure \[joblevel\], we can see a clear linear relationship between the data size and the time taken to complete the execution. 