Don't be surprised if this question pops up as one of the top DataStage technical interview questions in your next interview. There are two basic types of parallelism. Pipelining and partitioning. The DataStage tool provides these two methods. For example, for a simple parallel job that extracts data from one data source, transforms it in some way, and then writes it to another data source. In any case, this job looks the same on the designer's canvas but can be configured to work in different ways depending upon the parallelism selected.  Pipeline parallelism  If you run a sample of his job on a system with 3 or more processors, the phase read he will start on one processor and start filling the pipeline with read data.  In parallel parallelism, the Datastage tool works like a continuous process where the process of transformation, cleaning and load process keeps working in parallel. It is not required that one process of upstream will stop while the downstream process is working and vice-versa.  This process helps in minimizing the risk usage, which is for the staging area. Additionally, it also reduces the idle time held on the processors working.  Partition Parallelism  Suppose you have the same simple pipelined parallelism job, processing a very large amount of data.  In partition parallelism, the input data stream is divided into various subsets referred to as partitions which are further processed using the individual processors by the same operation process.