-* The PACT programming model encourages a more modular programming style. Although the number of user functions is usually higher, they are more fine-grain and focus on specific problems. Hence, interweaving of functionality which is common for MapReduce jobs can be avoided.
-* Data analysis tasks can be expressed as straight-forward data flows, especially when multiple inputs are required.
-* PACT has a record-based data model, which reduces the need to specify custom data types as not all data items need to be packed into a single value type.
-* PACT frequently eradicates the need for auxiliary structures, such as the distributed cache, which "break" the parallel programming model.
-* Data organization operations such as building a Cartesian product or combining records with equal keys are done by the runtime system. In MapReduce such often needed functionality must be provided by the developer of the user code.
-* PACTs specify data parallelization in a declarative way which leaves several degrees of freedom to the system. These degrees of freedom are an important prerequisite for automatic optimization. The [[PactCompiler|PACT compiler]] enumerate different execution strategies and chooses the strategy with the least estimated amount of data to ship. In contrast, Hadoop executes MapReduce jobs always with the same strategy.
For a more detailed comparison of the MapReduce and PACT programming models you can read our paper //"MapReduce and PACT - Comparing Data Parallel Programming Models"// (see our [[http://www.stratosphere.eu/publications|publications page]]).
