Spark architecture is based on a DAG engine and its data model works on Resilient Distributed Dataset (RDD), which is its USP with a large number of benefits in terms of performance. In Spark the computations are performed lazily, which allows the DAG engine to identify the step or computation that is not needed for the end result and is not performed at all, thus improving performance.
Spark has an advanced DAG engine that manages the data flow. A job in Spark is transformed in a DAG with task stages and the graph is then optimized. The tasks identified are then analyzed to check if they can be processed in one stage or multiple stages. Task locality is also analyzed to optimize the process.