Increased performance: By executing multiple tasks in parallel, the overall processing time is reduced, resulting in faster data integration.
Improved scalability: Parallel dataflows can be easily scaled to handle large amounts of data by adding more machines or processing cores.
Better fault tolerance: If one task in a parallel dataflow fails, the others can continue to run, reducing the impact of a failure on the overall process.
Greater flexibility: Parallel dataflows can be designed to handle different types of data and tasks, making it easier to adapt to changing requirements.