The firm’s latest 7.1 product release now supports Spark with virtually all of its data integration steps in a visual drag-and-drop environment. The ‘trouble’ with using Spark is that the use of it typically demands that users build Spark-specific data integration logic. “Transitioning from one engine for big data processing to another often means users need to re-write and debug their data integration logic for each engine, which takes time. Data engine ‘portability’Because of this, the firm’s news hook is essentially one that says: adaptive execution on any engine for big data processing. But why is data engine ‘portability’ important?

Source: Forbes May 22, 2017 12:56 UTC