This paper discusses how Actian DataFlow solves the challenges of accelerating data preparation and transformation using its scale-up and scale-out architecture.

Hadoop holds tremendous promise for large scale data management and data analytics projects that could be of huge benefit to many enterprises. However, Hadoop has limitations and difficulties of use that cause many projects to fail. These include the need for rare and expensive skillsets, inadequate execution speed, long implementation cycles, and extreme difficulty of incorporating other datasets. Actian DataFlow solves these challenges with a scale-up, scale-out architecture, automatic workload optimization, pipeline parallelism, and a wide range of pre-built operators in a easy-to-use, visual interface. Actian DataFlow provides unmatched price/performance and due to platform agnosticism, fits into most enterprise architectures with ease.