Many popular big data technologies (such as Apache Spark, BEAM, and Flink) are built in the JVM, and many interesting AI tools are built in other languages, and some requiring copying to the GPU. As many folks have experienced, while we may wish that we spend all of our time playing with cool algorithms -- we often need to spend more of our time working on data prep. Having to copy our data slowly between the JVM and the target language of computation can remove much of the benefit of being able to access our specialized tooling. Thankfully, as illustrated in the soon to be released Spark 2.3, Apache Arrow and related tools offer the ability to reduce this overhead. This talk will explore how Arrow is being integrated into Spark, and how it can be integrated into other systems, but also limitations and places where Apache Arrow will not magically save us.