While one can easily create their own data structures for graph problems, Scala's support for graphs comes from both semantic layer—Graph for Scala is effectively a convenient, interactive, and expressive language for working with graphs—and scalability via Spark and distributed computing. I hope that some of the material exposed in this chapter will be useful for implementing algorithms on top of Scala, Spark, and GraphX. It is worth mentioning that bot libraries are still under active development.

In the next chapter, we'll step down from from our flight in the the skies and look at Scala integration with traditional data analysis frameworks such as statistical language R and Python, which are often used for data munching. Later, in Chapter 9, NLP in Scala. I'll look at NLP Scala tools, which leverage complex data structures extensively.