This chapter focused on the integration of Spark with other big data technologies. The Parquet format is an excellent way to expose the data processed by Spark to external systems, and Impala makes this very easy. The advantage of the Parquet format is that it is very efficient in terms of storage and expressive enough to capture the schema. We also looked at the process of interfacing with HBase. Thus, we can have our cake and eat it too! This means that we can leverage Spark for distributed scalable data processing, without losing the capability to integrate with other big data technologies. The next chapter, probably my favorite, is about machine learning. We will explore ML pipelines.