We have seen how distributed data stores such as the HDFS and Apache Cassandra allow us to store and model huge volumes of structured, semi-structured, and unstructured data partitioned over horizontally scalable clusters providing fault tolerance, resilience, high availability, and consistency. But in order to provide actionable insights and to deliver meaningful business value, we now need to be able to process and analyze all that data.
Let's return to the traditional data processing scenario we described near the start of this chapter. Typically, the data transformation and analytical programming code written by an analyst, data engineer or software engineer (for example, in SQL, Python, R or SAS) would rely upon ...
