The short answer to the Hive/Pig question is that SQL (which Hive uses) allows users to answer questions, and hence is good in situations where you want to answer a question (analysis, report generation). Pig allows users to build a data flow, and is good in situations where you want to transform your data (ETL, data modeling, etc.). I do cover this is more detail in the book.
NoSQL is a pretty broad term, and like all "flavor of the month" terms has been co-opted by marketing to sell about everything. If we define NoSQL as the new set of data stores focussed around massive scale, non-relational layouts, and high read and/or write volumes, so things like HBase, Cassandra, MongoDB, etc. then Pig (and MapReduce on which it is based) is a little different. Pig does scale massively, and it handles non-relational data nicely. But it is oriented around batch processing, not high volume reads and writes. But there are connectors in Pig for Hive and Cassandra, so that data stored in one can be accessed by the other. So yes, you can combine them to form a solution, with your NoSQL store serving the read/write cases and Pig/MapReduce performing large batch operations for you.