Disclosure, I am a committer on the Apache Drill project.
Traditionally Hive has been focused on batch processing, it started as a SQL query engine built on top of map-reduce. Unfortunately many operations in the SQL model are not well suited for map-reduce. This is a problem that is being addressed by the Hive team with their vectorization/tez work, which is a completely different execution engine from map reduce. Around the time that this work began a number of new projects were trying to solve similar problems, making queries more interactive, providing more flexibility and greater coverage of the SQL language (Hive has supported a healthy subset of the SQL language, but not full support). It is true that this new Hive engine is solving some similar problems as Drill, as is the case for Impala, Facebook's Presto engine and a few other projects.
Drill is focused on providing standard SQL support, instead of the SQL-like HiveQL language. This allows it to be used to hook up to the analytical tools and ecosystem that has been developing around the SQL language for the past few decades.
Drill also has no requirement to define schema ahead of time when querying. This is best illustrated by the JSON read support, you can write a query against a bare file, and Drill will discover the schema as it reads. This is the case for simple files stored on a local or distributed file system or even for document stores like MongoDB.
While there will certainly be cases where schema will need to be known ahead of time, many users of Hadoop and semi-structured datastores are having a hard time with the overhead of trying to fit a wide variety of datasets into a traditional DB modnel, which is what the Hive metastore provides. In cases where the number of schemas in your data is manageable, this can give consumers of the data useful meta-data and structutre. For this reason, Drill has support for reading from Hive tables for these types of use cases.
However while this model has its place, our discussions with current Hadoop users has validated the design of Drill. Analysts want to be able to gain insights from data in Hadoop, and there are cases where the overhead of managing this data like a traditional DB is too resource intensive. Drill enables these types of users to explore raw semi-structured data as it enters the Hadoop cluster, instead of waiting for IT driven ETL.