Q. A key of type ___________ is generated which is used later to join ngrams with their heads and tails in the reducer phase.

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Q. ________ phase merges the counts for unique ngrams or ngram fragments across multiple documents.

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Q. Drill is designed from the ground up to support high-performance analysis on the ____________ data.

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Q. ___________ includes Apache Drill as part of the Hadoop distribution.

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Q. MapR __________ Solution Earns Highest Score in Gigaom Research Data Warehouse Interoperability Report.

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Q. Drill integrates with BI tools using a standard __________ connector.

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Q. Drill analyze semi-structured/nested data coming from _________ applications.

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Q. Apache _________ provides direct queries on self-describing and semi-structured data in files.

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Q. Drill provides a __________ like internal data model to represent and process data.

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Q. Drill also provides intuitive extensions to SQL to work with _______ data types.

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Q. The Apache Crunch Java library provides a framework for writing, testing, and running ___________ pipelines.

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Q. For Scala users, there is the __________ API, which is built on top of the Java APIs.

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Q. The Crunch APIs are modeled after _________ which is the library that Google uses for building data pipelines on top of their own implementation of MapReduce.

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Q. Crunch was designed for developers who understand __________ and want to use MapReduce effectively.

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Q. Hive, Pig, and Cascading all use a _________ data model.

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Q. A __________ represents a distributed, immutable collection of elements of type T.

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Q. ___________ executes the pipeline as a series of MapReduce jobs.

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Q. __________ represent the logical computations of your Crunch pipelines.

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Q. PCollection, PTable, and PGroupedTable all support a __________ operation.

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Q. Crunch uses Java serialization to serialize the contents of all of the ______ in a pipeline definition.

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