Q. Spark is engineered from the bottom-up for performance, running ___________ faster than Hadoop by exploiting in memory computing and other optimizations.

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Q. Spark powers a stack of high-level tools including Spark SQL, MLlib for _________

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Q. Apache Flume 1.3.0 is the fourth release under the auspices of Apache of the so-called ________ codeline.

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Q. ___________ was created to allow you to flow data from a source into your Hadoop environment.

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Q. A ____________ is an operation on the stream that can transform the stream.

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Q. A number of ____________ source adapters give you the granular control to grab a specific file.

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Q. ____________ is used when you want the sink to be the input source for another operation.

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Q. ___________ is where you would land a flow (or possibly multiple flows joined together) into an HDFS-formatted file system.

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Q. ____________ sink can be a text file, the console display, a simple HDFS path, or a null bucket where the data is simply deleted.

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Q. Flume deploys as one or more agents, each contained within its own instance of _________

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Q. ___________ provides Java-based indexing and search technology.

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Q. ___________ is a high performance search server built using Lucene Core.

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Q. ____________ is a subproject with the aim of collecting and distributing free materials.

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Q. _______ is a Python port of the Core project.

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Q. The Lucene _________ is pleased to announce the availability of Apache Lucene 5.0.0 and Apache Solr 5.0.0.

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Q. ___________ is a technology suitable for nearly any application that requires full-text search, especially cross-platform.

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Q. Lucene provides scalable, high-Performance indexing over ______ per hour on modern hardware.

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Q. Lucene index size is roughly _______ the size of text indexed.

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Q. All file access uses Java’s __________ APIs which give Lucene stronger index safety.

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Q. During merging, __________ now always checks the incoming segments for corruption before merging.

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