51. Spark is engineered from the bottom-up for performance, running . . . . . . . . faster than Hadoop by exploiting in memory computing and other optimizations.
52. For . . . . . . . . partitioning jobs, simply specifying a custom directory is not good enough.
53. All file access uses Java's . . . . . . . . APIs which give Lucene stronger index safety.
54. . . . . . . . . includes Apache Drill as part of the Hadoop distribution.
55. Hama was inspired by Google's . . . . . . . . large-scale graph computing framework.
56. A . . . . . . . . represents a distributed, immutable collection of elements of type T.
57. MapR . . . . . . . . Solution Earns Highest Score in Gigaom Research Data Warehouse Interoperability Report.
58. Crunch uses Java serialization to serialize the contents of all of the . . . . . . . . in a pipeline definition.
59. For Scala users, there is the . . . . . . . . API, which is built on top of the Java APIs.
60. . . . . . . . . property allows us to specify a custom dir location pattern for all the writes, and will interpolate each variable.
Read More Section(Realtime Processing with Apache Spark)
Each Section contains maximum 100 MCQs question on Realtime Processing with Apache Spark. To get more questions visit other sections.