Question

Consider the following statements in the context of Spark:
Statement 1: Spark improves efficiency through in-memory computing primitives and general computation graphs.
Statement 2: Spark improves usability through high-level APIs in Java, Scala, Python and also provides an interactive shell.
Only statement 1 is true
Only statement 2 is true
Both statements are true
Both statements are false

Answer 1

Answer:

Answer is both statements are true

Answer 2

Option C) Both statements are true

• Another parallel processing framework is Spark. Hadoop, for instance, was a very popular parallel processing framework but it had many flaws, particularly in the area of machine learning. As a result, many of these Hadoop flaws were addressed and an entirely new parallel processing framework was created. This framework is known as Spark.
• Because it uses a static type language, Scala is quicker than Python. Scala is a wise choice if better performance is needed. Writing Spark jobs in Scala is therefore natural because Spark is native to that language.
• Speed. By utilising in memory computing and other improvements, Spark, which was designed from the ground up for performance, can handle enormous amounts of data 100 times quicker than Hadoop. The world record for large-scale on-disk sorting is presently held by Spark, which is also quick when data is kept on disc.
• In order to readily access and use Spark's most recent capabilities, developers claim that adopting Scala makes it easier for them to go deeply into Spark's source code. The primary draw of Scala is its compatibility with Java, as this allows Java developers to quickly learn the language by immediately grasping object-oriented principles.

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