A data-analytics team is building a new pipeline that will run on a 100-node Apache Spark cluster. The team wants to (1) write code in the same language that Spark itself is implemented in, (2) gain immediate access to Spark's native DataFrame and Dataset APIs when a new Spark version is released, and (3) avoid the extra Py4J (or similar) serialization layer that adds cross-language overhead. According to the CompTIA Data+ list of common programming languages, which language should the team choose?
Scala is the language in which Apache Spark is primarily written, so developing Spark jobs in Scala avoids the Python-to-JVM bridge that Py4J introduces, removes an entire layer of serialization overhead, and lets developers use the DataFrame and strongly typed Dataset APIs as soon as they are available. Python, R, and Java can all be used with Spark, but they either rely on a bridging layer (Py4J for Python, rJava for R) or lag slightly behind Scala in API updates. Java is close, yet Spark's higher-level APIs and examples are maintained first in Scala, making Scala the best fit for the stated requirements.
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CompTIA Data+ DA0-002 (V2)
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