What language was designed to work natively with Spark's APIs?

The correct answer is Scala because it was specifically designed to work seamlessly with Apache Spark's APIs. Scala runs on the Java Virtual Machine (JVM) and offers strong support for functional programming paradigms, which aligns closely with the design philosophy of Spark. The native integration allows developers to leverage Spark’s rich set of features while taking advantage of Scala's concise syntax and powerful type system.

While Java, Python, and R also support Spark, they were not tailored for it in the same way Scala was. Java is used extensively in Spark, but it is more verbose and less expressive compared to Scala. Python provides an easy-to-use interface through PySpark, but it doesn’t have the same level of native support, resulting in performance limitations for certain operations. R can be used with Spark through SparkR, but it is more geared towards statistical analysis and data science rather than general-purpose programming. Therefore, Scala remains the language that is most closely integrated and optimized for working with Apache Spark's APIs.