¿debo usar scala o java para spark?

Scala is Spark's native language. This means that any new API will always be available in Scala first. Scala is a type-safe JVM language that incorporates both object-oriented and functional programming in an extremely concise, logical and extraordinarily powerful language. The rule of thumb is that using Scala or Python developers can write the most concise code and using Java or Scala can achieve the best runtime performance.

You will master the essential skills of the open source Apache Spark framework and the Scala programming language. The popularity and use of Scala is growing rapidly, as evidenced by the increasing number of open positions for Scala developers. The Scala programming language, developed by the founder of Typesafe, provides the confidence to design, develop, code and deploy things the right way by making the best use of the capabilities provided by Spark and other big data technologies. With the advent of various big data frameworks such as Apache Kafka and Apache Spark, the Scala programming language has gained prominence among big data developers.

Some of the more complex features of the language (tuples, functions, macros, to name a few) ultimately make it easier for the developer to write better code and increase performance by programming in Scala. With support for multiple programming languages such as Java, Python, R and Scala in Spark - it often becomes difficult for developers to decide which language to choose when working on a Spark project. The Scala programming language can be found in use at some of the best tech companies such as LinkedIn, Twitter and FourSquare. Yes, it may seem more complex to the Scala novice, but once you fully understand the concepts behind it, Scala code will seem much more simplistic than Java code.

Designing Scala libraries requires a lot of skill and technical knowledge; simply developing mainstream Scala code does not. Big names in the digital economy are investing in Scala programming for big data processing - Kafka created by LinkedIn and Scalding created by Twitter. Many organisations favour the speed and simplicity of Spark, which supports many application programming interfaces (APIs) available from languages such as Java, R, Python and Scala. Scala enough to give you a preview of Scala's power and capabilities and whet your appetite for learning the language.

As a result, there are certainly code distinctions and paradigm shifts that can make early learning of Scala programming a bit more difficult, but the result is a much cleaner and well-organised language that is ultimately easier to use and increases productivity. All these statistical reports show how Scala programming is becoming the choice for Apache Spark to make data analysis faster.