"Big data is one of those areas where programming languages are not just about syntax - they directly influence scalability, performance, and the developer experience. While Java has been the workhorse of enterprise systems for decades, Scala has emerged as the language of choice for big data platforms like Apache Spark, Kafka, and Akka. But why Scala, when Java already powers most of the JVM ecosystem? Let's break it down. Functional + Object-Oriented in One Scala blends functional programming with Java's object-oriented paradigm."
"From the above example it seems I am trying to show java is bad by design or spreading a propaganda against it - no, see before going into the article let me specify I am new to scala like just started so i am comfortable in java more than scala. So now comes the perspective to how look things up generally, and we are talking about big data."
Scala combines functional programming and object-oriented paradigms, enabling concise, expressive code and first-class functions that can be passed as values. Scala's syntax and abstractions reduce boilerplate compared with equivalent Java implementations, improving developer productivity. Major big data platforms such as Apache Spark, Kafka, and Akka favor Scala for performance, scalability, and composability. Java remains dominant across the JVM ecosystem and provides familiarity and stability. Big data often consists of non-atomic records composed from multiple atomic datasets, requiring languages and platforms that support complex data transformations at scale. Choosing Scala can simplify transformations and parallelism in big data pipelines.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]