It is reported that Apache Spark and Scala is an open source computing framework up to 100 times faster than Mapreduce .
RG members are requested to give their opinion in this respect.
Apache Spark™ is a fast and general engine for large-scale data processing : Computing EcoSystem.
Scala is a general-purpose programming language providing support for functional programming and a strong static type system. Multi-paradigm: functional, object-oriented, imperative, concurrent..
So you can use scala to program in Spark Ecosystem.
Apache Spark™ is a fast and general engine for large-scale data processing : Computing EcoSystem.
Scala is a general-purpose programming language providing support for functional programming and a strong static type system. Multi-paradigm: functional, object-oriented, imperative, concurrent..
So you can use scala to program in Spark Ecosystem.
Apache Spark is a distributed computation framework that simplifies and speeds-up the data crunching and analytics workflow for data scientists and engineers working over large datasets. It offers an unified interface for prototyping as well as building production quality application which makes it particularly suitable for an agile approach. I personally believe that Spark will inevitably become the de-facto Big Data framework for Machine Learning and Data Science.
Why only Scala and Python? Apache Spark comes with 4 APIs: Scala, Java, Python and recently R. The reason why I am only considering “PyScala” is because they mostly provides similar features respectively to the other 2 languages (Scala over Java and Python over R) with, in my opinion, better overall scoring. Moreover R is not a general-purpose language and its API is still in an experimental phase
park Programming is nothing but a general-purpose & lightning fast cluster computing platform. In other words, it is an open source, wide range data processing engine. That reveals development API’s, which also qualifies data workers to accomplish streaming, machine learning or SQL workloads which demand repeated access to data sets.
Apache Spark currently supports multiple programming languages, including Scala and Python.
Still Scala is chosen over Spark for the following reasons:
1. Python is in general slower than Scala. If you have significent processing logic written in your own codes, Scala definitely will offer better performance. 2. Scala is static typed. It looks like dynamic-typed language because it uses a sophisticated type inference mechanism. It means that I still have the compiler to catch the compile-time errors for me. Call me old school. 3. Apache Spark is built on Scala, thus being proficient in Scala helps you digging into the source code when something does not work as you expect. It is especially true for a young fast-moving open source project like Spark. 4. When Python wrapper calls the underlying Spark codes written in Scala running on a JVM, translation between two different environments and languages might be the source of more bugs and issues. 5. Last but not least, because Spark is implemented in Scala, using Scala allows you to access the latest greatest features. Most features are first availabe on Scala and then port to Python.
Read more here https://data-flair.training/blogs/spark-tutorial/
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