Data engineers should add scala language in the learning list. There has been a lot of debate over Scala, including criticisms and positives.
While some “scala developers” criticize the complexity, performance and integration with existing tools and libraries, there are people who praise the elegant syntax, robust type system and great fit for domain specific languages available in Scala.
Most of them discuss about the experiences building production back end or web systems where developers can avail so many options already. They can avail mature, battle tested options, such as Java, PHP, node.js. If the developers are more adventurous or prefer agility over performance, they can select Python as an option.
We will discuss how scala works for data processing and machine learning and what are the reasons–
- Good Balance between performance and productivity
- Functional paradigm
- Integration with big data ecosystem
Productivity without impacting on performance
In the big data and machine learning world, while most developers learn Python/R/Matlab, Scala’s syntax is a lot less daunting as compared to C++ or Java. Newly hired developer with no prior experience should acquire knowledge about basic syntax collections lambda and API that includes 20% of the features of languages. Libraries like ScalaLab, Breeze, and BIDMach act like syntax of popular tools with operator overloading and other syntactic sugar, which are no way possible in several mainstream languages.
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Simultaneously, performance is better than conventional tools like Python or R.
Developers can easily integrate Scala with the big data eco system, which is mostly JVM based. There are frameworks on top of Java libraries like Summingbird, Flink, Scalding, and ones designed from scratch yet interface with JVM systems, like Kafka and Spark.
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Developers find APIs of Scala more flexible instead of Hadoop streaming with PySpark, Python/Perl or Python/Ruby bolts in Storm as they can have direct access to the underlying API.
HBase, Cassandra, Datomic, and Voldemort are some data storage solutions designed for JVM.
Functional Paradigm Functional paradigm is the third benefit that fits well within the Map/Reduce and big data model. Batch processing works on top of immutable data, changes with MapReduce operations and create new copies.
If you want to become a scala developer, you can just learn the standard collection and make selection among libraries. Many libraries have instant reference of category theory, more particularly properties of monoid, semigroup, and group to ensure the correctness of distributed operations.
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Scala was intended to run on JVM platform. While many critics are downgrading JVM for many reasons, it is a super powerful platform that developers can leverage and deliver programming solutions to the businesses.
Scala platform includes extensive support. Companies including Twitter, Linkedin, Foursquare and more have ported maximum codebases to scala. Apache Spark and Kafka are the open source projects that use scala for their Core.
For more updates regarding scala platform, you can write to proficient scala developers and ask queries related to scala. Share your reviews for this post by commenting.