The most difficult thing for big data developers today is choosing a programming language for big data applications. Python and R programming, are the languages of choice among data scientists for building machine learning models whilst Java remains the go-to programming language for developing Hadoop applications.
With the rise of various big data frameworks like Apache Kafka and Apache Spark-Scala programming language has picked up prominence among big data developers.
With support for multiple programming languages like 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. A common question that industry experts are asked is – What language should I choose for my next Apache Spark project? The answer to this question varies, as it depends on the programming expertise of the developers but preferably Scala programming language has become the language of choice for working with big data frameworks like Apache Spark and Kafka.
What is Scala?
Scala is an acronym for “Scalable Language”. It is a general-purpose programming language designed for the programmers who want to write programs in a concise, elegant, and type-safe way. Scala enables programmers to be more productive. Scala is developed as an object-oriented and functional programming language. From the functional programming perspective- each function in Scala is a value and from the object-oriented aspect – each value in Scala is an object.
Scala is a JVM based statistically typed language that is safe and expressive. With its extensions that can be easily integrated into the language-Scala is considered as the language of choice to achieve extensibility.
Scala programming language can be found being used at the absolute best tech organizations like LinkedIn, Twitter, and FourSquare. Scala’s performance has lighted enthusiasm among a few financial institutions to utilize it for derivative pricing in EDF Trading. The greatest names in the digital economy that are investing into Scala programming for big data processing are – Kafka made by LinkedIn and Scalding made by Twitter. With amazing monoids, combinators, pattern matching features, provision to create DSLs, and more Scala as a tool for big data processing on Apache Spark is definitely a certainty.
Why should you learn Scala for Apache Spark?
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 capabilities provided by Spark and other big data technologies.
There is constantly a best programming tool for each errand. With regards to preparing big data and AI – Scala programming has dominating the big data world and here’s the reason:
- Apache Spark is written in Scala and in light of its scalability on JVM. Developers express that utilizing Scala helps dive profoundly into Spark’s source code with the goal that they can undoubtedly access and execute the newest features of Spark. Scala’s interoperability with Java is its biggest fascination as java developers can undoubtedly jump on the learning path by grasping the object-oriented concepts quickly.
- Scala programming holds a perfect balance between productivity and performance. The vast majority of the big data developers are from Python or R programming foundations. Syntax for Scala writing computer programs is less intimidating when compared with Java or C++. For a new Spark developer with no prior experience, it is sufficient for him/her to know the fundamental syntax collections and lambda to get gainful in big data processing utilizing Apache Spark. Additionally, the performance accomplished utilizing Scala is better than other traditional data analysis tools like R or Python. Over the time, as the abilities of a developer develop, it becomes easy to transit from imperative to more elegant functional programming code to improve performance.
- Organizations need to appreciate the expressive power of dynamic programming language without losing type safety – Scala programming has this potential and this can be decided from its increasing adoption rates in the enterprise.
- Scala is planned considering parallelism and concurrency in mind for big data applications. Scala has amazing built-in concurrency support and libraries like Akka which make it simple for built-in concurrency support
- Scala works together well within the MapReduce big data model as a result of its functional paradigm. Numerous Scala data frameworks follow comparable abstract data types that are predictable with Scala’s collection APIs. Developers simply need to become familiar with the standard collections and it is anything but difficult to work with different libraries.
- Scala programming language provides the best way to build scalable big data applications in terms of data size and program complexity. With support for immutable data structures, for-comprehensions, immutable named values- Scala provides remarkable support for functional programming.
- Scala is comparatively less complex, unlike Java. A single complex line of code in Scala can replace 20 to 25 lines of complex java code making it a preferable choice for big data processing on Apache Spark.
- Scala has well-designed planned libraries for scientific computing, linear algebra, and random number generation. The standard scientific library Breeze contains non-uniform random generation, numerical algebra, and other special functions. The saddle is the data library upheld by Scala programming which gives a strong foundation to data manipulation through 2D data structures, robustness to missing values, array-backed support, and automatic data alignment.
- Efficiency and speed assume an essential job regardless of increasing processor speeds. Scala is quick and proficient, settling on it as a perfect choice of language for computationally intensive algorithms. Compute cycle and memory efficiency are also well-tuned when using Scala for Spark programming.
- Other programming languages like Python or Java have slacked in the API coverage. Scala has crossed over this API coverage gap and is gaining traction from the Spark community. The thumb rule here is that by utilizing Scala or Python – developers can compose the most compact code and utilizing Java or Scala they can accomplish the best runtime execution. The best trade-off is to utilize Scala for Spark as it uses all the mainstream features, instead of developers having to master the advanced constructs.
With growing realization among the developers community that Scala not just gives the traditional agile language a close run yet but also helps organizations with taking their products to the next level with ease. This is the best time to learn Scala for Spark programming to adjust to the changing technological needs for big data processing. Scala programming may be a difficult language to ace for Apache Spark however the time spent on learning it is worth investment. Its winning combination of both object-oriented and functional programming paradigms might be surprising to beginners and they might take some time to pick up the new syntax.
Hands-on experience in working with Scala for Spark projects comes as an additional advantage for developers who want to enjoy programming in Apache Spark in a hassle-free way.