Apache Spark with Scala / Python and Apache Storm Certification types

Apache Spark, the general-purpose framework supports a wide range of programming languages like Scala, Python, R and Java. Hence it is common to come across the question, which language to choose for the project related to Spark. However, it is something tricky to answer this question since it depends on the use case, skillset and personal taste of the developers. The scale is the language that several developers prefer to choose. In this article, we are going to get an idea about these languages for Spark.

Most of the developers eliminate Java, though they have worked on this for long periods. This is because Java is not suitable for big data Apache Spark projects when compared to Scala and Python. It is very verbose. Even for achieving a simple goal, developers need to write several lines of codes. Of course, the introduction of Lambda expressions with Java 8 reduce this issue, but still Java is not as flexible as Scala and Python. In addition, Java doesn’t provide Read-Evaluate-Print Loop (REPL) interactive shell. This is a deal breaker for several developers. With the feature of an interactive shell, data scientists and developers can explore as well as access the dataset & archetype their application effortlessly without complete development cycle. When it comes to Big Data project, it is an essential tool.

Advantage Of Python

Python remains the preferable choice for several machine-learning algorithms. Parallel ML algorithms are included only in the Mlib and that are appropriately the distributed data set. If a developer has good proficient over Python, then they can easily develop machine-learning application.

Python vs. Scala

Next, let us look into the comparison of Scala and Python. Both of these languages include some similar features. They are as follows:

●     Both are functional

●     Both are object oriented

●     Both include passionate support communities

Scala include some beneficial support than Python and they are listed below:

●     It is a static kind. It appears like dynamic-oriented language since it utilizes a refined sort of inference mechanism. That is, it is still possible to use the compiler to fetch the compile-time issues.

●     Scala, in general, faster than Python. In case you are looking the language for important processing logic, which is designed in your codes, then choosing Scala would offer better performance.

●     Since Spark is developed on the Scala, being an expert in Scala helps the developers to debug the source code when something doesn’t perform as they expect. When it comes to rapidly developing an open source project such as Spark, it is significantly true.

●     Using the language Scala for Spark project allows the user to use the current greatest features.

●     Most of the new features are first added to Scala and then import to Python.

●     In case the developers use Python for Spark codes, which is written in Scala, translation run between these different languages and environment. This situation might remain as the source of unwanted issues and more bugs. 

●     Scala is a statically typed language that supports in finding errors earlier, even at the compile-time. However, Python is a Dynamic typed language.

●     With Scala, most of the unit test case code can be reused in the application.

●     Streaming processing includes the weakest provision in the Python. The initial streaming API of Python only supports the elementary source such as text file and text over the socket. In addition, still Python custom source not supports Kenesis and Flume. The two streaming output operation like saveAsHadoopFile() and saveAsObjectFile() are not existing in the today’s Python. 

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