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Scala Vs Python Vs R Vs Java - Which language is better for Spark & Why?

One of the most important decisions for the Big data learners or beginners is choosing the best programming language for big data manipulation and analysis. Just understanding business problems and choosing the right model is not enough but implementing them perfectly is equally important and choosing the right language (or languages) for solving the problem goes a long way. If you search top and highly effective programming languages for Big Data on Google, you will find the following top 4 programming languages: JavaScalaPythonRJavaJava is one of the oldest languages of all 4 programming languages listed here. Traditional Frameworks of Big data like Apache Hadoop and all the tools within its ecosystem are Java-based and hence using java opens up the possibility of utilizing large ecosystem of tools in the big data world.  ScalaA beautiful crossover between object-oriented and functional programming language is Scala. Scala is a highly Scalable Language. Scala was invented by the German Computer Scientist, Martin Odersky and the first version was launched in the year 2003.PythonPython was originally conceptualized by Guido van Rossum in the late 1980s. Initially, it was designed as a response to the ABC programming language and later gained its popularity as a functional language in a big data world. Python has been declared as one of the fastest-growing programming languages in 2018 as per the recently held Stack Overflow Developer Survey. Many data analysis, manipulation, machine learning, deep learning libraries are written in Python and hence it has gained its popularity in the big data ecosystem. It’s a very user-friendly language and it is its biggest advantage.  Fun factPython is not named after the snake. It’s named after the British TV show Monty Python.RR is the language of statistics. R is a language and environment for statistical computing and graphics. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is named partly after the first names of the first two R authors and partly as a play on the name of S*. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.*SS is a statistical programming language developed primarily by John Chambers and R is an implementation of the S programming language combined with lexical scoping semantics, inspired by Scheme.Every framework is implemented in the underlying programming language for its implementation. Ex Zend uses PHP, Panda Framework uses python similarly Hadoop framework uses Java and Spark uses Scala.However, Spark officially supports Java, Scala, Python and R, all 4 languages. If one browses through Apache Spark’s official website documentation, he/she would find many other languages utilized by the open-source community for Spark implementation.    When any developer wants to start learning Spark, the first question he stumbles upon is, out of these pools of languages, which one to use and which one to master? Solution Architects would have a tough time choosing the right language for spark framework and Organizations will always be wondering, which skill sets are relevant for my problem if one doesn’t have the right knowledge about these languages in the context of Spark.    This article will try to answer all these queries.so let’s start-JavaOldest of all and popular, widely adopted programming language of all. There is a number offeatures/advantages due to which Java is favorite for Big data developers and tool creators:Java is platform-agnostic language and hence it can run on almost any system. Java is portable due to something called Java Virtual Machine – JVM. JVM is a foundation of Hadoop ecosystem tools like Map Reduce, Storm, Spark, etc. These tools are written in Java and run on JVM.Java provides various communities support like GitHub and stack overflow etc.Java is scalable, backward compatible, stable and production-ready language. Also, supports a large variety of tried and tested libraries.It is statically typed language (We would see details of this functionality in later sections, in comparison with others)Java is mostly the choice for most of the big data projects but for the Spark framework, one has to ponder upon, whether Java would be the best fit.One major drawback of Java is its verbosity. One has to write long code (number of lines of code) to achieve simple functionality in Java.Java does not support Read-Evaluate-Print-Loop (REPL) which is a major deal-breaker when choosing a programming language for big data processing.ScalaScala is comparatively new to the programming scene but has become popular very quickly. Above are a few quotes from bigger names in the industry for Scala. From the Spark context, many experts prefer Scala over other programming languages as Spark is written in Scala. Scala is the native language of Spark. It means any new API always first be available in Scala.Scala is a hybrid functional programming language because It has both the features of object-oriented programming and functional programming. As an OO Programming Language, it considers every value as an object and all OOPS concepts apply. As a functional programming language, it defines and supports functions. All operations are done as functions. No variable stands by itself. Scala is a machine-compiled language.Scala and Java are popular programming languages that run over JVM. JVM makes these languages framework friendly. One can say, Scala is an advanced level of Java.Features/Advantages of Scala:It’s general-purpose object-oriented language with functional language properties too. It’s less verbose than Java.It can work with JVM and hence is portable.It can support Java APIs comfortably.It's fast and robust in Spark context as its Spark native.It is a statically typed language.Scala supports Read-Evaluate-Print-Loop (REPL)Drawbacks / Downsides of Scala:Scala is complex to learn due to the functional nature of language.Steep learning curve.Lack of matured machine learning languages.PythonPython is one of the de-facto languages of Data Science. It is a simple, open-source, general-purpose language and is very easy to learn. It has a rich set of libraries, utilities, ready-to-use features and support to a number of mature machine learning, big data processing, visualization libraries.Advantages of Python:It is interpreted language (i.e. support to REPL, Read, Evaluate, Print, Loop.) If you type a command into a command-line interpreter and it responds immediately. Java lacks this feature.Easy to learn, easy debugging, fewer lines of code.It is dynamically typed. i.e. can dynamically defined variable types. i.e. Python as a language is type-safe.Python is platform agnostic and scalable.Drawbacks/Disadvantages:Python is slow. Big data professionals find projects built in Java / Scala are faster and robust than the once with python.Whilst using user-defined functions or third party libraries in Python with Spark, processing would be slower as increased processing is involved as Python does not have equivalent Java/Scala native language API for these functionalities.Python does not support heavy weight processing fork() using uWSGI but it does not support true multithreading.R LanguageR is the favourite language of statisticians. R is fondly called a language of statisticians.  It’s popular for research, plotting, and data analysis. Together with RStudio, it makes a killer statistic, plotting, and data analytics application.R is majorly used for building data models to be used for data analysis.Advantages/Features of R:Strong statistical modeling and visualization capabilities.Support for ‘data science’ related work.It can be integrated with Apache Hadoop and Spark easily.Drawbacks/Disadvantages of R:R is not a general-purpose language.The code written in R cannot be directly deployed into production. It needs conversion into Java or Python.Not as fast as Java / Scala.Comparison of four languages for Apache SparkWith the introduction of these 4 languages, let’s now compare these languages for the Spark framework:These languages can be categorized into 2 buckets basis high-level spark architecture support, broadly:JVM Languages: Java and ScalaNon-JVM Languages: Python and RDue to these categorizations, performance may vary. Let’s understand architecture in little depth to understand the performance implications of using these languages. This would also help us to understand the question of when to use which language.Spark Framework High-level architecture An application written in any one of the languages is submitted on the driver node and further driver node distributes the workload by dividing the execution on multiple worker nodes.JVM compatible Application Execution Flow Consider the applications written are JVM compatible (Java/Scala). Now, Spark is also written in native JVM compatible Scala language, hence there is no explicit conversion required at any point of time to execute JVM compatible applications on Spark. Also, this makes the native language applications faster to perform on the Spark framework.There are multiple scenarios for Python/R written applications:Python/R driver talk to JVM driver by socket-based API. On the driver node, both the driver processes are invoked when the application language is non-JVM language.Scenario 1: Applications for which Equivalent Java/Scala Driver API exists - This scenario executes the same way as JVM compatible applications by invoking Java API on the driver node itself. The cost for inter-process communication through sockets is negligible and hence performance is comparable. This is with the assumption that processed data over worker nodes are not to be sent back to the Driver again.Scenario 1(b): If the assumption taken is void in scenario 1 i.e. processed data on worker nodes is to be sent back to driver then there is significant overhead and serialization required. This adds to processing time and hence performance in this scenario deteriorates.Scenario 2: Applications for which Equivalent Java/Scala Driver API do not exist – Ex. UDF (User-defined functions) / Third party python libraries. In such cases equivalent Java API doesn’t exist and hence, additional executor sessions are initiated on worker node and python API is serialized on worker node and executed. This python worker processes in addition to JVM and coordination between them is overhead. Processes also compete for resources which adds to memory contention.In addition, if the data is to send back to the driver node then processing takes a lot of time and problem scales up as volume increases and hence performance is bigger problem.As we have seen a performance, Let’s see the tabular comparison between these languages.Comparison PointsJavaScalaPythonRPerformanceFasterFaster (about 10x faster than Python)SlowerSlowerLearning CurveEasier than JavaTougher than PythonSteep learning curve than Java & PythonEasiestModerateUser GroupsWeb/Hadoop programmersBig Data ProgrammersBeginners & Data EngineersData Scientists/ StatisticiansUsageWeb development and Hadoop NativeSpark NativeData Engineering/ Machine Learning/ Data VisualizationVisualization/ Data Analysis/ Statistics use casesType of LanguageObject-Oriented, General PurposeObject-Oriented & Functional General PurposeGeneral PurposeSpecifically for Data Scientists.Needs conversion into Scala/Python before productizingConcurrencySupport ConcurrencySupport ConcurrencyDoes not Support ConcurrencyNAEase of UseVerboseLesser Verbose than ScalaLeast VerboseNAType SafetyStatically typedStatically typed (except for Spark 2.0 Data frames)Dynamically TypedDynamically TypedInterpreted Language (REPL)NoNoYesYesMaturated machine learning libraries availability/ SupportLimitedLimitedExcellentExcellentVisualization LibrariesLimitedLimitedExcellentExcellentWeb Notebooks SupportIjava Kernel in Jupyter NotebookApache Zeppelin Notebook SupportJupyter Notebook SupportR NotebookWhich language is better for Spark and Why?With the info we gathered for the languages, let's move to the main question i.e. which language to choose for Spark? My answer is not a straightforward single language for this question. I will state my point of view for choosing the proper language: If you are a beginner and want to choose a language from learning Spark perspective. If you are organization/ self employed or looking to answer a question for solutioning a project perspective. I. If you are beginner:If you are a beginner and have no prior education of programming language then Python is the language for you, as it’s easy to pick up. Simple to understand and very user-friendly. It would prove a good starting point for building Spark knowledge further. Also, If you are looking for getting into roles like ‘data engineering’, knowledge of Python along with supported libraries will go a long way. If you are a beginner but have education in programming languages, then you may find Java very familiar and easy to build upon prior knowledge. After all, it grapevine of all the languages.  If you are a hardcore bigdata programmer and love exploring complexities, Scala is the choice for you. It’s complex but experts say if once you love Scala, you will prefer it over other languages anytime.If you are a data scientist, statistician and looking to work with Spark, R is the language for you. R is more science oriented than Python. II. If you are organization/looking for choice of language for implementations:You need to answer the following important questions before choosing the language:Skills and Proficiency: Which skill-sets and proficiency over language, you already have with you/in your team?Design goals and availability of features/ Capability of language: Which libraries give you better support for the type of problem(s) you are trying to solve.Performance implications Details of these explained below: 1. Skillset: This is very straightforward. Whichever is available skill set within a team, go with that to solve your problem, after evaluating answers of other two questions. If you are self-employed, the one you have proficiency is the most likely suitable choice of language.  2. Library Support:  Following gives high-level capabilities of languages:R: Good for research, plotting, and data analysis.Python: Good for small- or medium-scale projects to build models and analyse data, especially for fast start-ups or small teams.Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance.In my opinion, Scala/Java can be used for larger robust projects to ease maintenance. Also, If one wants the app to scale quickly and needs it to be robust, Scala is the choice.Python and R: Python is more universal language than R, but R is more science oriented. Broadly, one can say Python can be implemented for Data engineering use cases and R for Data science-oriented use cases. On the other hand, if you discover these two languages have about the same library support you need, then pick the one whose syntax you prefer. You may find that you need both depending on the situation. 3. Performance: As seen earlier in the article, Scala/ Java is about 10x faster than Python/R as they are JVM supported languages. However, if you are writing Python/R applications wisely (like without using UDFs/ Not sending data back to the Driver etc) they can perform equally well.ConclusionFor learning, depending upon your prior knowledge, Python is the easiest of all to pick up. For implementations, Choice is in your hands which language to choose for implementations but let me tell you one secret or a tip, you don’t have to stick to one language until you finish your project. You can divide your problem in small buckets and utilize the best language to solve the problem. This way, you can achieve balance between optimum performance, availability, proficiency in a skill, and sub-problem at hand.  Do let us know how your experience was in learning the language comparisons and the language you think is better for Spark. Moreover, which one you think is “the one for you”, through comments below.
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Scala Vs Python Vs R Vs Java - Which language is better for Spark & Why?

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Scala Vs Python Vs R Vs Java - Which language is better for Spark & Why?

One of the most important decisions for the Big data learners or beginners is choosing the best programming language for big data manipulation and analysis. Just understanding business problems and choosing the right model is not enough but implementing them perfectly is equally important and choosing the right language (or languages) for solving the problem goes a long way. 

If you search top and highly effective programming languages for Big Data on Google, you will find the following top 4 programming languages: 

  1. Java
  2. Scala
  3. Python
  4. R

Java

Java is one of the oldest languages of all 4 programming languages listed here. Traditional Frameworks of Big data like Apache Hadoop and all the tools within its ecosystem are Java-based and hence using java opens up the possibility of utilizing large ecosystem of tools in the big data world.  

Scala

A beautiful crossover between object-oriented and functional programming language is Scala. Scala is a highly Scalable Language. Scala was invented by the German Computer Scientist, Martin Odersky and the first version was launched in the year 2003.

Python

Python was originally conceptualized by Guido van Rossum in the late 1980s. Initially, it was designed as a response to the ABC programming language and later gained its popularity as a functional language in a big data world. Python has been declared as one of the fastest-growing programming languages in 2018 as per the recently held Stack Overflow Developer Survey. Many data analysis, manipulation, machine learning, deep learning libraries are written in Python and hence it has gained its popularity in the big data ecosystem. It’s a very user-friendly language and it is its biggest advantage.  

Fun fact

Python is not named after the snake. It’s named after the British TV show Monty Python.

R

R is the language of statistics. R is a language and environment for statistical computing and graphics. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is named partly after the first names of the first two R authors and partly as a play on the name of S*. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.

*S

S is a statistical programming language developed primarily by John Chambers and R is an implementation of the S programming language combined with lexical scoping semantics, inspired by Scheme.

Every framework is implemented in the underlying programming language for its implementation. Ex Zend uses PHP, Panda Framework uses python similarly Hadoop framework uses Java and Spark uses Scala.

However, Spark officially supports Java, Scala, Python and R, all 4 languages. If one browses through Apache Spark’s official website documentation, he/she would find many other languages utilized by the open-source community for Spark implementation.    

When any developer wants to start learning Spark, the first question he stumbles upon is, out of these pools of languages, which one to use and which one to master? Solution Architects would have a tough time choosing the right language for spark framework and Organizations will always be wondering, which skill sets are relevant for my problem if one doesn’t have the right knowledge about these languages in the context of Spark.    

This article will try to answer all these queries.so let’s start-

Java

Oldest of all and popular, widely adopted programming language of all. There is a number of

features/advantages due to which Java is favorite for Big data developers and tool creators:

  1. Java is platform-agnostic language and hence it can run on almost any system. Java is portable due to something called Java Virtual Machine – JVM. JVM is a foundation of Hadoop ecosystem tools like Map Reduce, Storm, Spark, etc. These tools are written in Java and run on JVM.
  2. Java provides various communities support like GitHub and stack overflow etc.
  3. Java is scalable, backward compatible, stable and production-ready language. Also, supports a large variety of tried and tested libraries.
  4. It is statically typed language (We would see details of this functionality in later sections, in comparison with others)

Java is mostly the choice for most of the big data projects but for the Spark framework, one has to ponder upon, whether Java would be the best fit.

One major drawback of Java is its verbosity. One has to write long code (number of lines of code) to achieve simple functionality in Java.

Java does not support Read-Evaluate-Print-Loop (REPL) which is a major deal-breaker when choosing a programming language for big data processing.

ScalaScala

Scala is comparatively new to the programming scene but has become popular very quickly. Above are a few quotes from bigger names in the industry for Scala. From the Spark context, many experts prefer Scala over other programming languages as Spark is written in Scala. Scala is the native language of Spark. It means any new API always first be available in Scala.

Scala is a hybrid functional programming language because It has both the features of object-oriented programming and functional programming. As an OO Programming Language, it considers every value as an object and all OOPS concepts apply. As a functional programming language, it defines and supports functions. All operations are done as functions. No variable stands by itself. Scala is a machine-compiled language.

Scala and Java are popular programming languages that run over JVM. JVM makes these languages framework friendly. One can say, Scala is an advanced level of Java.

Scala

Features/Advantages of Scala:

  1. It’s general-purpose object-oriented language with functional language properties too. It’s less verbose than Java.
  2. It can work with JVM and hence is portable.
  3. It can support Java APIs comfortably.
  4. It's fast and robust in Spark context as its Spark native.
  5. It is a statically typed language.
  6. Scala supports Read-Evaluate-Print-Loop (REPL)

Drawbacks / Downsides of Scala:

  1. Scala is complex to learn due to the functional nature of language.
  2. Steep learning curve.
  3. Lack of matured machine learning languages.

Python

Python is one of the de-facto languages of Data Science. It is a simple, open-source, general-purpose language and is very easy to learn. It has a rich set of libraries, utilities, ready-to-use features and support to a number of mature machine learning, big data processing, visualization libraries.

Advantages of Python:

  1. It is interpreted language (i.e. support to REPL, Read, Evaluate, Print, Loop.) If you type a command into a command-line interpreter and it responds immediately. Java lacks this feature.
  2. Easy to learn, easy debugging, fewer lines of code.
  3. It is dynamically typed. i.e. can dynamically defined variable types. i.e. Python as a language is type-safe.
  4. Python is platform agnostic and scalable.

Drawbacks/Disadvantages:

  1. Python is slow. Big data professionals find projects built in Java / Scala are faster and robust than the once with python.

Whilst using user-defined functions or third party libraries in Python with Spark, processing would be slower as increased processing is involved as Python does not have equivalent Java/Scala native language API for these functionalities.

  1. Python does not support heavy weight processing fork() using uWSGI but it does not support true multithreading.

R Language

R is the favourite language of statisticians. R is fondly called a language of statisticians.  It’s popular for research, plotting, and data analysis. Together with RStudio, it makes a killer statistic, plotting, and data analytics application.

R is majorly used for building data models to be used for data analysis.

Advantages/Features of R:

  1. Strong statistical modeling and visualization capabilities.
  2. Support for ‘data science’ related work.
  3. It can be integrated with Apache Hadoop and Spark easily.

Drawbacks/Disadvantages of R:

  1. R is not a general-purpose language.
  2. The code written in R cannot be directly deployed into production. It needs conversion into Java or Python.
  3. Not as fast as Java / Scala.

Comparison of four languages for Apache Spark

With the introduction of these 4 languages, let’s now compare these languages for the Spark framework:

These languages can be categorized into 2 buckets basis high-level spark architecture support, broadly:

  1. JVM Languages: Java and Scala
  2. Non-JVM Languages: Python and R

Due to these categorizations, performance may vary. Let’s understand architecture in little depth to understand the performance implications of using these languages. This would also help us to understand the question of when to use which language.

Spark Framework High-level architectureSpark Framework High-level architecture 

An application written in any one of the languages is submitted on the driver node and further driver node distributes the workload by dividing the execution on multiple worker nodes.

JVM compatible Application Execution FlowJVM compatible Application Execution Flow 

Consider the applications written are JVM compatible (Java/Scala). Now, Spark is also written in native JVM compatible Scala language, hence there is no explicit conversion required at any point of time to execute JVM compatible applications on Spark. Also, this makes the native language applications faster to perform on the Spark framework.

There are multiple scenarios for Python/R written applications:

Python/R driver talk to JVM driver by socket-based API. On the driver node, both the driver processes are invoked when the application language is non-JVM language.

Scenario 1: Applications for which Equivalent Java/Scala Driver API exists - This scenario executes the same way as JVM compatible applications by invoking Java API on the driver node itself. The cost for inter-process communication through sockets is negligible and hence performance is comparable. This is with the assumption that processed data over worker nodes are not to be sent back to the Driver again.

Scenario 1(b): If the assumption taken is void in scenario 1 i.e. processed data on worker nodes is to be sent back to driver then there is significant overhead and serialization required. This adds to processing time and hence performance in this scenario deteriorates.

JVM compatible Application Execution Flow

Scenario 2: Applications for which Equivalent Java/Scala Driver API do not exist – Ex. UDF (User-defined functions) / Third party python libraries. In such cases equivalent Java API doesn’t exist and hence, additional executor sessions are initiated on worker node and python API is serialized on worker node and executed. This python worker processes in addition to JVM and coordination between them is overhead. Processes also compete for resources which adds to memory contention.

In addition, if the data is to send back to the driver node then processing takes a lot of time and problem scales up as volume increases and hence performance is bigger problem.

JVM compatible Application Execution Flow

As we have seen a performance, Let’s see the tabular comparison between these languages.

Comparison PointsJavaScalaPythonR
PerformanceFasterFaster (about 10x faster than Python)SlowerSlower
Learning CurveEasier than Java
Tougher than Python

Steep learning curve than Java & PythonEasiestModerate
User GroupsWeb/Hadoop programmersBig Data ProgrammersBeginners & Data EngineersData Scientists/ Statisticians
UsageWeb development and Hadoop NativeSpark NativeData Engineering/ Machine Learning/ Data VisualizationVisualization/ Data Analysis/ Statistics use cases
Type of LanguageObject-Oriented, General PurposeObject-Oriented & Functional General PurposeGeneral PurposeSpecifically for Data Scientists.
Needs conversion into Scala/Python before productizing

ConcurrencySupport ConcurrencySupport ConcurrencyDoes not Support ConcurrencyNA
Ease of UseVerboseLesser Verbose than ScalaLeast VerboseNA
Type SafetyStatically typedStatically typed (except for Spark 2.0 Data frames)Dynamically TypedDynamically Typed
Interpreted Language (REPL)NoNoYesYes
Maturated machine learning libraries availability/ SupportLimitedLimitedExcellentExcellent
Visualization LibrariesLimitedLimitedExcellentExcellent
Web Notebooks SupportIjava Kernel in Jupyter NotebookApache Zeppelin Notebook SupportJupyter Notebook Support

R Notebook

Which language is better for Spark and Why?

With the info we gathered for the languages, let's move to the main question i.e. which language to choose for Spark? 

My answer is not a straightforward single language for this question. I will state my point of view for choosing the proper language: 

  1. If you are a beginner and want to choose a language from learning Spark perspective. 
  2. If you are organization/ self employed or looking to answer a question for solutioning a project perspective. 

I. If you are beginner:

  • If you are a beginner and have no prior education of programming language then Python is the language for you, as it’s easy to pick up. Simple to understand and very user-friendly. It would prove a good starting point for building Spark knowledge further. Also, If you are looking for getting into roles like ‘data engineering’, knowledge of Python along with supported libraries will go a long way. 
  • If you are a beginner but have education in programming languages, then you may find Java very familiar and easy to build upon prior knowledge. After all, it grapevine of all the languages.  
  • If you are a hardcore bigdata programmer and love exploring complexities, Scala is the choice for you. It’s complex but experts say if once you love Scala, you will prefer it over other languages anytime.
  • If you are a data scientist, statistician and looking to work with Spark, R is the language for you. R is more science oriented than Python. 

II. If you are organization/looking for choice of language for implementations:

You need to answer the following important questions before choosing the language:

  1. Skills and Proficiency: Which skill-sets and proficiency over language, you already have with you/in your team?
  2. Design goals and availability of features/ Capability of language: Which libraries give you better support for the type of problem(s) you are trying to solve.
  3. Performance implications 

Details of these explained below: 

1. Skillset: This is very straightforward. Whichever is available skill set within a team, go with that to solve your problem, after evaluating answers of other two questions. 
If you are self-employed, the one you have proficiency is the most likely suitable choice of language.  

2. Library Support:  
Following gives high-level capabilities of languages:

  • R: Good for research, plotting, and data analysis.
  • Python: Good for small- or medium-scale projects to build models and analyse data, especially for fast start-ups or small teams.
  • Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance.
    In my opinion, Scala/Java can be used for larger robust projects to ease maintenance. Also, If one wants the app to scale quickly and needs it to be robust, Scala is the choice.
    Python and R: Python is more universal language than R, but R is more science oriented. Broadly, one can say Python can be implemented for Data engineering use cases and R for Data science-oriented use cases. On the other hand, if you discover these two languages have about the same library support you need, then pick the one whose syntax you prefer. You may find that you need both depending on the situation. 

3. Performance: As seen earlier in the article, Scala/ Java is about 10x faster than Python/R as they are JVM supported languages. However, if you are writing Python/R applications wisely (like without using UDFs/ Not sending data back to the Driver etc) they can perform equally well.

Conclusion

For learning, depending upon your prior knowledge, Python is the easiest of all to pick up. 

For implementations, Choice is in your hands which language to choose for implementations but let me tell you one secret or a tip, you don’t have to stick to one language until you finish your project. You can divide your problem in small buckets and utilize the best language to solve the problem. This way, you can achieve balance between optimum performance, availability, proficiency in a skill, and sub-problem at hand.  

Do let us know how your experience was in learning the language comparisons and the language you think is better for Spark. Moreover, which one you think is “the one for you”, through comments below.

Shruti

Shruti Deshpande

Blog Author

10+ years of data-rich experience in the IT industry. It started with data warehousing technologies into data modelling to BI application Architect and solution architect.


Big Data enthusiast and data analytics is my personal interest. I do believe it has endless opportunities and potential to make the world a sustainable place. Happy to ride on this tide.


*Disclaimer* - Expressed views are the personal views of the author and are not to be mistaken for the employer or any other organization’s views.

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If it is the first reason, then Scala might be the best one for you, otherwise, it is a simplified programming language like Kotlin. Now let us first briefly discuss Scala and Kotlin individually. ScalaDeveloped by Martin Odersky, the first version of Scala was launched in the year 2003 and is a classic example of a  general-purpose, object-oriented computer language, offering a wide range of functional programming language features and a strong static type system. Inspired from Java itself, Scala, as the name suggests, is highly scalable and this very feature sets Scala apart from other programming languages. When we say that Scala is inspired from Java, that means developers can code Scala in the same way they do for Java. Additionally, Scala makes it possible to use numerous Java and libraries within itself as well. It is designed to be able to use an elegant, concise and type-safe method to express common programming patterns. Scala is a very popular programming language amongst developers and rising up its ranks in the world of technology. Although Scala comes with a number of plus points, there are some which make it a bit ineffective. Here are the strengths and weaknesses of Scala. Strengths: Full Support for Pattern Matching, Macros, and Higher-Kinded Types Has a very flexible code syntax Gets a bigger Community Support Enables overloading operators Weaknesses: Slow in compilation Challenging Binary Compilation Not so proficient in the Management of Null SafetyKotlin Developed by JetBrains, Kotlin was released on February 2012 as an open-source language. Until now, there have been two released versions with the latest one being Kotlin 1.2, the most stable version that was released on November 28, 2017. Since Kotlin is extremely compatible with Java 6 the latest version of Java on Android, it has gained critical acclaim on Android worldwide and additionally, it offers various key features that are prepared only for Java 8 and not even Java 6 developers have access to that. Kotlin provides seamless and flawless interoperability with Java. That means, developers can easily call Java codes from Kotlin and same goes the other way around. The built-in null safety feature avoids showing the NullPointerException (NPE) that makes developing android apps easy and joyful, something every android programmer wants. Below mentioned are the key pointers on the strengths and weaknesses of Kotlin. Strengths Takes a Functional Programming Approach and Object-Oriented Programming style(OOP) Style  Has Higher-Order Functions Short, Neat, and Verbose-Free Expression  Supported by JetBrains and Google. Weaknesses: More limited Pattern Matching Additional Runtime Size Initial Readability of Code Shortage of Official Support Smaller Support Community. Ease of learning: Scala vs Kotlin Scala is a powerful programming language packed with superior features and possesses a flexible syntax. It is not an easy language to learn and is a nightmare for newcomers. Kotlin, on the other hand, has been reported to have been an easy-to-learn language for many Java developers as getting started with Kotlin is relatively easy and so is writing codes. Even though it is a comparatively easier language to learn and code with, Kotlin lacks the solid set of features that is common in Scala. It might take less time to learn a programming language, but the most important thing to look for is a comprehensive array of features. Scala, even though a very difficult language to learn, is cherished by the developers as it lets them do things that cannot be done in Kotlin Here are the major differences between Scala and Kotlin: ScalaKotlinType inferenceEfficientImmutabilityExtension FunctionsSingleton objectMassive InteroperabilityConcurrency controlLessens Crashes at RuntimeString interpolationSmart Cast FunctionHigher-order functionSafe and ReliableCase classes and Pattern matching Lazy computationLow adoption costRich collection setMaking the appropriate choice of languageNow, whether you may like a programming language or not, if that very language helps you get the best out of your job, then you will have to live with it. These are the facts about getting the best results. The outcome is the main factor in you deciding the appropriate language for your job. Kotlin is the only option for Android development as Android doesn’t use JVM, so any old JVM-compatible language will not work in Android. Kotlin has it all what it takes to compile, debug, and run the software on Android because of which it is in-built into Android Studio. However, Kotlin is not so usable outside Android development. If you are one of the developers who like working with Eclipse for your IDE, then Scala IDE is better than the Kotlin Plugin even if you can make Eclipse work with both the languages with limitations. Scala IDE is more advanced than the Kotlin plugin and is easier to set up. Some developers found it quite difficult to make the Kotlin plugin work. This case is quite the same with NetBeans. Kotlin is still getting there but is already popular amongst Java developers as it offers an easier transition than Scala. Kotlin is still maturing, but many Java people find adopting it is an easier transition than Scala is.  Scala, however, is for developers who are focused more on discovering new ideas while Kotlin is for those who want to get results. Kotlin stresses fast compilation but is more restrictive while Scala gives a lot of flexibility. Go for Scala if you breathe functional programming! It has more appropriate features for this type of programming than Kotlin does. Scala supports currying and partial application, the methods of breaking down functions requiring multiple arguments offering more flexibility. Go for the one that is the most appropriate one for your work, style of working and what you are aiming at. Think before you leap. The Outcome At the end of the day, all that matters is what you want to use the language for. While Scala goes well for the projects that require a combination of functional, OOP style programming languages, and where programmers need to handle lots of data or complex modelling, Kotlin becomes the best choice when you want something less frustrating than Java while developing apps because using Kotlin makes app development less cumbersome and a great thing to work on. It is just like a better-looking version of Java with less lengthy codes. 
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Scala Vs Kotlin

Ever-changing requirements in coding have always b... Read More

Xcode vs Swift

Xcode and Swift are two different products developed by Apple for macOS, iOS, iPadOS, watchOS, and tvOS. While Xcode is an integrated development environment (IDE) for macOS containing a suite of software development tools to develop software for macOS, iOS, iPadOS, watchOS, and tvOS, Swift is a general-purpose, multi-paradigm, compiled programming language developed iOS, macOS, watchOS, tvOS, Linux, and z/OS. So it is clear that they can not be compared with each other. On the contrary, Swift is compatible with Xcode as Swift v 5.1, the default version of Swift is included in Xcode v 11. In this article, we will go through what Xcode and Swift are in general and cover their features strengths and weaknesses followed by how Swift is compatible with Xcode. XcodeIt was first released in 2003 as version 1 with the latest stable one being version 10.2.1 released on 17 April 2019. It can be downloaded from the Mac App Store and is free to use for macOS Mojave users. Registered developers may download the preview releases and previous versions of the suite using via the Apple Developer website.  Overview of the major featuresSupport: Programming languages such as C, C++, Objective-C, Objective-C++, Java, AppleScript, Python, Ruby, ResEdit (Rez), and Swift are supported by Xcode with source code along with support for a variety of programming models including Cocoa, Carbo, and Java. Not only that, there is additional support via third parties for GNU Pascal, Free Pascal, Ada, C#, Perl, and D Capability: Xcode can build fat binary files that include the code for various architectures in the Mach-O executable format. Known as universal binary files, these allow the application to run on both PowerPC and Intel-based (x86) platforms including both 32-bit and 64-bit codes Compiling and debugging: Xcode uses the iOS SDK to compile and debug applications for iOS that run on ARM architecture processors GUI tool: Xcode comprises of the GUI tool, Instruments that runs dynamic tracing framework on the top of DTrace, a dynamic tracing framework designed by Sun Microsystems and released as a part of OpenSolaris. Advantages and disadvantages of Xcode: Xcode is designed by Apple and will only work with Apple operating systems: macOS, iOS, iPadOS, watchOS, and tvOS. Since its release in 2003, Xcode has made significant improvements and the latest version, Xcode 10.2.1 has all the features that are needed to perform continuous integration. Let us have a look at the pros of using Xcode: Equipped with a well designed and easy to use UI creator Excellent for code completion Using Xcode, a developer can learn profiling and heap analysis in a natural way Xcode’s simulator lets you easily test your app while you build it in an environment that simulates your iPhone The app store has a wide range of audience who are willing to pay for apps. Now, the cons: Clunky and outdated Objective C makes it more frustrating if you are habituated to use a modern language No support for tabbed work environments makes it difficult to work with multiple windows Hardly any information can be found online to solve problems due to a previous Apple NDA on Xcode development It is a complicated process to export your app onto a device Will only work with Apple operating systems The App Store approval process can be annoyingly lengthy.SwiftSwift was launched at Apple's 2014 Worldwide Developers Conference as a general-purpose, multi-paradigm, compiled programming language for iOS, macOS, watchOS, tvOS, Linux, and z/OS Being a new entry these operating systems, Swift accelerates on the best parts of C and Objective C without being held back by its compatibility. It utilises safe patterns for programming, adding more features to it, thus making programming easier and more flexible. By developing their existing debugger, compiler and framework infrastructure, it took quite some time to create the base for Swift. Furthermore, Automatic Reference Counting was used to simplify the memory management part. The framework stack which was once built upon a solid framework of Cocoa and Foundation has undergone significant changes and is now completely regulated and refurbished. Developers who have worked with Objective-C do find Swift quite similar. Objective-C’s dynamic object model and its comprehensively named parameters provide a lot of control to Swift.  Developers can use Swift to have access to the existing Cocoa framework in addition to the mix and match interoperability with an objective C code. Swift uses this common rule to offer multiple new features in combination with object-oriented and procedural portions of the language. The idea is to create the best possible language for a wide range of uses, varying from desktop and mobile apps, systems programming, and scaling up to cloud services. The designing of Swift was done to make sure that developers find it easy to maintain and write correct programs. Coding done in Xcode is safe, fast and expressive. Swift offers a host of features that give developers the control needed to make the code easy to read and write. Furthermore, Apple made Swift to be easily understandable to help developers avoid making mistakes while coding and make the code look organised, along with the modules that give namespaces and eliminate headers. Since Swift uses some features present in other languages, one of them being named parameters written with clean syntax that makes the APIs much easier to maintain and read. Here are some of the additional features of Swift: Multiple return values and Tuples Generics Short and quick iterations over a collection or range Structs that support extensions, methods and protocols Functional programming patterns Advanced control flow Powerful error handling. These features are systematically designed to make them work together resulting in creating a powerful but fun-to-use language. Advantages and disadvantages of Swift: Pros of using the Swift Programming language: Easy to read and maintain: The Swift program codes are based on natural English as it has borrowed syntaxes from other programming languages. This makes the language more expressive Scalable: Users can add more features to Swift, making it a scalable programming language. In the future, Swift is what Apple is relying on and not Objective C Concise: Swift does not include long lines of code and that favours the developers who want a concise syntax, thus increasing the development and testing rate of the program Safety and improved performance: It is almost 40% better than the Objective-C when speed and performance are taken into consideration as it is easy to tackle the bugs which lead to safer programming Cross-device support: This language is capable of handling a wide range of Apple platforms such as iOS, iOS X, macOS, tvOS, and watchOS. Automatic Memory Management: This feature present in Swift prevents memory leaks and helps in optimizing the application’s performance that is done by using Automatic Reference Counting. Cons of Swift: Compatibility issues: The updated versions Swift is found to a bit unstable with the newer versions of Apple leading to a few issues. Switching to a newer version of Swift is the fix but that is costly Speed Issues: This is relevant to the earlier versions of the Swift programming language Less in number: The number of Swift developers is limited as Swift is a new programming language Delay in uploading apps: Developers will be facing delays over their apps written in Swift to be uploaded to the App Store only after iOS 8 and Xcode 6 are released. The estimated time for release is reported to be September-October, 2014. Conclusion So as we discussed both Xcode and Swift, it is clear that they cannot be compared to each other. In fact, they both complement each other to deliver impressive results without any headaches. Apple relies on both quite a lot and it is certain to have Swift and Xcode the perfect combination of a robust application and a user-friendly programming language.
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Xcode vs Swift

Xcode and Swift are two different products develop... Read More

ASP.NET VS PHP

ASP.NET and PHP are pretty popular languages in the programming world used by a huge number of developers and this makes it difficult for the new developers to choose either one of them. The comparison between these two has been in debate in recent times. Both of these languages are used in large web-based applications. Some successful companies like Google, Facebook, and Twitter, etc, also use these languages. In this article, we will understand the differences between PHP and ASP.Net also, will discuss which is better ASP.NET or PHP.Before we learn more about the differences between the two languages, we must first understand some basics of the two technologies:PHPPHP stands for Hypertext Preprocessor. It is an open-source programming language that is used for web development and can be embedded into HTML. The best part of PMP is that it’s free and possesses a  ton of frameworks which simplifies web development and also great for beginners since it allows simple and easy coding techniques. PHP is great for professionals as well because of its advanced features.Why use a PHP framework?A PHP framework provides a basic structure for streamlining the development of web apps. The applications and websites built using PHP frameworks will help the businesses to improve their performance needs.The best PHP frameworks available:LaravelCodeIgniterSymfonyZendPhalconCakePHPYiiFuelPHPPros and Cons of PHP frameworkPros:Rapid Development                                              Centralized DatabaseStronger TeamworkMakes your application more secure               Cons:Slower ExecutionPHP is unsecuredPoor error handling methodLimited Visibility and ControlDemand for PHP Developer:In today’s web development market, most of the websites are developed using PHP development tools which indicates a huge demand for PHP developers. If you are looking to make an entry to the IT world as a developer, then PHP programming will be an easy entry point.Taking up a PHP training from an authentic and reliable training provider will be a great platform to hone your skills.ASP.NETASP.NET is an open-source server-side web development tool developed by Microsoft for easy building of web applications and web pages. It can be written using any .Net supported language which makes it more popular among .NET developers. High speed and low cost are the main reasons to use it. Websites built ASP.NET is faster and more efficient than a website built with PHP.Pros and Cons of ASP.NET frameworkPros:Less coding timeWorld class toolboxConsistencyCustomizability and ExtensibilityCons:Limited Object-Relational (OR) supportBit expensiveSlower than Native CodeDemand for ASP.NET Developer:If you are a .NET developer, you will find yourself demanded by several asp.net development companies as your programming skills are extremely valuable in today’s market. There are many companies hunting for developers who can do programming with .NET. Therefore, it is advisable that you brush up your skills with ASP.NET Certification Training which will increase your value many times and have an edge over others. The ASP.NET Certification Training program will definitely make your future bright and offer you heaps of career opportunities. Whether you are a fresher or a working professional, you can take up the certification course.Comparison Between ASP.NET and PHPBoth ASP.NET and PHP frameworks are effective frameworks to work with, however, one may have few advantages over the other. Let’s dive deeper and compare these frameworks to understand which one is better than the other.1. Market Share:According to the report, BuiltWith data source PHP is the most used programming language which has 73% of market share, ASP.NET has 23% of market share. PHP also has a market share of 58% in top 100K websites and market share of PHP in 10K websites is 52%.Statistics for websites using Programming Language technologies:2. WebsitesHere are two lists to compare ASP.NET vs PHP websites:Websites built using PHPWebsites built using ASP.NETWikipediaFacebookYahooWordPress.comiStockPhotoMicrosoftDellGoDaddy3. Inbuilt featuresPHP has many unique in-built features that can help web developers. On the other hand, ASP.NET doesn’t have any such features.4. Speed and PerformanceWhen you compare PHP vs. ASP.NET for speed, PHP will be the winner. ASP.NET is a bit slow compared to PHP as it is built on the COM-based system whereas, PHP program runs on its own memory space.5. Community SupportCompared to ASP.NET, learning support is great in the PHP framework and has a large support community. It will be difficult for you to get hold of #C language of ASP.NET as it is difficult to understand.Key differences between ASP.NET vs PHPPHPASP.NETPHP was launched by Rasmus Lerdorf in the year 1995.ASP.NET was launched by Microsoft in the year 2002.PHP is a scripting languageASP.NET is a paid Microsoft provided web application framework.PHP suits for small sized organizationsASP.NET suits for a large and medium-sized organization.PHP has a decent market share in the  marketASP.NET has a higher market sharePHP works slow for desktop applicationsASP.NET is well equipped to assist and create desktop applications.PHP suits best for applications that contain a prime focus on UIASP.NET suits better for applications where the key concern is security.Easy to learnQuite challenging to learn.Coding using PHP is easy when compared to all other languagesCoding with ASP.NET is complicatedPHP execution is faster since it uses in-built memory spaceCoding with ASP.NET is complicatedPHP can run in Linux Operating System which is available for freeASP.NET requires a Windows platform which is not freeConclusionBoth PHP and ASP.NET come with their pros and cons. PHP is secure, fast, reliable, and inexpensive and ASP.NET is easier to use and maintain because of its class library system. Since both programming languages are similar and accomplish the same results so the company can make a choice based on the needs and requirements of the app they are about to develop.
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ASP.NET VS PHP

ASP.NET and PHP are pretty popular languages in th... Read More