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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Python vs Scala for Apache Spark: Which is Better? 

Nitika Sharma
Nitika Sharma Last Updated : 27 Jun, 2023
11 min read

Apache Spark is a powerful big data processing engine that has gained widespread popularity recently due to its ability to process massive amounts of data types quickly and efficiently. While Spark can be used with several programming languages, Python and Scala are popular for building Spark applications. Both languages offer unique advantages and have a loyal fan base. This article will provide an in-depth comparison of Python vs Scala for Apache Spark to help you choose the best functional language for your next Spark project.

Table of contents

What is Python?

Python language is a high-level, interpreted object-oriented programming language widely used for developing applications in various domains. It was created by Guido van Rossum in the late 1980s and has since become one of the most popular languages in the world. Python’s syntax is easy to read and learn, making it an excellent language for beginners. It has a vast standard library and many third-party modules that make it useful for a wide range of tasks, including web development, scientific computing, data engineering, and artificial intelligence. Python language is open-source and runs on multiple platforms, including Windows, macOS, and Linux.

It is not easy to become a python developer. Many python developers or students write codes without following good practices. Here are some best practices for python developers!

What is Scala?

Scala is a modern, multi-paradigm programming language designed to run on the Java Virtual Machine (JVM). It was created in 2003 by Martin Odersky and has gained popularity in recent years due to its functional programming capabilities, concise syntax, and powerful type system. Scala combines object-oriented and functional programming paradigms, allowing developers to write concise, expressive, highly scalable, and performant code. It is commonly used for building large-scale, distributed systems, web applications, and data processing applications. Scala also has interoperability with Java, allowing developers to use existing Java libraries and tools within Scala applications.

Also Read: 21 Steps to Get Started with Apache Spark using Scala for Software Development

What is Apache Spark?

Apache Spark is an open-source, distributed computing system designed for processing large-scale data types across clusters of computers. It was created in 2009 by Matei Zaharia and is now maintained by the Apache Software Foundation. Spark provides a powerful engine for processing data in parallel, with support for programming languages like Java, Scala, and Python languages. Spark’s core engine is built around a distributed data processing framework called Resilient Distributed Datasets (RDDs), allowing fast and fault-tolerant data processing. Spark also includes several higher-level APIs for data processing, including SQL, streaming, machine learning, and graph processing. It has become a popular big data processing and analysis tool in many industries.

Difference Between Python and Scala

CategoryPythonScala
PurposeGeneral-purpose language used for scripting, web development, data analysis, and moreGeneral-purpose language used for building large-scale distributed systems, web applications, and data processing
SyntaxEasy to read and learn, with a focus on code readability and simplicityConcise syntax with a strong focus on functional programming paradigms
TypingDynamically typed, with no requirement to declare variable typesStatically typed, with a powerful type system that catches errors at compile-time
PerformanceSlower than Scala due to interpreted nature and dynamic typingFaster than Python due to compiled nature and static typing
LibrariesLarge standard library and extensive third-party library supportSmaller standard library, but good support for Java libraries due to interoperability
ConcurrencySupports multi-threading but with limitations due to Global Interpreter Lock (GIL)Strong support for concurrency with the Actor model and lightweight threads
Learning CurveEasy to learn and a good language for beginnersSteep learning curve, with a strong emphasis on functional programming concepts

Python and Scala are important functional languages that help not only in Software Development but also in Data Science. Another Java programming language that is important for Data Science is Haddop. To know more about it, check out our article on Introduction to Hadoop Ecosystem!

Python vs. Scala: Purpose

  • Python is a general-purpose language used for various tasks, including scripting, web development, data analysis, scientific computing, machine learning, and more. Python’s versatility makes it popular for beginners and experienced developers.
  • Scala, on the other hand, is also a general-purpose language but is specifically designed for building large-scale distributed systems, web applications, and data processing. Scala’s focus on scalability, fault tolerance, and performance makes it a popular choice for big data processing and analysis and for building microservices and distributed systems.
  • While Python language is more widely used across different domains, Scala is especially suited for building complex distributed systems that require high performance and scalability.

Syntax Difference in Python and Scala

  • Python has a simple and readable syntax, focusing on code readability and simplicity. It uses indentation to define code blocks and has a minimalistic approach to coding style. Python code is easy to read and learn, making it an excellent language for beginners.
  • Scala, however, has a more complex syntax than Python, with a strong focus on functional programming paradigms. It uses a concise syntax that includes many symbols and operators, which can be challenging for beginners. Scala code also tends to be more verbose than Python code, although its functional programming features can help reduce boilerplate code.
  • While Python has a more straightforward syntax, Scala’s concise syntax with a strong focus on functional programming paradigms can help build complex distributed systems and data processing applications.
Python Syntax
Source: Gitconnected
Scala Syntax
Source: Scala Documentation

Python vs. Scala: Typing

  • Python is a dynamically typed language, meaning variable types are not required to be declared explicitly, and their type can change at runtime. This makes Python more flexible and easier for quick prototyping and scripting tasks. However, dynamic typing can also lead to hard-to-find bugs and slower performance.
  • Scala, on the other hand, is a statically typed language, meaning that variable types must be declared at compile-time errors and cannot be changed at runtime. This makes Scala more restrictive than Python but also catches compile-time errors, making it easier to write reliable and maintainable code. Static typing also enables Scala to compile-time errors and run faster than Python programming.
  • Overall, Python’s dynamic typing makes it easier to write code quickly for programmers, while Scala’s static typing makes it easier to write reliable and performant code. The choice between dynamic and static typing largely depends on the nature of the project and personal preference.
Scala Typing
Source:ResearchGate
Python Typing
Source: Digital Ocean

Python vs. Scala for Apache Spark: Performance

Scala and Python have different performance characteristics due to their implementation and design choices.

  • Python is an interpreted language, meaning the interpreter executes the code without requiring a compilation step. This makes Python very flexible, easy to use, and slower than compiled languages. Furthermore, Python’s dynamic typing and garbage collection can add overhead, leading to slower execution times.
  • Scala, on the other hand, is a compiled language that runs on the Java Virtual Machine (JVM). The Scala compiler optimizes the code and generates bytecode that runs on the JVM, which provides additional optimizations such as just-in-time (JIT) compilation. Additionally, Scala’s static typing and functional programming features make it easier to write code that can be optimized by the compiler, leading to faster execution times.
  • Scala is faster than Python due to its compiled nature, static typing, and support for functional programming paradigms. However, Python’s ease of use for programmers and flexibility make it popular for quick prototyping and scripting tasks where performance is not critical.

Scala vs. Python: Libraries

  • Python has a large standard library and an extensive ecosystem of third-party libraries, making it easy to find and use for various tasks, such as web development, data analysis, machine learning, and more. Many popular data analysis and machine learning libraries, such as NumPy, Pandas, and Scikit-learn, are in Python.
  • Scala’s standard library is smaller than Python’s, but Scala has excellent interoperability with Java, which means it can leverage the vast array of Java libraries available. This is particularly useful for building large-scale distributed systems and web applications, where Java libraries are often used. Scala also has its ecosystem of libraries, including Akka for building highly concurrent and distributed systems and Spark for large-scale data processing and machine learning.
  • While Python has a more extensive library ecosystem, Scala’s interoperability with Java and specialized libraries makes it well-suited for building large-scale distributed systems and data processing applications for programmers.

Python vs. Scala for Apache Spark

  • Python has a Global Interpreter Lock (GIL), meaning only one thread can execute Python bytecode simultaneously. This limits Python’s ability to take advantage of multiple CPU cores and can lead to performance bottlenecks for CPU-bound tasks. However, Python has several libraries, such as asyncio, that provide support for asynchronous programming, which can help mitigate the limitations of the GIL.
  • Scala, on the other hand, has excellent support for concurrency through its use of actors, independent entities that communicate by exchanging messages. The Akka library provides a powerful and flexible implementation of actors to build highly concurrent and distributed systems.
  • While Python’s GIL limits its ability to take full advantage of multiple CPU cores, its support for asynchronous programming can help mitigate this limitation. Scala’s use of actors and the Akka library makes it an excellent choice for building highly concurrent and distributed systems.

Python vs. Scala: Learning Curve

  • Thanks to its simple and readable syntax, and large and supportive community, Python has a relatively gentle learning curve. Python’s focus on code readability and simplicity makes it a great language for beginners and quick prototyping tasks. Additionally, Python’s extensive documentation and a large ecosystem of libraries and frameworks make it easy to find resources and tools to help learn the language.
  • Scala, on the other hand, has a steeper learning curve due to its more complex syntax and functional programming concepts. Scala requires a good understanding of programming paradigms such as functional and object-oriented programming, making it more challenging for beginners. However, once you have learned these concepts, Scala’s expressiveness and ability to handle complex data processing and distributed computing tasks make it a powerful language.
  • Python has a lower learning curve than Scala due to its simple syntax, large community, and extensive documentation. Scala requires a good understanding of programming concepts and may be more challenging for beginners. However, Scala’s expressive power and ability to handle complex tasks make it an attractive choice for those willing to invest in learning it.

Also Read: Journey from a Python Noob to a Kaggler on Python in Software Development

Benefits of Python

Python has several benefits that make it a popular language for a wide range of applications:

  • Easy to Learn: Python has a simple and easy-to-learn syntax, which makes it an excellent language for beginners and those who want to learn to program quickly.
  • Large Community and Extensive Library Ecosystem: Python has a large and supportive community and a vast ecosystem of libraries and frameworks for various tasks such as web development, data analysis, machine learning, and more.
  • Versatility: Python can be used for various applications, including web development, scientific computing, data analysis, machine learning, and more.
  • Rapid Prototyping: Python’s ease of use and versatility make it ideal for rapid prototyping, enabling developers to test ideas quickly and build proofs-of-concept.
  • Interpreted Language: Python is an interpreted language, meaning compilation is unnecessary, making it easy to use and flexible.

Who is Python Best Suited For?

Python suits many users, including beginners, scientists, data analysts, machine learning engineers, web developers, and so many more. Due to its versatility and ease of use, Python programming is an excellent choice for anyone looking to learn programming, prototype quickly, or build production-grade applications.

Most Asked Interview Questions on Apache Spark

Main Benefits of Scala: Who is Scala Best Suited For?

Scala has several benefits that make it a popular language for a wide range of applications:

  • Strongly Typed Language: Scala is a strongly typed language that provides type safety, which can help prevent bugs and improve code quality.
  • Functional Programming Capabilities: Scala is an available programming language that supports immutability, higher-order functions, and other functional programming concepts. This can help simplify code and make it more expressive.
  • Interoperability with Java: Scala is interoperable with Java, meaning that it can use Java libraries and frameworks. This makes Scala an excellent choice for developers familiar with Java who want to leverage their existing skills.
  • Excellent Support for Concurrency: Scala has excellent support for concurrency through its use of actors and the Akka library, making it a perfect choice for building highly concurrent and distributed systems.
  • Expressiveness: Scala’s expressive syntax and concise code make it an excellent choice for building complex applications.

Scala is best suited for experienced developers familiar with programming paradigms such as functional and object-oriented programming. Due to its strong typing, functional programming capabilities, and excellent support for concurrency, Scala is a perfect choice for building large-scale distributed systems and data engineering applications.

Additionally, Scala is an excellent choice for developers who want to leverage their existing Java skills and build highly concurrent and distributed applications.

Tutorials are beneficial because they offer a structured way to learn new skills, allowing individuals to access information at their own pace. They can also provide step-by-step guidance, interactive exercises, and the ability to ask questions. Overall, tutorials can be an effective way to learn and acquire new knowledge. Check out our exclusive tutorials on Python and Scala! If you want to check out small-scale projects in Spark, refer to this article here.

Conclusion

Python and Scala are popular programming languages for Apache Spark-based big data analytics. While Python engineering is easy to learn, flexible, and has a vast library of data engineering tools and frameworks, Scala is a strongly-typed language that can offer better performance and scalability in large-scale distributed systems. Ultimately, the choice between Python and Scala for Apache Spark depends on the specific needs and requirements of the project, as well as the preferences and expertise of the data scientists and engineers involved. Therefore, it is essential to carefully consider the pros and cons of each language and choose the one that best fits your use case.

Looking to become an expert in Apache Spark-based big data analytics? Look no further than Analytics Vidhya’s comprehensive courses! With our courses, you can equip yourself with the skills and knowledge needed to master Apache Spark and make the most of big data analytics. Whether you’re a beginner just starting or an experienced data professional looking to level up your skills, we have courses tailored to meet your needs. With various interactive and engaging course materials, expert instructors, and hands-on projects to apply your learning, Analytics Vidhya is the perfect place to take your Apache Spark-based big data analytics skills to the next level. So why wait? Enroll in one of our courses today and start your journey toward becoming an Apache Spark expert!

Frequently Asked Questions

Q1. Which is better? Python or Scala?

A. Choosing between Python and Scala depends on the use case and personal preferences. Python is popular for being user-friendly, its simplicity, vast libraries, and versatility, while Scala is powerful for building distributed systems with a strong type of system.

Q2. Is Scala faster than Python?

A. Scala programming language can be faster than Python for certain use cases, especially those that require high-performance computing, concurrency, and parallelism. However, Python’s vast array of libraries and frameworks can make it more convenient and efficient for certain tasks, such as data engineering and machine learning.

Q3. What is the best language to use for Apache Spark?

A. Scala is the best language to use for Apache Spark due to its concise syntax, strong type system, and functional programming features, which allow for efficient and scalable distributed computing. However, Python is also a popular language for Spark due to its ease of use and extensive libraries.

Q4.Can you use Python in Apache Spark?

A. Yes, Python is useable for Apache Spark through the PySpark API, which provides a Python interface to Spark. PySpark allows users to write Spark applications in Python programming, including Spark SQL, machine learning, and graph processing. While Scala is the primary language for Spark, PySpark has become increasingly popular due to Python’s ease of use and its vast array of libraries.

Q5. What are data structures in Apache spark?

A. Data structures in Apache Spark are collections of data that are organized in a specific way to allow for efficient processing. These include Resilient Distributed Datasets (RDDs), data frames, Datasets, and Graphs. These data structures provide a powerful set of tools for processing and analyzing large-scale data sets efficiently and in parallel across a cluster of nodes.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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