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Reading list

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

Spark vs Presto: A Comprehensive Comparison

Pankaj Singh
Pankaj Singh Last Updated : 28 Dec, 2023
7 min read

Introduction

In big data processing and analytics, choosing the right tool is paramount for efficiently extracting meaningful insights from vast datasets. Two popular frameworks that have gained significant traction in the industry are Apache Spark and Presto. Both are designed to handle large-scale data processing efficiently, yet they have distinct features and use cases. As organizations grapple with the complexities of handling massive volumes of data, a comprehensive understanding of Spark and Presto’s nuances and distinctive features becomes essential. In this article, we will compare Spark vs Presto, exploring their performance and scalability, data processing capabilities, ecosystem, integration, and use cases and applications.

Spark vs Presto

Table of contents

Spark vs Presto: Understanding the Basics

Before we dive into the Spark vs Presto comparison, let’s first understand the basics of Spark and Presto. Spark is an open-source, distributed computing system that provides a unified analytics engine for big data processing. It offers support for various programming languages, including Java, Scala, Python, and R, making it accessible to many developers. On the other hand, Presto is a distributed SQL query engine designed for interactive analytics at scale. Standard SQL syntax allows users to query large datasets across multiple data sources.

Importance of Choosing the Right Data Processing Framework

Choosing the right data processing framework is crucial for organizations as it directly impacts their ability to process and analyze data efficiently. A well-suited framework can significantly enhance performance, scalability, and overall productivity. Therefore, it is essential to carefully evaluate the strengths and weaknesses of each framework before making a decision.

Overview of Spark and Presto

Spark and Presto are powerful frameworks that excel in different areas of data processing. Spark is known for its exceptional performance and scalability, making it ideal for big data processing and analytics. It supports batch processing, real-time stream processing, as well as machine learning and graph processing. On the other hand, Presto shines in interactive analytics and ad-hoc queries, allowing users to explore and analyze data in real-time. It also offers federated querying capabilities, enabling users to query data from multiple sources seamlessly.

Spark vs Presto

Spark vs Presto: Performance and Scalability

Regarding performance and scalability, both Spark and Presto have their strengths. Spark boasts impressive language support, providing built-in support for Java, Scala, Python, and R. This wide range of programming languages allows developers to leverage existing skills and choose the language that best suits their needs. Spark’s distributed computing capabilities also enable it to process large datasets across a cluster of machines efficiently. Thanks to its in-memory computing capabilities, it excels in data processing speed.

On the other hand, Presto also offers robust language support, including SQL, making it accessible to a broader audience. Its distributed computing capabilities allow it to handle massive datasets and execute queries in parallel. While Presto may not match Spark’s data processing speed due to its disk-based processing approach, it compensates with its ability to handle complex queries efficiently.

Comparison of Performance and Scalability

Both Spark and Presto have unique advantages in terms of performance and scalability. Spark’s in-memory computing capabilities and support for various programming languages make it a powerful choice for big data processing. On the other hand, Presto’s ability to handle complex queries and its distributed SQL query engine make it an excellent option for interactive analytics and ad-hoc queries.

Spark vs Presto

Spark vs Presto: Data Processing Capabilities

Moving on to data processing capabilities, Spark and Presto offer various features to handle different data processing tasks. Spark’s batch processing capabilities allow users to process large volumes of data in parallel, making it suitable for tasks such as ETL (Extract, Transform, Load) and data warehousing. It also excels in real-time stream processing, enabling users to process and analyze streaming data. Furthermore, Spark provides robust machine learning and graph processing support, making it a versatile framework for various data processing tasks.

On the other hand, Presto’s strength lies in querying large datasets across multiple data sources. It allows users to write SQL queries to retrieve data from various databases and file systems, providing a unified view of the data. Presto also offers interactive analytics capabilities, allowing users to explore and analyze data in real-time. Additionally, its federated querying feature enables users to query data from different sources seamlessly, eliminating the need for data duplication.

Comparison of Data Processing Capabilities

When it comes to data processing capabilities, Spark and Presto offer distinct features that cater to different use cases. Spark’s batch processing, real-time stream processing, and machine learning capabilities make it a comprehensive framework for various data processing tasks. On the other hand, Presto’s focus on querying large datasets, interactive analytics, and federated querying makes it an excellent choice for ad-hoc queries and data exploration.

Spark vs Presto

Spark vs Presto: Ecosystem and Integration

A data processing framework’s ecosystem and integration capabilities are vital in its adoption and usability. Spark offers seamless integration with Hadoop and other big data tools, allowing users to leverage existing infrastructure and tools. It also supports various data sources and file formats, making it easy to ingest and process data from different systems. Additionally, Spark integrates well with popular machine-learning libraries, enabling users to perform advanced analytics and machine-learning tasks.

On the other hand, Presto offers integration with various data sources, including databases, file systems, and cloud storage services. It supports different file formats, making it flexible in handling diverse data types. Furthermore, Presto integrates with other data processing tools, allowing users to combine the strengths of different frameworks and create a unified data processing pipeline.

Comparison of Ecosystem and Integration

Spark and Presto offer robust ecosystem and integration capabilities, allowing users to integrate seamlessly with existing tools and systems. Spark’s integration with Hadoop and other big data tools and its support for machine learning libraries make it a comprehensive framework for data processing. On the other hand, Presto’s integration with various data sources and its ability to work with different file formats provide flexibility and versatility in data processing.

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Spark vs Presto: Use Cases and Applications

Understanding Spark and Presto’s use cases and applications is essential in determining which framework best suits specific business needs. Spark finds its applications in big data processing and analytics, where its performance and scalability shine. It is also widely used for real-time stream processing, enabling businesses to analyze streaming data in real-time. Spark’s machine learning and AI capabilities also make it a popular choice for advanced analytics tasks.

On the other hand, Presto’s use cases revolve around interactive analytics and ad-hoc queries. Its ability to query large datasets across multiple sources in real-time makes it ideal for data exploration and data science tasks. Furthermore, Presto’s federated querying capabilities enable businesses to perform cross-source analysis without data duplication.

Comparison of Use Cases and Applications

Regarding use cases and applications, Spark and Presto cater to different needs. Spark’s strengths lie in big data processing, real-time stream processing, and machine learning, making it suitable for various analytics tasks. On the other hand, Presto’s focus on interactive analytics, ad-hoc queries, and federated querying makes it an excellent choice for data exploration and real-time analysis across multiple sources.

Spark vs Presto: The Tabular Difference 

Presto and Apache Spark are distributed computing frameworks designed for processing large-scale data, but they have different architectures, use cases, and features. Here’s a tabular difference between Presto and Apache Spark:

Feature Presto Spark
Primary Use Case SQL Query Engine for Big Data Analytics General-purpose distributed data processing
Programming Language SQL Scala, Java, Python, and R
Data Processing Model SQL queries for structured data Resilient Distributed Datasets (RDDs) for both structured and unstructured data
Distributed Processing Masterless (Coordinator and Workers) Master-slave architecture (Driver and Executors)
Ease of Use SQL familiarity, suitable for analysts More developer-friendly APIs and libraries
Integration with Hadoop Can query data in HDFS Tight integration with Hadoop ecosystem
Batch and Stream Processing Batch processing primarily, limited streaming capabilities Unified batch and stream processing model
Data Sources Supports a variety of data sources including Hive, MySQL, PostgreSQL, etc. Extensive connectors for various data sources
Performance High-performance for SQL queries Generally good performance; optimization through RDDs
Caching Supports caching for query optimization Caching through RDDs and DataFrames
Community Support Active community support Large and active open-source community
Ecosystem Limited ecosystem compared to Spark Rich ecosystem with libraries like MLlib, Spark SQL, GraphX, etc.
Fault Tolerance Supports fault tolerance through task retries Built-in fault tolerance with lineage information and data replication
Storage Reads data directly from storage Uses distributed file system (e.g., HDFS) or other storage systems

Conclusion

The right choice in the Spark vs Presto showdown depends on your use case and performance requirements. Spark may be your best bet if you’re looking for a unified platform focusing on machine learning and stream processing. On the other hand, if interactive querying and exceptional query performance are your priorities, Presto shines in these areas.

Ultimately, understanding your data processing needs, considering the learning curve, and evaluating the specific features of each tool will guide you toward making an informed decision. Whether you opt for Apache Spark’s versatility or Presto’s query prowess, both platforms play pivotal roles in the big data landscape, offering powerful solutions for diverse analytical challenges.

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Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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