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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

Apache Spark Vs. Hadoop MapReduce – Top 7 Differences

Devashree
Devashree Last Updated : 22 Jun, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Apache Spark was released in 2014. Earlier to it, Hadoop MapReduce was the main focus for processing large data with no competitors. While Spark is the new and widely used technology, MapReduce still has its place in specific applications. Let’s take a deeper look at two popular tools for big data, MapReduce and Spark, and compare them to decide which is best in specific circumstances.

Organizations are receiving big data at an overwhelming pace due to smart devices and the increased use of internet technologies. Hence, it is necessary to handle it for efficient data analysis becomes crucial. While several technologies are used to easily process big data, two of the most popular are Hadoop MapReduce and Apache Spark.

What is Apache Spark?

Apache Spark is an open-source distributed system for handling Big Data workloads. It improves query processing performance on varying data sizes by using efficient query execution and in-memory caching.

What is Hadoop MapReduce?

MapReduce is a Java-based distributed computing programming model within the Hadoop framework. It is used to access large amounts of data in the Hadoop File System (HDFS). The Mapper and Reducer are two jobs performed in MapReduce programming. Mapper is responsible for sorting all the available data, while Reducer is in charge of aggregating it and turning it into smaller chunks.

Top 7 differences between Apache Spark and Hadoop MapReduce

Although both the tools handle big data, they are not the same. Let us explore the main differences between them based on their features.

Top 7 differences between Apache Spark and Hadoop MapReduce

1. Ease of Use

Apache Spark contains APIs for Scala, Java, and Python and Spark SQL for SQL users. Apache Spark offers basic building blocks that allow users to easily develop user-defined functions. You can use Apache Spark in interactive mode when running commands to get an instant response.

On the other hand, Hadoop MapReduce was developed in Java and is difficult to program. There is no interactive mode with Hadoop MapReduce, unlike Apache Spark. However, Hive provides a command-line interface. It is used for issuing commands in successive lines of text.

Spark is more user-friendly and features an interactive mode. On the other hand, Hadoop MapReduce is more difficult to program. However, there are other tools available for help.

2. Data Processing

Apache Spark can perform many other tasks than just data processing. Apache Spark can handle graphs and has its own Machine Learning Library – MLlib. Because of its great performance, Apache Spark can be used for both – batch and near real-time processing. Apache Spark is a flexible, multiple-use platform that can be used to handle all activities rather than dividing them over many platforms.

MapReduce is an ideal solution for batch processing in Hadoop. If you want a real-time solution, you can use Impala or Apache Storm, and for graph processing, you can use Apache Giraph. Earlier, MapReduce used Apache Mahout for Machine Learning tasks, but Mahout was discontinued when Spark came in.

Spark is great because it provides a single data framework for all your data processing requirements. Spark is capable of handling any data processing requirement. On the other hand, MapReduce is less flexible than Spark when it comes to performing a variety of data processing jobs, while MapReduce could be one of the best available batch processing tools.

3. Performance

Apache Spark is very much popular for its speed. It runs 100 times faster in memory and ten times faster on disk than Hadoop MapReduce since it processes data in memory (RAM). At the same time, Hadoop MapReduce has to persist data back to the disk after every Map or Reduce action.

Spark takes a lot of RAM to operate effectively. Spark saves processes to memory and keeps them there if different instructions are not given. If Spark is used with other resource-demanding services, its performance may be hampered notably. Additionally, Spark’s performance will suffer if the data sources are too large to fit fully in memory.

Although MapReduce does not provide data caching, other services can assist it with little to no performance downturn since it terminates its operations the moment they are complete.

MapReduce and Spark both have benefits when it comes to performance. Spark is easily the best choice for your big data requirements if your data get accommodated with the amount of memory space you have or if you have a dedicated cluster. On the other hand, MapReduce is a better solution if you have a large volume of data that won’t fit neatly into memory, and you need your data framework for coordinating with other services.

4. Failure Recovery

MapReduce is more suitable for recovery after failure than Spark since it uses hard drives instead of RAM. When Spark comes back online after crashing in the middle of a data processing activity, it will have to start all over from the beginning. This process requires more time.

If MapReduce fails while doing a job, it will resume where it left off when it restarts. Because MapReduce is based on a hard disk, it can maintain its position if it fails in the middle of a job.
Both Spark and Hadoop MapReduce have high failure tolerance, but Hadoop MapReduce is slightly more tolerant.

5. Security

Apache Spark’s security is set to “OFF” by default, leaving you vulnerable to threats. Spark supports RPC channel authentication through a shared secret. Event logging is a feature of Spark, and Web UIs can be protected using java extensions. Furthermore, because Spark can operate on YARN and use HDFS, it can benefit from Kerberos authentication, HDFS file permissions, and encryption between nodes.

Hadoop MapReduce can make use of all Hadoop security capabilities and can integrate with other Hadoop security projects like Knox Gateway and Apache Sentry. Spark security is still in its early stages compared to MapReduce, which has more security features and projects.

6. Cost

While Spark and MapReduce are open-source solutions, you will still need to spend more on computers and staff. Spark and MapReduce can both run on commodity systems and in the cloud.

MapReduce requires a larger number of devices with higher disk space but little RAM capacity. On the other hand, Spark requires fewer devices with standard disk space but definitely higher RAM capacity. An adequate capacity is necessary to hold all the available data. Because disk space is less expensive than RAM space, MapReduce is the less expensive alternative.

7. Scheduler

Apache Spark can schedule all its tasks by itself, while Hadoop MapReduce requires external schedulers like Oozie.

Conclusion

This article briefly explored the key differences between two popular big data frameworks – Apache Spark and Hadoop MapReduce.

Apache Spark’s capabilities for data scientists are amazing; still, a lot of improvement is possible for the tool’s growth. Despite being older and slower than Spark, MapReduce is still a popular choice for batch processing. MapReduce can also be used for dealing with large data sets that don’t fit in memory.

As discussed earlier, there are some notable benefits of MapReduce over Spark. You should select a framework that best matches your requirements. Spark is probably the better choice if you need to do a little bit of everything, while MapReduce is better if you require a batch processing engine that can handle big data.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Devashree
Devashree

Devashree has an M.Eng degree in Information Technology from Germany and a Data Science background. As an Engineer, she enjoys working with numbers and uncovering hidden insights in diverse datasets from different sectors to build beautiful visualizations to try and solve interesting real-world machine learning problems.

In her spare time, she loves to cook, read & write, discover new Python-Machine Learning libraries or participate in coding competitions.

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