EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Head to Head Differences Tutorial Hadoop vs Spark
Secondary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • Scikit Learn vs TensorFlow
    • Azure Functions vs Logic Apps
    • Azure Data Factory vs Databricks
    • SHA1 vs MD5
    • Azure SQL Database vs Managed Instance
    • Azure SQL Database vs SQL Server
    • PostgreSQL vs MySQL
    • PostgreSQL vs MySQL Benchmark
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • T-SQL vs SQL
    • PostgreSQL vs MariaDB
    • Spark vs Impala
    • Datadog vs Splunk
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Predictive Analytics vs Business Intelligence
    • AI vs Machine Learning vs Deep Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • SQL vs SQLite
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Hadoop vs Spark

By Priya PedamkarPriya Pedamkar

Hadoop vs Spark

Difference Between Hadoop vs Spark

Hadoop is an open-source framework that allows to store and process big data, in a distributed environment across clusters of computers. Hadoop is designed to scale up from a single server to thousands of machines, where every machine is offering local computation and storage. Spark is an open-source cluster computing designed for fast computation. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. The main feature of Spark is in-memory cluster computing that increases the speed of an application.

Hadoop

  • Hadoop is a registered trademark of the Apache software foundation. It utilizes a simple programming model to perform the required operation among clusters. All modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be dealt with by the framework.
  • It runs the application using the MapReduce algorithm, where data is processed in parallel on different CPU nodes. In other words, the Hadoop framework is capable enough to develop applications, which are further capable of running on clusters of computers and they could perform a complete statistical analysis for a huge amount of data.
  • The core of Hadoop consists of a storage part, which is known as Hadoop Distributed File System and a processing part called the MapReduce programming model. Hadoop basically split files into the large blocks and distribute them across the clusters, transfer package code into nodes to process data in parallel.
  • This approach dataset to be processed faster and more efficiently. Other Hadoop modules are Hadoop common, which is a bunch of Java libraries and utilities returned by Hadoop modules. These libraries provide a file system and operating system level abstraction, also contain required Java files and scripts to start Hadoop. Hadoop Yarn is also a module, which is being used for job scheduling and cluster resource management.

Spark

  • Spark was built on the top of Hadoop MapReduce module and it extends the MapReduce model to efficiently use more type of computations which include Interactive Queries and Stream Processing. Spark was introduced by the Apache software foundation, to speed up the Hadoop computational computing software process.
  • Spark has its own cluster management and is not a modified version of Hadoop. Spark utilizes Hadoop in two ways – one is storage and second is processing. Since cluster management is arriving from Spark itself, it uses Hadoop for storage purposes only.
  • Spark is one of the Hadoop’s subprojects which was developed in 2009, and later it became open source under a BSD license. It has lots of wonderful features, by modifying certain modules and incorporating new modules. It helps run an application in a Hadoop cluster, multiple times faster in memory.
  • This is made possible by reducing the number of read/write operations to disk. It stores the intermediate processing data in memory, saving read/write operations. Spark also provides built-in APIs in Java, Python or Scala. Thus, one can write applications in multiple ways. Spark not only provides a Map and Reduce strategy but also support SQL queries, Streaming data, Machine learning and Graph Algorithms.

Head to Head Comparison Between Hadoop vs Spark (Infographics)

Below is the top 8 difference between Hadoop and Spark:

Hadoop vs Spark Infographics

Key Differences between Hadoop and Spark

Both Hadoop vs Spark are popular choices in the market; let us discuss some of the major difference between Hadoop and Spark:

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

  1. Hadoop is an open source framework which uses a MapReduce algorithm whereas Spark is lightning fast cluster computing technology, which extends the MapReduce model to efficiently use with more type of computations.
  2. Hadoop’s MapReduce model reads and writes from a disk, thus slow down the processing speed whereas Spark reduces the number of read/write cycles to disk and store intermediate data in-memory, hence faster-processing speed.
  3. Hadoop requires developers to hand code each and every operation whereas Spark is easy to program with RDD – Resilient Distributed Dataset.
  4. Hadoop MapReduce model provides a batch engine, hence dependent on different engines for other requirements whereas Spark performs batch, interactive, Machine Learning and Streaming all in the same cluster.
  5. Hadoop is designed to handle batch processing efficiently whereas Spark is designed to handle real-time data efficiently.
  6. Hadoop is a high latency computing framework, which does not have an interactive mode whereas Spark is a low latency computing and can process data interactively.
  7. With Hadoop MapReduce, a developer can only process data in batch mode only whereas Spark can process real-time data through Spark Streaming.
  8. Hadoop is designed to handle faults and failures, it is naturally resilient toward faults, hence a highly fault-tolerant system whereas, with Spark, RDD allows recovery of partitions on failed nodes.
  9. Hadoop needs an external job scheduler for example – Oozie to schedule complex flows whereas Spark has in-memory computation, so it has its own flow scheduler.
  10. Hadoop is a cheaper option available while comparing it in terms of cost whereas Spark requires a lot of RAM to run in-memory, thus increasing the cluster and hence cost.

Hadoop and Spark Comparison Table

The primary Comparison between Hadoop and Spark are discussed below

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,650 ratings)
Basis Of Comparison Between Hadoop vs Spark

Hadoop

Spark

Category Basic Data processing engine Data analytics engine
Usage Batch processing with a huge volume of data Process real-time data, from real-time events like Twitter, Facebook
Latency High latency computing Low latency computing
Data Process data in batch mode Can process interactively
Ease of Use Hadoop’s MapReduce model is complex, need to handle low-level APIs Easier to use, abstraction enables a user to process data using high-level operators
Scheduler External job scheduler is required In-memory computation, no external scheduler required
Security Highly secure Less secure as compare to Hadoop
Cost Less costly since MapReduce model provide a cheaper strategy Costlier than Hadoop since it has an in-memory solution

 Conclusion

Hadoop MapReduce allows parallel processing of massive amounts of data. It breaks a large chunk into smaller ones to be processed separately on different data nodes and automatically gathers the results across the multiple nodes to return a single result. In case the resulting dataset is larger than available RAM, Hadoop MapReduce may outperform Spark.

Spark, on the other hand, is easier to use than Hadoop, as it comes with user-friendly APIs for Scala (its native language), Java, Python, and Spark SQL. Since Spark provides a way to perform streaming, batch processing, and machine learning in the same cluster, users find it easy to simplify their infrastructure for data processing.

Final decision to choose between Hadoop vs Spark depends on the basic parameter – requirement. Apache Spark is much more advanced cluster computing engine than Hadoop’s MapReduce, since it can handle any type of requirement i.e. batch, interactive, iterative, streaming etc. while Hadoop limits to batch processing only. At the same time, Spark is costlier than Hadoop with its in-memory feature, which eventually requires a lot of RAM. At the end of the day, it all depends on a business’s budget and functional requirement. I hope now you must have got a fairer idea of both Hadoop vs Spark.

Recommended Articles

This has a been a guide to the top difference between Hadoop vs Spark. Here we also discuss Hadoop vs Spark head to head comparison, key differences along with infographics and comparison table. You may also have a look at the following Hadoop vs Spark articles to learn more.

  1. Data Warehouse vs Hadoop
  2. Splunk vs Spark
  3. Hadoop vs Cassandra – 17 Awesome Differences
  4. Pig vs Spark – Which One Is Better
  5. Hadoop vs SQL Performance: Difference
Popular Course in this category
Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)
  20 Online Courses |  14 Hands-on Projects |  135+ Hours |  Verifiable Certificate of Completion
4.5
Price

View Course

Related Courses

Data Scientist Training (85 Courses, 67+ Projects)4.9
Tableau Training (8 Courses, 8+ Projects)4.8
Azure Training (6 Courses, 5 Projects, 4 Quizzes)4.7
Data Visualization Training (15 Courses, 5+ Projects)4.7
All in One Data Science Bundle (360+ Courses, 50+ projects)4.7
37 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more