Skip to content

Implemented graph algorithms using Map-Reduce, Spark/Scala, Apache Pig, and Spark SQL. Leveraged distributed computing with Apache Spark, GraphX, and Pregel for efficient processing of large-scale graphs, improving scalability and reducing computation time.

Notifications You must be signed in to change notification settings

ramidimeghanareddy/CloudComputing_and_BigData

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

32 Commits
 
 
 
 
 
 

Repository files navigation

Cloud Computing and Big Data

Meghana Ramidi

Follow Github GitHub stars

Project1: A Simple Map-Reduce Program

The purpose of this project is to develop a simple Map-Reduce program on Hadoop that analyzes data from Twitter.

Project2: Matrix Multiplication using Map-Reduce

The purpose of this project is to develop a Map-Reduce program on Hadoop to multiply two sparse matrices.

Project3: Graph Processing using Map-Reduce

The purpose of this project is to develop a graph analysis program using Map-Reduce.

Project4: KMeans Clustering on Spark

The purpose of this project is to develop a data analysis program using Apache Spark.

Project5: Graph Processing on Spark

The purpose of this project is to develop a graph analysis program using Apache Spark.

Project6: Graph Analysis using Pig

The purpose of this project is to develop a simple graph analysis program using Apache Pig.

Project7: Graph Analysis using Spark SQL

The purpose of this project is to develop a simple analysis program for graph analysis using Spark SQL.

Project8: Graph Partition using GraphX

The purpose of this project is to do graph partition program using Pregel on Spark GraphX.

About

Implemented graph algorithms using Map-Reduce, Spark/Scala, Apache Pig, and Spark SQL. Leveraged distributed computing with Apache Spark, GraphX, and Pregel for efficient processing of large-scale graphs, improving scalability and reducing computation time.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published