Comparing Apache Spark and Map Reduce with Performance Analysis using K-Means

Performance Analysis of Spark using k-means

Big Data has long been the topic of fascination for Computer Science enthusiasts around the world, and has gained even more prominence in the recent times with the continuous explosion of data resulting from the likes of social media and the quest for tech giants to gain access to deeper analysis of their data. This paper discusses two of the comparison of Hadoop Map Reduce and the recently int...

Fast Data Clustering and Outlier Detection Using K-means Clustering on Apache Spark

The components forming the information society nowadays are seen in all areas of our lives. As computers have a great deal of importance in our lives, the amount of information has begun to gather meaningful and specific qualities. Not only the amount of information is increased, but also the speed of access to information has increased. Large data is the transformed form of all data recovered ...

Getting Started with Apache Spark

Reproducible Experiments for Comparing Apache Flink and Apache Spark on Public Clouds

Big data processing is a hot topic in today’s computer science world. There is a significant demand for analysing big data to satisfy many requirements of many industries. Emergence of the Kappa architecture created a strong requirement for a highly capable and efficient data processing engine. Therefore data processing engines such as Apache Flink and Apache Spark emerged in open source world ...

CMS Analysis and Data Reduction with Apache Spark

Experimental Particle Physics has been at the forefront of analyzing the world’s largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems for distributed data processing, collectively called ”Big Data” technologies have emerged from industry and open source projects to support th...