Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique (original) (raw)

Clustering of large datasets using Hadoop Ecosystem

In today's rapid change of world along with the advancement of technology, the amount of data being generated and used is very high. The rate of data production is very rapid and is not easy to measure. The existing data processing techniques are not capable enough to process data which are so large. K-means is a traditional clustering method which is easy to implement but it converges to local minima from starting position and is sensitive to initial clusters. Hadoop or the Hadoop Distributed File System (HDFS) is a distributed file system which is highly fault tolerant and can be implemented on low cost hardware. It provides complete access to data for any operation and is suitable for applications that needs large data sets. Hadoop is used for parallel processing of large data set in less time.

A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP

HM Publishers, 2017

We are living in an excessively developed technical era where internet is becoming fundamental need for all individuals. Today, our social, personal as well as professional life are revolving around world wide web. Thus, giving birth to Big Data at an incredible momentum. Traditional management tools and frameworks are proved, unfair while dealing with Big Data. This paper emphasize on number of review papers which acquainted us with the milieu of big data as well as emerging technologies helping Big Data. We also put light on the challenges increasing from the use of big data. We try to uncover correct approach to retrieve valuable information from the pile of Big Data.

A Distribution of Nodes in Big Data using Hadoop Open Source System

International Journal of Innovative Technology and Exploring Engineering, 2020

Apache Hadoop is an free open source Java framework under Apache Software Foundation. It provides storage of large amount of data efficiently with low costing. Hadoop has two main core components one is HDFS (Hadoop Distributed File System) and second Map Reduce. It is basically a file system and has capability of high fault-tolerant and while deploying supports less cost hardware. It. provides the high speed admittance to the relevance data. The Hadoop architecture is based on cluster, which consist of two nodes named as Data -Node and Name-Node which perform the internal activity known as heart beat to process data storage on distributed file system and Map reducing is performed internally to show the clustering of distributed data on localhost of ssh serverwebsite. Large quantity of data is needed to store in distributed file structure, for this Hadoop has played important role. Maintaining the large volume storage, making data duplicity for providing security and recovery of big...

A Review Paper on Big Data and Hadoop

The term 'Big Data' describes innovative techniques and technologies to capture, store, distribute, manage and analyse petabyte-or larger-sized datasets with high-velocity and different structures. Big data can be structured, unstructured or semi-structured, resulting in incapability of conventional data management methods. Data is generated from various different sources and can arrive in the system at various rates. In order to process these large amounts of data in an inexpensive and efficient way, parallelism is used. Big Data is a data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. Hadoop is the core platform for structuring Big Data, and solves the problem of making it useful for analytics purposes. Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance.

Inception of Big Data with Hadoop and Map Reduce

Big Data is a term used to describe large collections of data that may be unstructured, and grow so large and quickly that it is difficult to manage with regular database or statistical tools. Therefore, Big Data solutions based on Hadoop and other analytics software are becoming more and more relevant.This massive amount of data can be analyzed by using Hadoop. Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. The technologies used by big data application to handle the enormous data are Hadoop, Map Reduce, Apache Hive, No SQL and HPCC. In this paper I suggest various methods for furnishing to the problems in hand through Map Reduce framework over Hadoop Distributed File System (HDFS). Map Reduce is a Minimization technique which makes use of file indexing with mapping, sorting, shuffling and finally reducing. Map Reduce techniques have been studied in this paper which is implemented for Big Data analysis using HDFS.

Hadoop : Big Data Analytics Solution

2016

In the last 2 decades, there has been tremendous expansion of digital data related to almost every domain of the World. Be it astronomy, military, health care or education, digital data is rapidly increasing. Traditional data processing tools such as RDBMS fail for such large volumes of data. Hadoop has been developed as a solution to this problem and addresses the 4 main challenges of Big Data i.e. (4V) Volume, Velocity, Variety and Variability. Hadoop is an open-source platform under Apache Foundation for providing flexible, reliable, scalable distributed computing. Hadoop Distributed File System, HDFS provides storage for large data sets using commodity computers, providing automated splits and distribution of the files onto different machines. Yet Another Resource Negotiator, YARN is a cluster management technology on top of HDFS for managing the jobs internally and automatically. YARN supports multiple processing environments for processing of data such as, Pig, Hive, Spark, Gi...