Big Data Processing Using Spark in Cloud (original) (raw)

A Close-Up View About Spark in Big Data Jurisdiction

The Big data is the name used ubiquitously now a day in distributed paradigm on the web. As the name point out it is the collection of sets of very large amounts of data in pet bytes, Exabyte etc. related systems as well as the algorithms used to analyze this enormous data. Hadoop technology as a big data processing technology has proven to be the go to solution for processing enormous data sets. MapReduce is a conspicuous solution for computations, which requirement one-pass to complete, but not exact efficient for use cases that need multi-pass for computations and algorithms. The Job output data between every stage has to be stored in the file system before the next stage can begin. Consequently, this method is slow, disk Input/output operations and due to replication. Additionally, Hadoop ecosystem doesn't have every component to ending a big data use case. Suppose we want to do an iterative job, you would have to stitch together a sequence of MapReduce jobs and execute them in sequence. Every this job has high-latency, and each depends upon the completion of the previous stage. Apache Spark is one of the most widely used open source processing engines for big data, with wealthy language-integrated APIs and an extensive range of libraries. Apache Spark is a usual framework for distributed computing that offers high performance for both batch and interactive processing. In this paper, we aimed to demonstrate a close-up view about Apache Spark and its features and working with Spark using Hadoop. We are in a nutshell discussing about the Resilient Distributed Datasets (RDD), RDD operations, features, and limitation. Spark can be used along with MapReduce in the same Hadoop cluster or can be used lonely as a processing framework. In the last comparative analysis between Spark and Hadoop and MapReduce in this paper.

Big Data and Apache Spark: A Review

— Big Data is currently a very burning topic in the fields of Computer Science and Business Intelligence, and with such a scenario at our doorstep, a humungous amount of information waits to be documented properly with emphasis on the market. By market, we mean the current technologies in use, the current prevalent tools, and the companies playing an imperative role in taming the data with such a colossal outreach.

Big data analytics on Apache Spark

International Journal of Data Science and Analytics, 2016

Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming and structured data processing. It is a general-purpose cluster computing framework with language-integrated APIs in Scala, Java, Python and R. As a rapidly evolving open source project, with an increasing number of contributors from both academia and industry, it is difficult for researchers to comprehend the full body of development and research behind Apache Spark, especially those who are beginners in this area. In this paper, we present a technical review on big data analytics using Apache Spark. This review focuses on the key components, abstractions and features of Apache Spark. More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing. In addition, we highlight some research and development directions on Apache Spark for big data analytics.

Big Data Analysis: Apache Spark Perspective

Big Data have gained enormous attention in recent years. Analyzing big data is very common requirement today and such requirements become nightmare when analyzing of bulk data source such as twitter twits are done, it is really a big challenge to analyze the bulk amount of twits to get relevance and different patterns of information on timely manner. This paper will explore the concept of Big Data Analysis and recognize some meaningful information from some sample big data source, such as Twitter twits, using one of industries emerging tool, known as Spark by Apache

Distributed big data analysis using Spark parallel data processing

Bulletin of Electrical Engineering and Informatics, 2022

Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.

Big Data, Cloud and Applications

Communications in computer and information science, 2018

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

A REVIEW: MAPREDUCE AND SPARK FOR BIG DATA ANALYTICS

In this paper we discuss the various challenges of Big Data and problem arises due to continuous explosion of data resulting from the likes of social media and other online sources to gain access to deeper analysis of their data. This paper discusses two of the comparison of Hadoop Map Reduce and the recently introduced Apache Spark – both of which provide a processing model for analyzing big data. Although both of these options are based on the concept of Big Data, their performance varies significantly based on the use case under implementation. Data growing at very high speed and is having very large volume. Presently, to assemble the large volume of dataset at lesser cost, storage technology and data collection has made it possible for any organization.

Large Scale Distributed Data Science from scratch using Apache Spark 2.0

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion, 2017

Apache Spark is an open-source cluster computing framework. It has emerged as the next generation big data processing engine, overtaking Hadoop MapReduce which helped ignite the big data revolution. Spark maintains MapReduce's linear scalability and fault tolerance, but extends it in a few important ways: it is much faster (100 times faster for certain applications), much easier to program in due to its rich APIs in Python, Java, Scala, SQL and R (MapReduce has 2 core calls) , and its core data abstraction, the distributed data frame. In addition, it goes far beyond batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming, machine learning, and graph processing. With massive amounts of computational power, deep learning has been shown to produce state-of-the-art results on various tasks in different fields like computer vision, automatic speech recognition, natural language processing and online advertising targeting. Thanks to the open-source frameworks, e.g. Torch, Theano, Caffe, MxNet, Keras and TensorFlow, we can build deep learning model in a much easier way. Among all these framework, TensorFlow is probably the most popular open source deep learning library. TensorFlow 1.0 was released recently, which provide a more stable, flexible and powerful computation tool for numerical computation using data flow graphs. Keras is a highlevel neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. This tutorial will provide an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. It is divided into three parts: the first part will cover fundamental Spark concepts, including Spark Core, functional programming ala map-reduce, data frames, the Spark Shell, Spark Streaming, Spark SQL, MLlib, and more; the second part will focus on hands-on algorithmic design and development with Spark (developing algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as pagerank/shortest path, gradient descent algorithms such as support vectors machines and matrix factorization. Industrial applications and deployments of Spark will also be presented.; the third part will introduce deep learning concepts, how to implement a deep learning model through TensorFlow, Keras and run the model on Spark. Example code will be made available in python (pySpark) notebooks.

A Comparative Study on Hadoop MapReduce and Apache Spark Framework for Big Data Analytics

International Journal of Research Publication and Reviews, 2024

In today internet world, due to the current advent of new technologies, mobile devices, and communication media like social networking sites, the amount of data generated every year is growing at a very high rate. The growth of this generated data is beyond our imagination. It is impossible to store these huge data sets in RDMBSs like MySQL, as there are no specific formats of the data and that can be in either text or image formats. It requires the need of technologies which can easily manage and process huge volumes of structured and unstructured data in real-time and can protect data privacy and security. Big data technologies like MapReduce, Apache Flume, and Apache Spark can capture, store, and analyze this huge amount of data in very efficient and less costly manner. Spark and MapReduce programming frameworks provide an effective open-source solution for managing and analyzing the Big Data. MapReduce is a high-performance distributed Big Data programming framework. It processes the data in batch processing environment. On the other hand, Apache Spark is a scalable distributed inmemory data processing engine. It processes the data in both batch and real time environment. It uses Resilient Distributed Datasets (RDD) and Directed Acyclic Graph (DAG) for data processing. In this paper, a review on Hadoop MapReduce and Apache Spark have been made by comparing them on various parameters like performance, streaming, fault tolerance, storage, language support, and reliability.