MapReduce

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  • Google 's Caching For Big Data Applications Using The Mapreduce Framework

    1914 Words  | 8 Pages

    (Twiche) TWITTER TREND CACHING FOR BIG-DATA APPLICATIONS USING THE MAPREDUCE FRAMEWORK Santosh Wayal,Yogesh More,Prasad Wandhekar,Utkarsh Honey, Prof. Jayshree Chaudhari Department of Computer Engineering Dr.D.Y.Patil School of Engineering Pune, India ABSTRACT: The big-data refers to the large-scale distributed data processing applications which work on exceptionally large amounts of data like twitter data. Google’s MapReduce and Apache’s Hadoop, its open-source implementation, are the software

  • Investigation Into An Efficient Hybrid Model Of A With Mapreduce + Parallel Platform Data Warehouse Architecture Essay

    1954 Words  | 8 Pages

    Investigation into deriving an Efficient Hybrid model of a - MapReduce + Parallel-Platform Data Warehouse Architecture Shrujan Kotturi skotturi@uncc.edu College of Computing and Informatics Department of Computer Science Under the Supervision of Dr. Yu Wang yu.wang@uncc.edu Professor, Computer Science Investigation into deriving an Efficient Hybrid model of a - MapReduce + Parallel-Platform Data Warehouse Architecture Shrujan Kotturi University of North Carolina at Charlotte North Carolina

  • Notes On Hadoop And Mark Logic

    1638 Words  | 7 Pages

    Jyoti rana Professor Savidrath By IT 440/540 4/26/2016 How To: Hadoop and Mark logic Before talking about Hadoop and Mark Logic, it is very important to understand Big Data. What is big data, what’s the consequence and how it is linked with Hadoop and Mark Logic? “Large set of data, unstructured and structured which is created everyday over the internet via different devices is known as Big Data”. For example: “if the user has 7 accounts and creates multiple

  • The Importance Of Big Data

    809 Words  | 4 Pages

    Resilient Distributed Datasets (RDD) and Directed Acyclic Graphs (DAG). RDDs are a collection of data items that can be split and can be stored in-memory on worker nodes of a spark cluster. The DAG abstraction of Spark helps eliminate the Hadoop MapReduce multistage execution model. As Rajiv Bhat, Senior Vice President of Data Sciences and Marketplace at InMobi rightly said, “Spark is beautiful. With Hadoop, it would take six-seven months to develop a machine learning model. Now, we can do about

  • Data Analysis in the Cloud

    747 Words  | 3 Pages

    V. DATA ANALYSIS IN THE CLOUD In this section we descus the expected properties of a system designed for performing data analysis at the cloud environment and how parallel database systems and MapReduce-based systems achieve these properties. Expected properties of a system designed for performing data analysis at cloud: • Performance Performance is the primary characteristic of database systems that can use to select best solution for the system.High performance relate with quality, amount and

  • Essay on Social Media's Role in Network Management in Big Data

    772 Words  | 4 Pages

    after they have occurred. FlowComb also uses MapReduce framework to influence the design of the system. MapReduce provides a divide and conquer data processing model, where large workloads are split into smaller tasks, each processed by a single server in a cluster (the map phase). The results of each task are sent over the cluster network (the shuffle phase) and merged to obtain the final result (the reduce phase). The network footprint of a MapReduce job consists pre dominantly of traffic sent during

  • Database Analysis : The Data Warehouse

    1153 Words  | 5 Pages

    nicely in the data warehouse include text, images, audio and video, all of which are considered as semi-structured data. Thus, this is where Hadoop enters the architecture. Hadoop is a family of products (Hadoop Distributed File System (HDFS), MapReduce, Pig, Hive, HBase, Mahout, Cassandra, YARN, Ambari, Avro, Chukwa, and Zookeeper), each with different and multiple capabilities. Please visit www.apache.org for details on these products. These products are available as native open source from Apache

  • Using Parallel Processing For Large Scale Database Analysis

    1280 Words  | 6 Pages

    processing for large-scale database analysis, so the MapReduce is one of new technology to get amounts of data, perform massive computation, and extract critical knowledge out of big data for business intelligence, proper analysis of large scale of datasets, it requires accurate input output capacity from the large server systems to perform and analyze weblog data which is derived from two steps called mapping and reducing. Between these two steps, MapReduce requires a on important phase called shuffling

  • Historical Features Of Spatial Data Essay

    1075 Words  | 5 Pages

    Spatial data mining is a rising exploration field devoted to the advancement and utilization of novel computational procedures for the examination of big spatial datasets. It envelops methods for finding valuable spatial associations and patterns that are not stored in spatial datasets. Generally these procedures need to manage complex features with spatial data properties. The properties and relationships that have been contained in spatial data are different from transactional data. For instance

  • What Is Hadoop Data Analysis Technologies

    867 Words  | 4 Pages

    provides the ability to collect data on HDFS (Hadoop Distributed File System), there are many applications available in the market (like MapReduce, Pig and Hive) that can be used to analyze the data. Let us first take a closer look at all three applications and then analyze which application is better suited for KISAN CALL CENTER DATA project. 4.1.1 MapReduce MapReduce is a set of Java

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