apache hadoop


Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model

Apache Hadoop , NoSQL and NewSQL solutions of big data
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Big Data is a popular term encompassing the use of techniques to capture, analyses, and process as well as visualize potentially large datasets in a reasonable timeframe not accessible to standard IT technologies, therefore platform, tools and software used for this

Integrating kerberos into apache hadoop
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Page 1. Integrating Kerberos into Apache Hadoop Kerberos Conference 2010 Owen OMalley owen@yahoo-inc.com Yahoos Hadoop Team Page 2. Kerberos Conference 2010 Who am I An architect working on Hadoop full time Mainly focused on MapReduce Tech-lead on

Apache Hadoop YARN
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Page 1. Hadoop 2.8 Configuration and First Examples Big Data Page 2. Apache Hadoop YARN Apache Hadoop (1.X) De facto Big Data open source platform Running for about 5 years in production at hundreds of companies like Yahoo, Ebay and Facebook Hadoop 2.X

Handling Big (ger) Logs: Connecting ProM 6 to Apache Hadoop .
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Within process mining the main goal is to support the analysis, improvement and apprehension of business processes. Numerous process mining techniques have been developed with that purpose. The majority of these techniques use conventional

Big data processing using Apache Hadoop in cloud system
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The ever growing technology has resulted in the need for storing and processing excessively large amounts of data on cloud. The current volume of data is enormous and is expected to replicate over 650 times by the year 201 out of which, 85% would be

Tweet analysis: twitter data processing using Apache Hadoop
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Abstract BIG DATAhas been getting much importance in different industries over the last year or two, on a scale that has generated lots of data every day. Big Data is a term applied to data sets of very large size such that the traditional databases are unable to process their

Big data analytics with apache hadoop mapreduce framework
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Huge amount of data cannot be handled by conventional database management system. For storing, processing and accessing massive volume of data, which is possible with help of Big data. In this paper we discussed the Hadoop Distributed File System and MapReduce

Introducing apache hadoop : the modern data operating system
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Stanford EE380 Computer Systems Colloquium Introducing Apache Hadoop : The Modern Data Operating System

Big data: Using arcgis with apache hadoop
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-Cassandra-a scalable multi-master database with no single points of failure-HBase-a scalable, distributed database that supports structured data storage for large tables-Hive-a data warehouse infrastructure that provides data summarization and ad hoc querying-Pig-a

Bigdata Analysis: Streaming Twitter Data with Apache Hadoop and Visualizing using BigInsights
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Nowadays the term big data becomes the buzzword in every organization due to ever- growing generation of data every day in life. The amount of data in industries has been increasing and exploding to high rates-so-called big data. The use of big data will become a

Big data analysis: comparison of hadoop MapReduce and apache spark
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Big data could be found in three forms: StructuredUn-structured, Semi-structured. The Apache Hadoop software library is a framework that allows for the distributed processing of big data sets across clusters of computers using simple programming models

Opinion mining of twitter data using Hadoop and Apache Pig
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If User location available we can also help to gauge the trends in different geographical regions. HADOOP The Apache Hadoop project develops open-source software for scalable, reliable, distributed computing. The Apache

Big data analytics using Hadoop tools Apache Hive vs Apache Pig
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data. Apache Hadoop is a framework to deal with big data which is based on distributed computing concepts. The Apache Hadoop framework has Hadoop Distributed File System (HDFS) and Hadoop MapReduce at its core

Bringing context to apache hadoop
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One of the first challenges when deploying MapReduce over pervasive grids is that Apache Hadoop , the most known MapReduce distribution, requires a highly structured environment such as a dedicated cluster or a cloud infrastructure. In pervasive environments, context

Apache hadoop as a storage backend for fedora commons
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Certain types of repositories are constantly growing in size. This is true for archives, national libraries, and research institutions. Research itself is increasingly data-driven (Hey Trefethen). This leads to vast amounts of raw and preprocessed data. Web archiving

Using Apache Hadoop * for context-aware recommender systems
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The CARS manages the massive amounts of data associated with recommendation engines information filtering systems that predict the rating of products and services and adds the intelligence of immediate contextual parameters, such as time of day, location, and weather

Mohohan: An on-line video transcoding service via apache hadoop
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Outline Mohohan: An On-line Video Transcoding Service via Apache Hadoop Chun-Han Chen OgilvyOne Inc.

Map reduce programming for electronic medical records data analysis on cloud using apache hadoop , hive and sqoop
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Health care organizations now a days made a strategic decision to turn huge medical data coming from various sources into competitive advantage. This will help the health care organizations to monitor any abnormal measurements which require immediate reaction

Building a Distributed Search System with Apache Hadoop and Lucene
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This work analyses the problem coming from the so called Big Data scenario, which can be defined as the technological challenge to manage and administer quantity of information with global dimension in the order of Terabyte (10 bytes) or Petabyte (10 bytes) and with an

Minimum redundancy maximum relevance: Mapreduce implementation using apache hadoop
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High-dimensional datasets include useful information for prediction purposes, but redundancy of features and noise affect negatively classifier performance. Feature selection algorithms are employed to tackle the curse of dimensionality and improve performance by


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