apache spark 2019



apache spark 2019 Apache Spark is an open source parallel processing framework for running large-scale data analytics applications across clustered computers Apache Spark is an open-source distributed general-purpose cluster-computing framework. Originally developed at the University of California, Berkeley AMPLab,

Processing Large Raster and Vector Data in Apache Spark
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Spatial data processing frameworks in many cases are limited to vector data only. However, an important type of spatial data is raster data which is produced by sensors on satellites but also by high resolution cameras taking pictures of nano structures, such as chips on wafers

Geospatial Data Management in Apache Spark : A Tutorial
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The volume of spatial data increases at a staggering rate. This tutorial comprehensively studies how existing works extend Apache Spark to uphold massive-scale spatial data. During this 1.5 hour tutorial, we first provide a background introduction of the characteristics

ARFF data source library for distributed single/multiple instance, single/multiple output learning on Apache Spark
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Apache Spark has become a popular framework for distributed machine learning and data mining. However, it lacks support for operating with Attribute-Relation File Format (ARFF) files in a native, convenient, transparent, efficient, and distributed way. Moreover, Spark


This is an exciting time to be a data platform professional. Over the past decade, we have seen a proliferation of data platform technologies, all trying to solve the critical problem of our era: collecting, storing, managing, and querying ever-increasing amounts of data. To

Benchmarking Spark -SQL under Alliterative RDF Relational Storage Backends
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In this paper, we present a systematic comparison of there rele- vant RDF relational schemas, ie, Single Statement Table, Property Ta- bles or Vertically-Partitioned Tables queried using Apache Spark RDF query answering using apache spark : Re- view and assessment GraphX. This book also discusses how to tune Spark parameters for production scenarios and how to write robust applications in Apache Spark using Scala in cloud computing environment. The book is organized into 11 chapters Apache Spark , on the other hand, is gaining significant attention in the field of big data processing because of its in-memory process- ing capabilities Keywords Frequent itemset mining Apache Spark Apriori algorithm Large-scale datasets 1 Introduction Apache Spark is a unified analytic engine for massive data processing which has been successfully used in many data mining fields. In this paper, we propose a dis- tributed algorithm for mining frequent itemsets over massive streaming data named SWEclat

A NUMA Aware Spark on Many-cores and Large Memory Servers
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Abstract: Within the scope of the CloudDBAppliance project, we investigate how Apache Spark can leverage a many cores and large memory platform, with a scale up approach as opposed to the commonly used scale out one: that is, the approach is to deploy a spark cluster

Learning on Apache Spark and Analytics Zoo
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RDFSpark: a new solution for querying massive RDF data using spark
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On the other hand, Apache Spark is an open source distributed computing framework, characterized by its speed as MapReduce, Big Data pro- cessing has never been easier In this paper, we have seen the features of Apache Spark in data processing and analysis

Rating Prediction using Deep Learning and Spark
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There has been many approaches to integrate distributed systems and multi-core GPU systems, such as, DeepLearning Pipeline for Apache Spark by Databricks, TensorFlowOnSpark by Yahoo, BigDL/Analytics Zoo by Intel, DL4J by Skymind, Distributed DeepLearning with

Apache Hadoop: A Guide for Cluster Configuration Testing
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Hadoop facilitates processing through MapReduce, analyzing using Apache Spark and storage using the Hadoop Distributed File System (HDFS). Hadoop is popular due to its wide applicability and easy to run on commodity hardware functionality In each iteration, the input dataset is scanned that resides on disk, causing the high disk I/O. Apache Spark implementations of Apriori show better performance due to in-memory processing capabilities. It 3.2 Apache Spark Apache

Spark Framework for Streaming and Generating Predictive Business Intelligence
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Abstract Apache Spark is one of the stream processing frameworks that can be associated with cloud computing. Real time streaming data is processed with machine learning and natural language processing. Apache Spark is used to explore process mining as well

Privacy-Preserving Record Linkage with Spark
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In this work, we evaluate Apache Spark as an option to scale PPRL It is known that Apache Spark , a prominent framework within the Hadoop-ecosystem, can be used to achieve great performance and scale to hundreds of nodes [35] Apache Spark for processing large-scale data on various nodes is a recent MapReduce based frame- work and Hedjazi et al Apache Spark based on the Avro framework combines the picture files and provides an in-memory order to allow the actions to happen much faster

Spark -based Parallelization of Basic Local Alignment Search Tool
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The Apache Spark YARN [17] was adopted to task scheduling and resource allocation 2. Awan AJ, M. Brorsson, V. Vlassov, E. Ayguade (2016). Architectural Impact on Performance of In-memory Data Analytics: Apache Spark Case Study, arXiv Preprint arXiv:1604.08484 It is one of the subfields of artificial intelligence that concentrates on the construction of algo- rithms, which are able to learn from and predict from data. Figure 2 shows the Apache Spark platform Consequently, ML enjoys Fig. 2 Apache Spark platform Page 4

Querying large-scale RDF datasets using the SANSA framework
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In particular, we demonstrate a W3C SPARQL endpoint pow- ered by our SANSA frameworks RDF partitioning system and Apache Spark for querying the DBpedia knowledge base. This programs. 1 http:// spark . apache .org/ Page 2

EC-Shuffle: Dynamic Erasure Coding Optimization for Efficient and Reliable Shuffle in Spark
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Abstract Fault-tolerance capabilities attract increasing at- tention from existing data processing frameworks, such as Apache Spark . To avoid replaying costly distributed compu- tation, like shuffle, local checkpoint and remote replication are two popular approaches

Big Data as a source of statistics
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A Siddiqui 2019 194.44.12.92 Apache Spark Apache Spark is an open-source distributed cluster-computing framework Every year some new technologies are coming up to meet the challenges of storing big data like Apache Spark , MongoDB to name a few Abstract Apache Spark is probably the most widely adopted framework for developing big-data batch applica- tions and for executing them on a cluster of (virtual) machines Section 2 provides an overview of Apache Spark and recalls the definition of CLTLoc and TA


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