Impala: a Modern, Open-Source SQL Engine for Hadoop
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Analysis of Big Data Storage Tools for Data Lakes Based on Apache Hadoop Platform
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 8, 2021 Analysis of Big Data Storage Tools for Data Lakes based on Apache Hadoop Platform Vladimir Belov, Evgeny Nikulchev MIREA—Russian Technological University, Moscow, Russia Abstract—When developing large data processing systems, determined the emergence and use of various data storage the question of data storage arises. One of the modern tools for formats in HDFS. solving this problem is the so-called data lakes. Many implementations of data lakes use Apache Hadoop as a basic Among the most widely known formats used in the Hadoop platform. Hadoop does not have a default data storage format, system are JSON [12], CSV [13], SequenceFile [14], Apache which leads to the task of choosing a data format when designing Parquet [15], ORC [16], Apache Avro [17], PBF [18]. a data processing system. To solve this problem, it is necessary to However, this list is not exhaustive. Recently, new formats of proceed from the results of the assessment according to several data storage are gaining popularity, such as Apache Hudi [19], criteria. In turn, experimental evaluation does not always give a Apache Iceberg [20], Delta Lake [21]. complete understanding of the possibilities for working with a particular data storage format. In this case, it is necessary to Each of these file formats has own features in file structure. study the features of the format, its internal structure, In addition, differences are observed at the level of practical recommendations for use, etc. The article describes the features application. Thus, row-oriented formats ensure high writing of both widely used data storage formats and the currently speed, but column-oriented formats are better for data reading. -
File Formats for Big Data Storage Systems
International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019 File Formats for Big Data Storage Systems Samiya Khan, Mansaf Alam analysis or visualization. Hadoop allows the user to store data Abstract: Big data is one of the most influential technologies of on its repository in several ways. Some of the commonly the modern era. However, in order to support maturity of big data available and understandable working formats include XML systems, development and sustenance of heterogeneous [9], CSV [10] and JSON [11]. environments is requires. This, in turn, requires integration of Although, JSON, XML and CSV are human-readable technologies as well as concepts. Computing and storage are the two core components of any big data system. With that said, big formats, but they are not the best way, to store data, on a data storage needs to communicate with the execution engine and Hadoop cluster. In fact, in some cases, storage of data in such other processing and visualization technologies to create a raw formats may prove to be highly inefficient. Moreover, comprehensive solution. This brings the facet of big data file parallel storage is not possible for data stored in such formats. formats into picture. This paper classifies available big data file In view of the fact that storage efficiency and parallelism are formats into five categories namely text-based, row-based, the two leading advantages of using Hadoop, the use of raw column-based, in-memory and data storage services. It also compares the advantages, shortcomings and possible use cases of file formats may just defeat the whole purpose. -
Developer Tool Guide
Informatica® 10.2.1 Developer Tool Guide Informatica Developer Tool Guide 10.2.1 May 2018 © Copyright Informatica LLC 2009, 2019 This software and documentation are provided only under a separate license agreement containing restrictions on use and disclosure. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise) without prior consent of Informatica LLC. Informatica, the Informatica logo, PowerCenter, and PowerExchange are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https://www.informatica.com/trademarks.html. Other company and product names may be trade names or trademarks of their respective owners. U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation is subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License. Portions of this software and/or documentation are subject to copyright held by third parties. Required third party notices are included with the product. The information in this documentation is subject to change without notice. If you find any problems in this documentation, report them to us at [email protected]. -
Research Article Improving I/O Efficiency in Hadoop-Based Massive Data Analysis Programs
Hindawi Scientific Programming Volume 2018, Article ID 2682085, 9 pages https://doi.org/10.1155/2018/2682085 Research Article Improving I/O Efficiency in Hadoop-Based Massive Data Analysis Programs Kyong-Ha Lee ,1 Woo Lam Kang,2 and Young-Kyoon Suh 3 1Research Data Hub Center, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 2School of Computing, KAIST, Daejeon, Republic of Korea 3School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea Correspondence should be addressed to Young-Kyoon Suh; [email protected] Received 30 April 2018; Revised 24 October 2018; Accepted 6 November 2018; Published 2 December 2018 Academic Editor: Basilio B. Fraguela Copyright © 2018 Kyong-Ha Lee et al. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Apache Hadoop has been a popular parallel processing tool in the era of big data. While practitioners have rewritten many conventional analysis algorithms to make them customized to Hadoop, the issue of inefficient I/O in Hadoop-based programs has been repeatedly reported in the literature. In this article, we address the problem of the I/O inefficiency in Hadoop-based massive data analysis by introducing our efficient modification of Hadoop. We first incorporate a columnar data layout into the conventional Hadoop framework, without any modification of the Hadoop internals. We also provide Hadoop with indexing capability to save a huge amount of I/O while processing not only selection predicates but also star-join queries that are often used in many analysis tasks. -
Unravel Data Systems Version 4.5
UNRAVEL DATA SYSTEMS VERSION 4.5 Component name Component version name License names jQuery 1.8.2 MIT License Apache Tomcat 5.5.23 Apache License 2.0 Tachyon Project POM 0.8.2 Apache License 2.0 Apache Directory LDAP API Model 1.0.0-M20 Apache License 2.0 apache/incubator-heron 0.16.5.1 Apache License 2.0 Maven Plugin API 3.0.4 Apache License 2.0 ApacheDS Authentication Interceptor 2.0.0-M15 Apache License 2.0 Apache Directory LDAP API Extras ACI 1.0.0-M20 Apache License 2.0 Apache HttpComponents Core 4.3.3 Apache License 2.0 Spark Project Tags 2.0.0-preview Apache License 2.0 Curator Testing 3.3.0 Apache License 2.0 Apache HttpComponents Core 4.4.5 Apache License 2.0 Apache Commons Daemon 1.0.15 Apache License 2.0 classworlds 2.4 Apache License 2.0 abego TreeLayout Core 1.0.1 BSD 3-clause "New" or "Revised" License jackson-core 2.8.6 Apache License 2.0 Lucene Join 6.6.1 Apache License 2.0 Apache Commons CLI 1.3-cloudera-pre-r1439998 Apache License 2.0 hive-apache 0.5 Apache License 2.0 scala-parser-combinators 1.0.4 BSD 3-clause "New" or "Revised" License com.springsource.javax.xml.bind 2.1.7 Common Development and Distribution License 1.0 SnakeYAML 1.15 Apache License 2.0 JUnit 4.12 Common Public License 1.0 ApacheDS Protocol Kerberos 2.0.0-M12 Apache License 2.0 Apache Groovy 2.4.6 Apache License 2.0 JGraphT - Core 1.2.0 (GNU Lesser General Public License v2.1 or later AND Eclipse Public License 1.0) chill-java 0.5.0 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 OpenCensus 0.12.3 Apache License 2.0 ApacheDS Protocol -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
Hive, Spark, Presto for Interactive Queries on Big Data
DEGREE PROJECT IN INFORMATION AND COMMUNICATION TECHNOLOGY, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Hive, Spark, Presto for Interactive Queries on Big Data NIKITA GUREEV KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE TRITA TRITA-EECS-EX-2018:468 www.kth.se Abstract Traditional relational database systems can not be efficiently used to analyze data with large volume and different formats, i.e. big data. Apache Hadoop is one of the first open-source tools that provides a dis- tributed data storage system and resource manager. The space of big data processing has been growing fast over the past years and many tech- nologies have been introduced in the big data ecosystem to address the problem of processing large volumes of data, and some of the early tools have become widely adopted, with Apache Hive being one of them. How- ever, with the recent advances in technology, there are other tools better suited for interactive analytics of big data, such as Apache Spark and Presto. In this thesis these technologies are examined and benchmarked in or- der to determine their performance for the task of interactive business in- telligence queries. The benchmark is representative of interactive business intelligence queries, and uses a star-shaped schema. The performance Hive Tez, Hive LLAP, Spark SQL, and Presto is examined with text, ORC, Par- quet data on different volume and concurrency. A short analysis and con- clusions are presented with the reasoning about the choice of framework and data format for a system that would run interactive queries on big data. -
Metadata in Pig Schema
Metadata In Pig Schema Kindled and self-important Lewis lips her columbine deschool while Bearnard books some mascarons transcontinentally. Elfish Clayton transfigure some blimps after doughiest Coleman predoom wherewith. How annihilated is Ray when freed and old-established Oral crank some flatboats? Each book selection of metadata selection for easy things possible that in metadata standards tend to make a library three times has no matter what aspects regarding interface Value type checking and concurrent information retrieval skills, and guesses that make easy and metadata in pig schema, without explicitly specified a traditional think aloud in a million developers. Preservation Metadata Digital Preservation Coalition. The actual photos of schema metadata of the market and pig benchmarking cloud service for. And insulates users from schema and storage format changes. Resulting Schema When both group a relation the result is enough new relation with two columns group conclude the upset of clever original relation The. While other if pig in schema metadata from top of the incoming data analysis using. Sorry for copy must upgrade is more by data is optional thrift based on building a metadata schemas work, your career in most preferred. Metacat publishes schema metadata and businessuser-defined. Use external metadata stores Azure HDInsight Microsoft Docs. Using the Parquet File Format with Impala Hive Pig and. In detail on how do i split between concrete operational services. Spark write avro to kafka Tower Dynamics Limited. For cases where the id or other metadata fields like ttl or timestamp of the document. We mostly start via an example Avro schema and a corresponding data file. -
Perform Data Engineering on Microsoft Azure Hdinsight (775)
Perform Data Engineering on Microsoft Azure HDInsight (775) www.cognixia.com Administer and Provision HDInsight Clusters Deploy HDInsight clusters Create a cluster in a private virtual network, create a cluster that has a custom metastore, create a domain-joined cluster, select an appropriate cluster type based on workload considerations, customize a cluster by using script actions, provision a cluster by using Portal, provision a cluster by using Azure CLI tools, provision a cluster by using Azure Resource Manager (ARM) templates and PowerShell, manage managed disks, configure vNet peering Deploy and secure multi-user HDInsight clusters Provision users who have different roles; manage users, groups, and permissions through Apache Ambari, PowerShell, and Apache Ranger; configure Kerberos; configure service accounts; implement SSH tunneling; restrict access to data Ingest data for batch and interactive processing Ingest data from cloud or on-premises data; store data in Azure Data Lake; store data in Azure Blob Storage; perform routine small writes on a continuous basis using Azure CLI tools; ingest data in Apache Hive and Apache Spark by using Apache Sqoop, Application Development Framework (ADF), AzCopy, and AdlCopy; ingest data from an on-premises Hadoop cluster Configure HDInsight clusters Manage metastore upgrades; view and edit Ambari configuration groups; view and change service configurations through Ambari; access logs written to Azure Table storage; enable heap dumps for Hadoop services; manage HDInsight configuration, use -
Enabling Geospatial in Big Data Lakes and Databases with Locationtech Geomesa
Enabling geospatial in big data lakes and databases with LocationTech GeoMesa ApacheCon@Home 2020 James Hughes James Hughes ● CCRi’s Director of Open Source Programs ● Working in geospatial software on the JVM for the last 8 years ● GeoMesa core committer / product owner ● SFCurve project lead ● JTS committer ● Contributor to GeoTools and GeoServer ● What type? Big Geospatial Data ● What volume? Problem Statement for today: Problem: How do we handle “big” geospatial data? Problem Statement for today: Problem: How do we handle “big” geospatial data? First refinement: What type of data do are we interested in? Vector Raster Point Cloud Problem Statement for today: Problem: How do we handle “big” geospatial data? First refinement: What type of data do are we interested in? Vector Raster Point Cloud Problem Statement for today: Problem: How do we handle “big” vector geospatial data? Second refinement: How much data is “big”? What is an example? GDELT: Global Database of Event, Language, and Tone “The GDELT Event Database records over 300 categories of physical activities around the world, from riots and protests to peace appeals and diplomatic exchanges, georeferenced to the city or mountaintop, across the entire planet dating back to January 1, 1979 and updated every 15 minutes.” ~225-250 million records Problem Statement for today: Problem: How do we handle “big” vector geospatial data? Second refinement: How much data is “big”? What is an example? Open Street Map: OpenStreetMap is a collaborative project to create a free editable map of the world. The geodata underlying the map is considered the primary output of the project. -
Hortonworks Data Platform Data Movement and Integration (December 15, 2017)
Hortonworks Data Platform Data Movement and Integration (December 15, 2017) docs.cloudera.com Hortonworks Data Platform December 15, 2017 Hortonworks Data Platform: Data Movement and Integration Copyright © 2012-2017 Hortonworks, Inc. Some rights reserved. The Hortonworks Data Platform, powered by Apache Hadoop, is a massively scalable and 100% open source platform for storing, processing and analyzing large volumes of data. It is designed to deal with data from many sources and formats in a very quick, easy and cost-effective manner. The Hortonworks Data Platform consists of the essential set of Apache Hadoop projects including MapReduce, Hadoop Distributed File System (HDFS), HCatalog, Pig, Hive, HBase, ZooKeeper and Ambari. Hortonworks is the major contributor of code and patches to many of these projects. These projects have been integrated and tested as part of the Hortonworks Data Platform release process and installation and configuration tools have also been included. Unlike other providers of platforms built using Apache Hadoop, Hortonworks contributes 100% of our code back to the Apache Software Foundation. The Hortonworks Data Platform is Apache-licensed and completely open source. We sell only expert technical support, training and partner-enablement services. All of our technology is, and will remain, free and open source. Please visit the Hortonworks Data Platform page for more information on Hortonworks technology. For more information on Hortonworks services, please visit either the Support or Training page. You can contact us directly to discuss your specific needs. Except where otherwise noted, this document is licensed under Creative Commons Attribution ShareAlike 4.0 License. http://creativecommons.org/licenses/by-sa/4.0/legalcode ii Hortonworks Data Platform December 15, 2017 Table of Contents 1. -
Major Technical Advancements in Apache Hive
Major Technical Advancements in Apache Hive Yin Huai1 Ashutosh Chauhan2 Alan Gates2 Gunther Hagleitner2 Eric N. Hanson3 Owen O’Malley2 Jitendra Pandey2 Yuan Yuan1 Rubao Lee1 Xiaodong Zhang1 1The Ohio State University 2Hortonworks Inc. 3Microsoft 1{huai, yuanyu, liru, zhang}@cse.ohio-state.edu 2{ashutosh, gates, ghagleitner, owen, jitendra}@hortonworks.com [email protected] ABSTRACT than 100 developers have made technical efforts to improve Hive on Apache Hive is a widely used data warehouse system for Apache more than 3000 issues. With its rapid development pace, Hive has Hadoop, and has been adopted by many organizations for various been significantly updated by new innovations and research since big data analytics applications. Closely working with many users the original Hive paper [45] was published four years ago. We will and organizations, we have identified several shortcomings of Hive present its major technical advancements in this paper. in its file formats, query planning, and query execution, which are Hive was originally designed as a translation layer on top of key factors determining the performance of Hive. In order to make Hadoop MapReduce. It exposes its own dialect of SQL to users Hive continuously satisfy the requests and requirements of process- and translates data manipulation statements (queries) to a directed ing increasingly high volumes data in a scalable and efficient way, acyclic graph (DAG) of MapReduce jobs. With an SQL interface, we have set two goals related to storage and runtime performance users do not need to write tedious and sometimes difficult MapRe- in our efforts on advancing Hive. First, we aim to maximize the ef- duce programs to manipulate data stored in Hadoop Distributed fective storage capacity and to accelerate data accesses to the data Filesystem (HDFS).