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Netapp Solutions for Hadoop Reference Architecture: Cloudera Faiz Abidi (Netapp) and Udai Potluri (Cloudera) June 2018 | WP-7217
White Paper NetApp Solutions for Hadoop Reference Architecture: Cloudera Faiz Abidi (NetApp) and Udai Potluri (Cloudera) June 2018 | WP-7217 In partnership with Abstract There has been an exponential growth in data over the past decade and analyzing huge amounts of data in a reasonable time can be a challenge. Apache Hadoop is an open- source tool that can help your organization quickly mine big data and extract meaningful patterns from it. However, enterprises face several technical challenges when deploying Hadoop, specifically in the areas of cluster availability, operations, and scaling. NetApp® has developed a reference architecture with Cloudera to deliver a solution that overcomes some of these challenges so that businesses can ingest, store, and manage big data with greater reliability and scalability and with less time spent on operations and maintenance. This white paper discusses a flexible, validated, enterprise-class Hadoop architecture that is based on NetApp E-Series storage using Cloudera’s Hadoop distribution. TABLE OF CONTENTS 1 Introduction ........................................................................................................................................... 4 1.1 Big Data ..........................................................................................................................................................4 1.2 Hadoop Overview ...........................................................................................................................................4 2 NetApp E-Series -
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 -
Groups and Activities Report 2017
Groups and Activities Report 2017 ISBN 978-92-9083-491-5 This report is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 2 | Page CERN IT Department Groups and Activities Report 2017 CONTENTS GROUPS REPORTS 2017 Collaborations, Devices & Applications (CDA) Group ............................................................................. 6 Communication Systems (CS) Group .................................................................................................... 11 Compute & Monitoring (CM) Group ..................................................................................................... 16 Computing Facilities (CF) Group ........................................................................................................... 20 Databases (DB) Group ........................................................................................................................... 23 Departmental Infrastructure (DI) Group ............................................................................................... 27 Storage (ST) Group ................................................................................................................................ 28 ACTIVITIES AND PROJECTS REPORTS 2017 CERN openlab ........................................................................................................................................ 34 CERN School of Computing (CSC) ......................................................................................................... -
The Programmer's Guide to Apache Thrift MEAP
MEAP Edition Manning Early Access Program The Programmer’s Guide to Apache Thrift Version 5 Copyright 2013 Manning Publications For more information on this and other Manning titles go to www.manning.com ©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders. http://www.manning-sandbox.com/forum.jspa?forumID=873 Licensed to Daniel Gavrila <[email protected]> Welcome Hello and welcome to the third MEAP update for The Programmer’s Guide to Apache Thrift. This update adds Chapter 7, Designing and Serializing User Defined Types. This latest chapter is the first of the application layer chapters in Part 2. Chapters 3, 4 and 5 cover transports, error handling and protocols respectively. These chapters describe the foundational elements of Apache Thrift. Chapter 6 describes Apache Thrift IDL in depth, introducing the tools which enable us to describe data types and services in IDL. Chapters 7 through 9 bring these concepts into action, covering the three key applications areas of Apache Thrift in turn: User Defined Types (UDTs), Services and Servers. Chapter 7 introduces Apache Thrift IDL UDTs and provides insight into the critical role played by interface evolution in quality type design. Using IDL to effectively describe cross language types greatly simplifies the transmission of common data structures over messaging systems and other generic communications interfaces. Chapter 7 demonstrates the process of serializing types for use with external interfaces, disk I/O and in combination with Apache Thrift transport layer compression. -
CDP DATA CENTER 7.1 Laurent Edel : Solution Engineer Jacques Marchand : Solution Engineer Mael Ropars : Principal Solution Engineer
CDP DATA CENTER 7.1 Laurent Edel : Solution Engineer Jacques Marchand : Solution Engineer Mael Ropars : Principal Solution Engineer 30 Juin 2020 SPEAKERS • © 2019 Cloudera, Inc. All rights reserved. AGENDA • CDP DATA CENTER OVERVIEW • DETAILS ABOUT MAJOR COMPONENTS • PATH TO CDP DC && SMART MIGRATION • Q/A © 2019 Cloudera, Inc. All rights reserved. CLOUDERA DATA PLATFORM © 2020 Cloudera, Inc. All rights reserved. 4 ARCHITECTURE CIBLE : ENTERPRISE DATA CLOUD CDP Cloud Public CDP On-Prem (platform-as-a-service) (installable software) © 2020 Cloudera, Inc. All rights reserved. 5 CDP DATA CENTER OVERVIEW CDP Data Center (installable software) NEW CDP Data Center features include: Cloudera Manager • High-performance SQL analytics • Real-time stream processing, analytics, and management • Fine-grained security, enterprise metadata, and scalable data lineage • Support for object storage (tech preview) • Single pane of glass for management - multi-cluster support Enterprise analytics and data management platform, built for hybrid cloud, optimized for bare metal and ready for private cloud Cloudera Runtime © 2020 Cloudera, Inc. All rights reserved. 6 A NEW OPEN SOURCE DISTRIBUTION FOR BETTER CAPABILITY Cloudera Runtime - created from the best of CDH and HDP Deprecate competitive Merge overlapping Keep complementary Upgrade shared technologies technologies technologies technologies © 2019 Cloudera, Inc. All rights reserved. 7 COMPONENT LIST CDP Data Center 7.1(May) 2020 • Cloudera Manager 7.1 • HBase 2.2 • Key HSM 7.1 • Kafka Schema Registry 0.8 -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo........................................................................................................................................................... -
Implementing Replication for Predictability Within Apache Thrift Jianwei Tu the Ohio State University [email protected]
Implementing Replication for Predictability within Apache Thrift Jianwei Tu The Ohio State University [email protected] ABSTRACT have a large number of packets. A study indicated that about Interactive applications, such as search, social networking and 0.02% of all flows contributed more than 59.3% of the total retail, hosted in cloud data center generate large quantities of traffic volume [1]. TCP is the dominating transport protocol small workloads that require extremely low median and tail used in data center. However, the performance for short flows latency in order to provide soft real-time performance to users. in TCP is very poor: although in theory they can be finished These small workloads are known as short TCP flows. in 10-20 microseconds with 1G or 10G interconnects, the However, these short TCP flows experience long latencies actual flow completion time (FCT) is as high as tens of due in part to large workloads consuming most available milliseconds [2]. This is due in part to long flows consuming buffer in the switches. Imperfect routing algorithm such as some or all of the available buffers in the switches [3]. ECMP makes the matter even worse. We propose a transport Imperfect routing algorithms such as ECMP makes the matter mechanism using replication for predictability to achieve low even worse. State of the art forwarding in enterprise and data flow completion time (FCT) for short TCP flows. We center environment uses ECMP to statically direct flows implement replication for predictability within Apache Thrift across available paths using flow hashing. It doesn’t account transport layer that replicates each short TCP flow and sends for either current network utilization or flow size, and may out identical packets for both flows, then utilizes the first flow direct many long flows to the same path causing flash that finishes the transfer. -
Chapter 2 Introduction to Big Data Technology
Chapter 2 Introduction to Big data Technology Bilal Abu-Salih1, Pornpit Wongthongtham2 Dengya Zhu3 , Kit Yan Chan3 , Amit Rudra3 1The University of Jordan 2 The University of Western Australia 3 Curtin University Abstract: Big data is no more “all just hype” but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V’s to up 10V’s of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference. Keywords Big data, 3V of Big data, Cloud Computing, Data Lake, Enterprise Data Centre, PaaS, IaaS, SaaS, Hadoop, Spark, HBase, Information retrieval, Solr 2.1 Introduction The ability to exploit the ever-growing amounts of business-related data will al- low to comprehend what is emerging in the world. In this context, Big Data is one of the current major buzzwords [1]. Big Data (BD) is the technical term used in reference to the vast quantity of heterogeneous datasets which are created and spread rapidly, and for which the conventional techniques used to process, analyse, retrieve, store and visualise such massive sets of data are now unsuitable and inad- equate. This can be seen in many areas such as sensor-generated data, social media, uploading and downloading of digital media. -
Release Notes Date Published: 2020-08-10 Date Modified
Cloudera Runtime 7.1.3 Release Notes Date published: 2020-08-10 Date modified: https://docs.cloudera.com/ Legal Notice © Cloudera Inc. 2021. All rights reserved. The documentation is and contains Cloudera proprietary information protected by copyright and other intellectual property rights. No license under copyright or any other intellectual property right is granted herein. Copyright information for Cloudera software may be found within the documentation accompanying each component in a particular release. Cloudera software includes software from various open source or other third party projects, and may be released under the Apache Software License 2.0 (“ASLv2”), the Affero General Public License version 3 (AGPLv3), or other license terms. Other software included may be released under the terms of alternative open source licenses. Please review the license and notice files accompanying the software for additional licensing information. Please visit the Cloudera software product page for more information on Cloudera software. For more information on Cloudera support services, please visit either the Support or Sales page. Feel free to contact us directly to discuss your specific needs. Cloudera reserves the right to change any products at any time, and without notice. Cloudera assumes no responsibility nor liability arising from the use of products, except as expressly agreed to in writing by Cloudera. Cloudera, Cloudera Altus, HUE, Impala, Cloudera Impala, and other Cloudera marks are registered or unregistered trademarks in the United States and other countries. All other trademarks are the property of their respective owners. Disclaimer: EXCEPT AS EXPRESSLY PROVIDED IN A WRITTEN AGREEMENT WITH CLOUDERA, CLOUDERA DOES NOT MAKE NOR GIVE ANY REPRESENTATION, WARRANTY, NOR COVENANT OF ANY KIND, WHETHER EXPRESS OR IMPLIED, IN CONNECTION WITH CLOUDERA TECHNOLOGY OR RELATED SUPPORT PROVIDED IN CONNECTION THEREWITH. -
Performance Evaluation of Relational Embedded Databases: an Empirical
Performance evaluation of relational Check for updates embedded databases: an empirical study Evaluación del rendimiento de bases de datos embebida: un estudio empírico Author: ABSTRACT 1 Hassan B. Hassan Introduction: With the rapid deployment of embedded databases Qusay I. Sarhan2 across a wide range of embedded devices such as mobile devices, Internet of Things (IoT) devices, etc., the amount of data generat- ed by such devices is also growing increasingly. For this reason, the SCIENTIFIC RESEARCH performance is considered as a crucial criterion in the process of selecting the most suitable embedded database management system How to cite this paper: to be used to store/retrieve data of these devices. Currently, many Hassan, B. H., and Sarhan, Q. I., Performance embedded databases are available to be utilized in this context. Ma- evaluation of relational embedded databases: an empirical study, Kurdistan, Irak. Innovaciencia. terials and Methods: In this paper, four popular open-source rela- 2018; 6(1): 1-9. tional embedded databases; namely, H2, HSQLDB, Apache Derby, http://dx.doi.org/10.15649/2346075X.468 and SQLite have been compared experimentally with each other to evaluate their operational performance in terms of creating data- Reception date: base tables, retrieving data, inserting data, updating data, deleting Received: 22 September 2018 Accepted: 10 December 2018 data. Results and Discussion: The experimental results of this Published: 28 December 2018 paper have been illustrated in Table 4. Conclusions: The experi- mental results and analysis showed that HSQLDB outperformed other databases in most evaluation scenarios. Keywords: Embedded devices, Embedded databases, Performance evaluation, Database operational performance, Test methodology. -
Storage and Ingestion Systems in Support of Stream Processing
Storage and Ingestion Systems in Support of Stream Processing: A Survey Ovidiu-Cristian Marcu, Alexandru Costan, Gabriel Antoniu, María Pérez-Hernández, Radu Tudoran, Stefano Bortoli, Bogdan Nicolae To cite this version: Ovidiu-Cristian Marcu, Alexandru Costan, Gabriel Antoniu, María Pérez-Hernández, Radu Tudoran, et al.. Storage and Ingestion Systems in Support of Stream Processing: A Survey. [Technical Report] RT-0501, INRIA Rennes - Bretagne Atlantique and University of Rennes 1, France. 2018, pp.1-33. hal-01939280v2 HAL Id: hal-01939280 https://hal.inria.fr/hal-01939280v2 Submitted on 14 Dec 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Storage and Ingestion Systems in Support of Stream Processing: A Survey Ovidiu-Cristian Marcu, Alexandru Costan, Gabriel Antoniu, María S. Pérez-Hernández, Radu Tudoran, Stefano Bortoli, Bogdan Nicolae TECHNICAL REPORT N° 0501 November 2018 Project-Team KerData ISSN 0249-0803 ISRN INRIA/RT--0501--FR+ENG Storage and Ingestion Systems in Support of Stream Processing: A Survey Ovidiu-Cristian Marcu∗, Alexandru -
Pentaho EMR46 SHIM 7.1.0.0 Open Source Software Packages
Pentaho EMR46 SHIM 7.1.0.0 Open Source Software Packages Contact Information: Project Manager Pentaho EMR46 SHIM Hitachi Vantara Corporation 2535 Augustine Drive Santa Clara, California 95054 Name of Product/Product Version License Component An open source Java toolkit for 0.9.0 Apache License Version 2.0 Amazon S3 AOP Alliance (Java/J2EE AOP 1.0 Public Domain standard) Apache Commons BeanUtils 1.9.3 Apache License Version 2.0 Apache Commons CLI 1.2 Apache License Version 2.0 Apache Commons Daemon 1.0.13 Apache License Version 2.0 Apache Commons Exec 1.2 Apache License Version 2.0 Apache Commons Lang 2.6 Apache License Version 2.0 Apache Directory API ASN.1 API 1.0.0-M20 Apache License Version 2.0 Apache Directory LDAP API Utilities 1.0.0-M20 Apache License Version 2.0 Apache Hadoop Amazon Web 2.7.2 Apache License Version 2.0 Services support Apache Hadoop Annotations 2.7.2 Apache License Version 2.0 Name of Product/Product Version License Component Apache Hadoop Auth 2.7.2 Apache License Version 2.0 Apache Hadoop Common - 2.7.2 Apache License Version 2.0 org.apache.hadoop:hadoop-common Apache Hadoop HDFS 2.7.2 Apache License Version 2.0 Apache HBase - Client 1.2.0 Apache License Version 2.0 Apache HBase - Common 1.2.0 Apache License Version 2.0 Apache HBase - Hadoop 1.2.0 Apache License Version 2.0 Compatibility Apache HBase - Protocol 1.2.0 Apache License Version 2.0 Apache HBase - Server 1.2.0 Apache License Version 2.0 Apache HBase - Thrift - 1.2.0 Apache License Version 2.0 org.apache.hbase:hbase-thrift Apache HttpComponents Core