Apache Zookeeper Essentials
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Scalable Cloud Computing
Scalable Cloud Computing Keijo Heljanko Department of Computer Science and Engineering School of Science Aalto University [email protected] 2.10-2013 Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 1/57 Guest Lecturer I Guest Lecturer: Assoc. Prof. Keijo Heljanko, Department of Computer Science and Engineering, Aalto University, I Email: [email protected] I Homepage: https://people.aalto.fi/keijo_heljanko I For more info into today’s topic, attend the course: “T-79.5308 Scalable Cloud Computing” Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 2/57 Business Drivers of Cloud Computing I Large data centers allow for economics of scale I Cheaper hardware purchases I Cheaper cooling of hardware I Example: Google paid 40 MEur for a Summa paper mill site in Hamina, Finland: Data center cooled with sea water from the Baltic Sea I Cheaper electricity I Cheaper network capacity I Smaller number of administrators / computer I Unreliable commodity hardware is used I Reliability obtained by replication of hardware components and a combined with a fault tolerant software stack Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 3/57 Cloud Computing Technologies A collection of technologies aimed to provide elastic “pay as you go” computing I Virtualization of computing resources: Amazon EC2, Eucalyptus, OpenNebula, Open Stack Compute, . I Scalable file storage: Amazon S3, GFS, HDFS, . I Scalable batch processing: Google MapReduce, Apache Hadoop, PACT, Microsoft Dryad, Google Pregel, Spark, ::: I Scalable datastore: Amazon Dynamo, Apache Cassandra, Google Bigtable, HBase,. I Distributed Coordination: Google Chubby, Apache Zookeeper, . I Scalable Web applications hosting: Google App Engine, Microsoft Azure, Heroku, . -
Zookeeper Administrator's Guide
ZooKeeper Administrator's Guide A Guide to Deployment and Administration by Table of contents 1 Deployment........................................................................................................................ 2 1.1 System Requirements....................................................................................................2 1.2 Clustered (Multi-Server) Setup.....................................................................................2 1.3 Single Server and Developer Setup..............................................................................4 2 Administration.................................................................................................................... 4 2.1 Designing a ZooKeeper Deployment........................................................................... 5 2.2 Provisioning.................................................................................................................. 6 2.3 Things to Consider: ZooKeeper Strengths and Limitations..........................................6 2.4 Administering................................................................................................................6 2.5 Maintenance.................................................................................................................. 6 2.6 Supervision....................................................................................................................7 2.7 Monitoring.....................................................................................................................8 -
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 -
Projects – Other Than Hadoop! Created By:-Samarjit Mahapatra [email protected]
Projects – other than Hadoop! Created By:-Samarjit Mahapatra [email protected] Mostly compatible with Hadoop/HDFS Apache Drill - provides low latency ad-hoc queries to many different data sources, including nested data. Inspired by Google's Dremel, Drill is designed to scale to 10,000 servers and query petabytes of data in seconds. Apache Hama - is a pure BSP (Bulk Synchronous Parallel) computing framework on top of HDFS for massive scientific computations such as matrix, graph and network algorithms. Akka - a toolkit and runtime for building highly concurrent, distributed, and fault tolerant event-driven applications on the JVM. ML-Hadoop - Hadoop implementation of Machine learning algorithms Shark - is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones. Apache Crunch - Java library provides a framework for writing, testing, and running MapReduce pipelines. Its goal is to make pipelines that are composed of many user-defined functions simple to write, easy to test, and efficient to run Azkaban - batch workflow job scheduler created at LinkedIn to run their Hadoop Jobs Apache Mesos - is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, MPI, Hypertable, Spark, and other applications on a dynamically shared pool of nodes. -
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........................................................................................................................................................... -
Analysis and Detection of Anomalies in Mobile Devices
Master’s Degree in Informatics Engineering Dissertation Final Report Analysis and detection of anomalies in mobile devices António Carlos Lagarto Cabral Bastos de Lima [email protected] Supervisor: Prof. Dr. Tiago Cruz Co-Supervisor: Prof. Dr. Paulo Simões Date: September 1, 2017 Master’s Degree in Informatics Engineering Dissertation Final Report Analysis and detection of anomalies in mobile devices António Carlos Lagarto Cabral Bastos de Lima [email protected] Supervisor: Prof. Dr. Tiago Cruz Co-Supervisor: Prof. Dr. Paulo Simões Date: September 1, 2017 i Acknowledgements I strongly believe that both nature and nurture playing an equal part in shaping an in- dividual, and that in the end, it is what you do with the gift of life that determines who you are. However, in order to achieve great things motivation alone might just not cut it, and that’s where surrounding yourself with people that want to watch you succeed and better yourself comes in. It makes the trip easier and more enjoyable, and there is a plethora of people that I want to acknowledge for coming this far. First of all, I’d like to thank professor Tiago Cruz for giving me the support, motivation and resources to work on this project. The idea itself started over one of our then semi- regular morning coffee conversations and from there it developed into a full-fledged concept quickly. But this acknowledgement doesn’t start there, it dates a few years back when I first had the pleasure of having him as my teacher in one of the introductory courses. -
Open Source and Third Party Documentation
Open Source and Third Party Documentation Verint.com Twitter.com/verint Facebook.com/verint Blog.verint.com Content Introduction.....................2 Licenses..........................3 Page 1 Open Source Attribution Certain components of this Software or software contained in this Product (collectively, "Software") may be covered by so-called "free or open source" software licenses ("Open Source Components"), which includes any software licenses approved as open source licenses by the Open Source Initiative or any similar licenses, including without limitation any license that, as a condition of distribution of the Open Source Components licensed, requires that the distributor make the Open Source Components available in source code format. A license in each Open Source Component is provided to you in accordance with the specific license terms specified in their respective license terms. EXCEPT WITH REGARD TO ANY WARRANTIES OR OTHER RIGHTS AND OBLIGATIONS EXPRESSLY PROVIDED DIRECTLY TO YOU FROM VERINT, ALL OPEN SOURCE COMPONENTS ARE PROVIDED "AS IS" AND ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. Any third party technology that may be appropriate or necessary for use with the Verint Product is licensed to you only for use with the Verint Product under the terms of the third party license agreement specified in the Documentation, the Software or as provided online at http://verint.com/thirdpartylicense. You may not take any action that would separate the third party technology from the Verint Product. Unless otherwise permitted under the terms of the third party license agreement, you agree to only use the third party technology in conjunction with the Verint Product. -
Reflexión Académica En Diseño & Comunicación
ISSN 1668-1673 XXXII • 2017 Año XVIII. Vol 32. Noviembre 2017. Buenos Aires. Argentina Reflexión Académica en Diseño & Comunicación IV Congreso de Creatividad, Diseño y Comunicación para Profesores y Autoridades de Nivel Medio. `Interfaces Palermo´ Reflexión Académica en Diseño y Comunicación Comité Editorial Universidad de Palermo. Lucia Acar. Universidade Estácio de Sá. Brasil. Facultad de Diseño y Comunicación. Gonzalo Javier Alarcón Vital. Universidad Autónoma Metropolitana. México. Centro de Estudios en Diseño y Comunicación. Mercedes Alfonsín. Universidad de Buenos Aires. Argentina. Mario Bravo 1050. Fernando Alberto Alvarez Romero. Pontificia Universidad Católica del C1175ABT. Ciudad Autónoma de Buenos Aires, Argentina. Ecuador. Ecuador. www.palermo.edu Gonzalo Aranda Toro. Universidad Santo Tomás. Chile. [email protected] Christian Atance. Universidad de Lomas de Zamora. Argentina. Mónica Balabani. Universidad de Palermo. Argentina. Director Alberto Beckers Argomedo. Universidad Santo Tomás. Chile. Oscar Echevarría Renato Antonio Bertao. Universidade Positivo. Brasil. Allan Castelnuovo. Market Research Society. Reino Unido. Coordinadora de la Publicación Jorge Manuel Castro Falero. Universidad de la Empresa. Uruguay. Diana Divasto Raúl Castro Zuñeda. Universidad de Palermo. Argentina. Michael Dinwiddie. New York University. USA. Mario Rubén Dorochesi Fernandois. Universidad Técnica Federico Santa María. Chile. Adriana Inés Echeverria. Universidad de la Cuenca del Plata. Argentina. Universidad de Palermo Jimena Mariana García Ascolani. Universidad Comunera. Paraguay. Rector Marcelo Ghio. Instituto San Ignacio. Perú. Ricardo Popovsky Clara Lucia Grisales Montoya. Academia Superior de Artes. Colombia. Haenz Gutiérrez Quintana. Universidad Federal de Santa Catarina. Brasil. Facultad de Diseño y Comunicación José Korn Bruzzone. Universidad Tecnológica de Chile. Chile. Decano Zulema Marzorati. Universidad de Buenos Aires. Argentina. Oscar Echevarría Denisse Morales. -
Hortonworks Data Platform Date of Publish: 2018-09-21
Release Notes 3 Hortonworks Data Platform Date of Publish: 2018-09-21 http://docs.hortonworks.com Contents HDP 3.0.1 Release Notes..........................................................................................3 Component Versions.............................................................................................................................................3 New Features........................................................................................................................................................ 3 Deprecation Notices..............................................................................................................................................4 Terminology.............................................................................................................................................. 4 Removed Components and Product Capabilities.....................................................................................4 Unsupported Features........................................................................................................................................... 4 Technical Preview Features......................................................................................................................4 Upgrading to HDP 3.0.1...................................................................................................................................... 5 Before you begin..................................................................................................................................... -
PDF Download Scaling Big Data with Hadoop and Solr
SCALING BIG DATA WITH HADOOP AND SOLR - PDF, EPUB, EBOOK Hrishikesh Vijay Karambelkar | 166 pages | 30 Apr 2015 | Packt Publishing Limited | 9781783553396 | English | Birmingham, United Kingdom Scaling Big Data with Hadoop and Solr - PDF Book The default duration between two heartbeats is 3 seconds. Some other SQL-based distributed query engines to certainly bear in mind and consider for your use cases are:. What Can We Help With? Check out some of the job opportunities currently listed that match the professional profile, many of which seek experience with Search and Solr. This mode can be turned off manually by running the following command:. Has the notion of parent-child document relationships These exist as separate documents within the index, limiting their aggregation functionality in deeply- nested data structures. This step will actually create an authorization key with ssh, bypassing the passphrase check as shown in the following screenshot:. Fields may be split into individual tokens and indexed separately. Any key starting with a will go in the first region, with c the third region and z the last region. Now comes the interesting part. After the jobs are complete, the results are returned to the remote client via HiveServer2. Finally, Hadoop can accept data in just about any format, which eliminates much of the data transformation involved with the data processing. The difference in ingestion performance between Solr and Rocana Search is striking. Aptude has been working with our team for the past four years and we continue to use them and are satisfied with their work Warren E. These tables support most of the common data types that you know from the relational database world. -
Classifying, Evaluating and Advancing Big Data Benchmarks
Classifying, Evaluating and Advancing Big Data Benchmarks Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften vorgelegt beim Fachbereich 12 Informatik der Johann Wolfgang Goethe-Universität in Frankfurt am Main von Todor Ivanov aus Stara Zagora Frankfurt am Main 2019 (D 30) vom Fachbereich 12 Informatik der Johann Wolfgang Goethe-Universität als Dissertation angenommen. Dekan: Prof. Dr. Andreas Bernig Gutachter: Prof. Dott. -Ing. Roberto V. Zicari Prof. Dr. Carsten Binnig Datum der Disputation: 23.07.2019 Abstract The main contribution of the thesis is in helping to understand which software system parameters mostly affect the performance of Big Data Platforms under realistic workloads. In detail, the main research contributions of the thesis are: 1. Definition of the new concept of heterogeneity for Big Data Architectures (Chapter 2); 2. Investigation of the performance of Big Data systems (e.g. Hadoop) in virtual- ized environments (Section 3.1); 3. Investigation of the performance of NoSQL databases versus Hadoop distribu- tions (Section 3.2); 4. Execution and evaluation of the TPCx-HS benchmark (Section 3.3); 5. Evaluation and comparison of Hive and Spark SQL engines using benchmark queries (Section 3.4); 6. Evaluation of the impact of compression techniques on SQL-on-Hadoop engine performance (Section 3.5); 7. Extensions of the standardized Big Data benchmark BigBench (TPCx-BB) (Section 4.1 and 4.3); 8. Definition of a new benchmark, called ABench (Big Data Architecture Stack Benchmark), that takes into account the heterogeneity of Big Data architectures (Section 4.5). The thesis is an attempt to re-define system benchmarking taking into account the new requirements posed by the Big Data applications. -
Talend ESB Development Guide
Talend ESB Development Guide 7.0.1 Talend ESB Adapted for v7.0.1. Supersedes previous releases. Publication date: April 13, 2018 Copyright © 2018 Talend Inc. All rights reserved. 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/ This document may include documentation produced at The Apache Software Foundation which is licensed under The Apache License 2.0. Notices Talend and Talend ESB are trademarks of Talend, Inc. Apache CXF, CXF, Apache Karaf, Karaf, Apache Cellar, Cellar, Apache Camel, Camel, Apache Maven, Maven, Apache Archiva, Archiva, Apache Syncope, Syncope, Apache ActiveMQ, ActiveMQ, Apache Log4j, Log4j, Apache Felix, Felix, Apache ServiceMix, ServiceMix, Apache Ant, Ant, Apache Derby, Derby, Apache Tomcat, Tomcat, Apache ZooKeeper, ZooKeeper, Apache Jackrabbit, Jackrabbit, Apache Santuario, Santuario, Apache DS, DS, Apache Avro, Avro, Apache Abdera, Abdera, Apache Chemistry, Chemistry, Apache CouchDB, CouchDB, Apache Kafka, Kafka, Apache Lucene, Lucene, Apache MINA, MINA, Apache Velocity, Velocity, Apache FOP, FOP, Apache HBase, HBase, Apache Hadoop, Hadoop, Apache Shiro, Shiro, Apache Axiom, Axiom, Apache Neethi, Neethi, Apache WSS4J, WSS4J are trademarks of The Apache Foundation. Eclipse Equinox is a trademark of the Eclipse Foundation, Inc. SoapUI is a trademark of SmartBear Software. Hyperic is a trademark