IBM Big SQL (With Hbase), Splice Major Contributor to the Apache Be a Major Determinant“ Machine (Which Incorporates Hbase Madlib Project
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Hitachi Solution for Databases in Enterprise Data Warehouse Offload Package for Oracle Database with Mapr Distribution of Apache
Hitachi Solution for Databases in an Enterprise Data Warehouse Offload Package for Oracle Database with MapR Distribution of Apache Hadoop Reference Architecture Guide By Shashikant Gaikwad, Subhash Shinde December 2018 Feedback Hitachi Data Systems welcomes your feedback. Please share your thoughts by sending an email message to [email protected]. To assist the routing of this message, use the paper number in the subject and the title of this white paper in the text. Revision History Revision Changes Date MK-SL-131-00 Initial release December 27, 2018 Table of Contents Solution Overview 2 Business Benefits 2 High Level Infrastructure 3 Key Solution Components 4 Pentaho 6 Hitachi Advanced Server DS120 7 Hitachi Virtual Storage Platform Gx00 Models 7 Hitachi Virtual Storage Platform Fx00 Models 7 Brocade Switches 7 Cisco Nexus Data Center Switches 7 MapR Converged Data Platform 8 Red Hat Enterprise Linux 10 Solution Design 10 Server Architecture 11 Storage Architecture 13 Network Architecture 14 Data Analytics and Performance Monitoring Using Hitachi Storage Advisor 17 Oracle Enterprise Data Workflow Offload 17 Engineering Validation 29 Test Methodology 29 Test Results 30 1 Hitachi Solution for Databases in an Enterprise Data Warehouse Offload Package for Oracle Database with MapR Distribution of Apache Hadoop Reference Architecture Guide Use this reference architecture guide to implement Hitachi Solution for Databases in an enterprise data warehouse offload package for Oracle Database. This Oracle converged infrastructure provides a high performance, integrated, solution for advanced analytics using the following big data applications: . Hitachi Advanced Server DS120 with Intel Xeon Silver 4110 processors . Pentaho Data Integration . MapR distribution for Apache Hadoop This converged infrastructure establishes best practices for environments where you can copy data in an enterprise data warehouse to an Apache Hive database on top of Hadoop Distributed File System (HDFS). -
Database Solutions on AWS
Database Solutions on AWS Leveraging ISV AWS Marketplace Solutions November 2016 Database Solutions on AWS Nov 2016 Table of Contents Introduction......................................................................................................................................3 Operational Data Stores and Real Time Data Synchronization...........................................................5 Data Warehousing............................................................................................................................7 Data Lakes and Analytics Environments............................................................................................8 Application and Reporting Data Stores..............................................................................................9 Conclusion......................................................................................................................................10 Page 2 of 10 Database Solutions on AWS Nov 2016 Introduction Amazon Web Services has a number of database solutions for developers. An important choice that developers make is whether or not they are looking for a managed database or if they would prefer to operate their own database. In terms of managed databases, you can run managed relational databases like Amazon RDS which offers a choice of MySQL, Oracle, SQL Server, PostgreSQL, Amazon Aurora, or MariaDB database engines, scale compute and storage, Multi-AZ availability, and Read Replicas. You can also run managed NoSQL databases like Amazon DynamoDB -
Mapreduce Service
MapReduce Service Troubleshooting Issue 01 Date 2021-03-03 HUAWEI TECHNOLOGIES CO., LTD. Copyright © Huawei Technologies Co., Ltd. 2021. All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means without prior written consent of Huawei Technologies Co., Ltd. Trademarks and Permissions and other Huawei trademarks are trademarks of Huawei Technologies Co., Ltd. All other trademarks and trade names mentioned in this document are the property of their respective holders. Notice The purchased products, services and features are stipulated by the contract made between Huawei and the customer. All or part of the products, services and features described in this document may not be within the purchase scope or the usage scope. Unless otherwise specified in the contract, all statements, information, and recommendations in this document are provided "AS IS" without warranties, guarantees or representations of any kind, either express or implied. The information in this document is subject to change without notice. Every effort has been made in the preparation of this document to ensure accuracy of the contents, but all statements, information, and recommendations in this document do not constitute a warranty of any kind, express or implied. Issue 01 (2021-03-03) Copyright © Huawei Technologies Co., Ltd. i MapReduce Service Troubleshooting Contents Contents 1 Account Passwords.................................................................................................................. 1 1.1 Resetting -
Desarrollo De Una Solución Business Intelligence Mediante Un Paradigma De Data Lake
Desarrollo de una solución Business Intelligence mediante un paradigma de Data Lake José María Tagarro Martí Grado de Ingeniería Informática Consultor: Humberto Andrés Sanz Profesor: Atanasi Daradoumis Haralabus 13 de enero de 2019 Esta obra está sujeta a una licencia de Reconocimiento-NoComercial-SinObraDerivada 3.0 España de Creative Commons FICHA DEL TRABAJO FINAL Desarrollo de una solución Business Intelligence mediante un paradigma de Data Título del trabajo: Lake Nombre del autor: José María Tagarro Martí Nombre del consultor: Humberto Andrés Sanz Fecha de entrega (mm/aaaa): 01/2019 Área del Trabajo Final: Business Intelligence Titulación: Grado de Ingeniería Informática Resumen del Trabajo (máximo 250 palabras): Este trabajo implementa una solución de Business Intelligence siguiendo un paradigma de Data Lake sobre la plataforma de Big Data Apache Hadoop con el objetivo de ilustrar sus capacidades tecnológicas para este fin. Los almacenes de datos tradicionales necesitan transformar los datos entrantes antes de ser guardados para que adopten un modelo preestablecido, en un proceso conocido como ETL (Extraer, Transformar, Cargar, por sus siglas en inglés). Sin embargo, el paradigma Data Lake propone el almacenamiento de los datos generados en la organización en su propio formato de origen, de manera que con posterioridad puedan ser transformados y consumidos mediante diferentes tecnologías ad hoc para las distintas necesidades de negocio. Como conclusión, se indican las ventajas e inconvenientes de desplegar una plataforma unificada tanto para análisis Big Data como para las tareas de Business Intelligence, así como la necesidad de emplear soluciones basadas en código y estándares abiertos. Abstract (in English, 250 words or less): This paper implements a Business Intelligence solution following the Data Lake paradigm on Hadoop’s Big Data platform with the aim of showcasing the technology for this purpose. -
Modern Technologies of Bigdata Analytics: Case Study on Hadoop Platform Dharminder Yadav1, Umesh Chandra2
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 4, July- August 2017 ISSN 2278-6856 Modern Technologies of BigData Analytics: Case study on Hadoop Platform Dharminder Yadav1, Umesh Chandra2 1Research Scholar, Computer Science Department, Glocal University, Saharanpur, UP, India 2PhD, Assistant Professor, Computer Science Department, Glocal University, Saharanpur, UP, India Abstract exchange, banking, on-line and on-site procuring [2]. Data is growing in the worldwide by daily activities, by using the Enormous Information as an examination subject from a hand-held devices, the Internet, and social media sites.This few focuses course of events, paper main discusses about data processing by using various geographic yield, disciplinary output, types of distributed tool of Hadoop.This present study cover most of the tools used papers, topical and theoretical advancement. The Big Data in Hadoop that help in parallel processing and MapReduce. challenges define in 6V's that are variety, velocity, volume, The day since BigData term introduced to database world , Hadoop act like a savior for most of the large, small value, veracity, and volatility [23]. organization. Researchers will definitely found a way through Hadoop to work huge data concept and most of the researchers are being done in the field of BigData analytics and data mining with the help of Hadoop. Keywords— Big Data, Hadoop, HDFS (Hadoop Distributed File System), NOSQL 1.INTRODUCTION Big Data provide storage and data processing facilities to Cloud computing [26]. Big data comes around 2005 but now it is used everywhere in daily life, which alludes to an expansive scope of informational collections practically difficult to manage, handle and prepare utilizing accessible Volume: Data is growing exponentially by daily activities regular apparatuses and information administration which we handle. -
Mapr Spark Certification Preparation Guide
MAPR SPARK CERTIFICATION PREPARATION GUIDE By HadoopExam.com 1 About Spark and Its Demand ........................................................................................................................ 4 Core Spark: ........................................................................................................................................ 6 SparkSQL: .......................................................................................................................................... 6 Spark Streaming: ............................................................................................................................... 6 GraphX: ............................................................................................................................................. 6 Machine Learning: ............................................................................................................................ 6 Who should learn Spark? .............................................................................................................................. 6 About Spark Certifications: ........................................................................................................................... 6 HandsOn Exam: ......................................................................................................................................... 7 Multiple Choice Questions: ...................................................................................................................... -
Optimisation of Ad-Hoc Analysis of an OLAP Cube Using Sparksql
UPTEC X 17 007 Examensarbete 30 hp September 2017 Optimisation of Ad-hoc analysis of an OLAP cube using SparkSQL Milja Aho Abstract Optimisation of Ad-hoc analysis of an OLAP cube using SparkSQL Milja Aho Teknisk- naturvetenskaplig fakultet UTH-enheten An Online Analytical Processing (OLAP) cube is a way to represent a multidimensional database. The multidimensional database often uses a star Besöksadress: schema and populates it with the data from a relational database. The purpose of Ångströmlaboratoriet Lägerhyddsvägen 1 using an OLAP cube is usually to find valuable insights in the data like trends or Hus 4, Plan 0 unexpected data and is therefore often used within Business intelligence (BI). Mondrian is a tool that handles OLAP cubes that uses the query language Postadress: MultiDimensional eXpressions (MDX) and translates it to SQL queries. Box 536 751 21 Uppsala Apache Kylin is an engine that can be used with Apache Hadoop to create and query OLAP cubes with an SQL interface. This thesis investigates whether the Telefon: engine Apache Spark running on a Hadoop cluster is suitable for analysing OLAP 018 – 471 30 03 cubes and what performance that can be expected. The Star Schema Benchmark Telefax: (SSB) has been used to provide Ad-Hoc queries and to create a large database 018 – 471 30 00 containing over 1.2 billion rows. This database was created in a cluster in the Omicron office consisting of five worker nodes and one master node. Queries were Hemsida: then sent to the database using Mondrian integrated into the BI platform Pentaho. http://www.teknat.uu.se/student Amazon Web Services (AWS) has also been used to create clusters with 3, 6 and 15 slaves to see how the performance scales. -
Provisioning Guide Version 2.3.0 Table of Contents
Provisioning Guide Version 2.3.0 Table of Contents 1. About This Document . 3 1.1. Intended Audience . 3 1.2. New and Changed Information . 3 1.3. Notation Conventions . 4 1.4. Comments Encouraged . 6 2. Quick Start . 8 2.1. Download Binaries . 8 2.2. Unpack Installer and Server package . 9 2.3. Collect Information . 10 2.3.1. Java Location . 10 2.3.2. Data Nodes . 11 2.3.3. Distribution Manager URL . 11 2.4. Run Installer . 12 3. Introduction . 13 3.1. Security Considerations . 13 3.2. Provisioning Options . 14 3.3. Provisioning Activities . 14 3.4. Provisioning Master Node . 15 3.5. Trafodion Installer . 15 3.5.1. Usage . 16 3.5.2. Install vs. Upgrade . 17 3.5.3. Guided Setup . 17 3.5.4. Automated Setup . 17 3.6. Trafodion Provisioning Directories . 20 4. Requirements . 22 4.1. General Cluster and OS Requirements and Recommendations . 22 4.1.1. Hardware Requirements and Recommendations . 22 4.1.2. OS Requirements and Recommendations . 23 4.1.3. IP Ports . 24 4.2. Prerequisite Software . 25 4.2.1. Hadoop Software . 25 4.2.2. Software Packages . 25 4.3. Trafodion User IDs and Their Privileges . 26 4.3.1. Trafodion Runtime User . 26 4.3.2. Trafodion Provisioning User . 26 4.4. Recommended Configuration Changes . 28 4.4.1. Recommended Security Changes . 29 4.4.2. Recommended HDFS Configuration Changes . 29 4.4.3. Recommended HBase Configuration Changes . 29 5. Prepare . 31 5.1. Install Optional Workstation Software . 31 5.2. Configure Installation User ID . -
Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs"
Appendix to the paper "Code smell prediction employing machine learning meets emerging Java language constructs" Hanna Grodzicka, Michał Kawa, Zofia Łakomiak, Arkadiusz Ziobrowski, Lech Madeyski (B) The Appendix includes two tables containing the dataset used in the paper "Code smell prediction employing machine learning meets emerging Java lan- guage constructs". The first table contains information about 792 projects selected for R package reproducer [Madeyski and Kitchenham(2019)]. Projects were the base dataset for cre- ating the dataset used in the study (Table I). The second table contains information about 281 projects filtered by Java version from build tool Maven (Table II) which were directly used in the paper. TABLE I: Base projects used to create the new dataset # Orgasation Project name GitHub link Commit hash Build tool Java version 1 adobe aem-core-wcm- www.github.com/adobe/ 1d1f1d70844c9e07cd694f028e87f85d926aba94 other or lack of unknown components aem-core-wcm-components 2 adobe S3Mock www.github.com/adobe/ 5aa299c2b6d0f0fd00f8d03fda560502270afb82 MAVEN 8 S3Mock 3 alexa alexa-skills- www.github.com/alexa/ bf1e9ccc50d1f3f8408f887f70197ee288fd4bd9 MAVEN 8 kit-sdk-for- alexa-skills-kit-sdk- java for-java 4 alibaba ARouter www.github.com/alibaba/ 93b328569bbdbf75e4aa87f0ecf48c69600591b2 GRADLE unknown ARouter 5 alibaba atlas www.github.com/alibaba/ e8c7b3f1ff14b2a1df64321c6992b796cae7d732 GRADLE unknown atlas 6 alibaba canal www.github.com/alibaba/ 08167c95c767fd3c9879584c0230820a8476a7a7 MAVEN 7 canal 7 alibaba cobar www.github.com/alibaba/ -
PROCESSING LARGE / BIG DATA SET THROUGH Mapr and PIG
International Journal of Scientific & Engineering Research Volume 8, Issue 7, July-2017 863 ISSN 2229-5518 PROCESSING LARGE / BIG DATA SET THROUGH MapR AND PIG Arvind Kumar-Senior ERP Solution Architect / Manager, Derex Technologies, Inc. Abstract : We live in the data age. It’s not easy to measure the total volume of data stored electronically, but an IDC estimate put the size of the “digital universe” at 0.18 zettabytes in 2006, and is forecasting a tenfold growth by 2011 to 1.8 zettabytes.* A zettabyte is 1021 bytes, or equivalently one thousand Exabyte’s, one million petabytes, or one billion terabytes. That’s roughly the same order of magnitude as one disk drive for every person in the world. MapReduce is a programming model for data processing. The model is simple, yet nottoo simple to express useful programs in. Hadoop can run MapReduce programs writtenin various languages, MapReduce programs are inherentlyparallel, thus putting very large-scale data analysis into the hands of anyone withenough machines at their disposal. MapReduce comes into its own for large datasets. MapReduce is a framework for performing distributed data processing using the MapReduce programming paradigm. In the MapReduce paradigm, each job has a user-defined map phase, which is a parallel, share-nothing processing of input; followed by a user-defined reduce phase where the output of the map phase is aggregated). Pig raises the level of abstraction for processing large datasets. With MapReduce, thereis a map function and there is a reduce function, and working out how to fit your data processing into this pattern, which often requires multiple MapReduce stages, can be a challenge. -
Esgyndb 版本说明2.4.2
EsgynDB 版本说明 2.4.2 2018 年 7 月 版权 © Copyright 2018 Esgyn 公告 本文档包含的信息如有更改,恕不另行通知。 保留所有权利。除非版权法允许,否则在未经 Esgyn 预先书面许可的情况下, 严禁改编或翻译本手册的内容。Esgyn 对于本文中所包含的技术或编辑错误、遗 漏概不负责。 Esgyn 产品和服务附带的正式担保声明中规定的担保是该产品和服务享有的唯 一担保。本文中的任何信息均不构成额外的保修条款。 声明 Microsoft® 和 Windows® 是美国微软公司的注册商标。Java® 和 MySQL® 是 Oracle 及其子公司的注册商标。Bosun 是 Stack Exchange 的商标。Apache®、 Hadoop®、HBase®、Hive®、openTSDB®、Sqoop® 和 Trafodion® 是 Apache 软 件基金会的商标。Esgyn 和 EsgynDB 是 Esgyn 的商标。 目 录 1. 功能 ........................................................................................................ 2 EsgynDB 2.4.2 ................................................................................................... 2 EsgynDB 2.4.1 ................................................................................................... 2 EsgynDB 2.4.0 ................................................................................................... 2 2. 迁移要点................................................................................................ 3 2.1 在 EsgynDB 2.3.0 的基础上升级 ..................................................................... 3 2.1.1 系统 .......................................................................................................... 3 2.1.2 应用程序 .................................................................................................. 4 2.2 在 EsgynDB 2.2.0 或更早版本的基础上升级 ................................................. 5 2.2.1 系统 .......................................................................................................... 5 2.2.2 TRAF_HOME .......................................................................................... -
Your Expert Guide to Hadoop Big Data Platforms
E-guide Hadoop Big Data Platforms Buyer’s Guide – part 3 Your expert guide to Hadoop big data platforms E-guide In this e-guide A look at Amazon Elastic MapReduce A look at Amazon Elastic cloud-based Hadoop MapReduce cloud-based Hadoop Abie Reifer, DecisionWorx Learn more about the Cloudera The Amazon Elastic MapReduce Web service offers a managed Hadoop distribution Hadoop framework that enables users to distribute and process big data across dynamically scalable Amazon EC2 instances. Inside the Hortonworks open enterprise Hadoop distribution Amazon Elastic MapReduce provides users access to a cloud-based Hadoop implementation for analyzing and processing large amounts of data. Built on top Inside the IBM BigInsights of Amazon's cloud services, EMR leverages Amazon's Elastic Compute Cloud platform for big data and Simple Storage services, enabling users to provision a Hadoop cluster management quickly. Amazon's cloud elasticity and setup tools also give users a way to temporarily Inside the MapR Hadoop distribution for managing big scale up a cloud-based Hadoop cluster for short-term increased computing data capacity. Amazon EMR lets users focus on the design of their workflow without the distractions of configuring a Hadoop cluster. As with other Amazon cloud Inside the Microsoft Azure services, users pay for only what they use. HDInsight cloud infrastructure Page 1 of 28 E-guide In this e-guide Amazon Elastic MapReduce features A look at Amazon Elastic The current version of Amazon EMR, 4.3.0, bundles several open source MapReduce cloud-based applications, a set of components for users to monitor and manage cluster Hadoop resources, and components that enable application and cluster interoperability with other services.