Oracle® Big Data SQL User’S Guide
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Java Linksammlung
JAVA LINKSAMMLUNG LerneProgrammieren.de - 2020 Java einfach lernen (klicke hier) JAVA LINKSAMMLUNG INHALTSVERZEICHNIS Build ........................................................................................................................................................... 4 Caching ....................................................................................................................................................... 4 CLI ............................................................................................................................................................... 4 Cluster-Verwaltung .................................................................................................................................... 5 Code-Analyse ............................................................................................................................................. 5 Code-Generators ........................................................................................................................................ 5 Compiler ..................................................................................................................................................... 6 Konfiguration ............................................................................................................................................. 6 CSV ............................................................................................................................................................. 6 Daten-Strukturen -
Oracle Metadata Management V12.2.1.3.0 New Features Overview
An Oracle White Paper October 12 th , 2018 Oracle Metadata Management v12.2.1.3.0 New Features Overview Oracle Metadata Management version 12.2.1.3.0 – October 12 th , 2018 New Features Overview Disclaimer This document is for informational purposes. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described in this document remains at the sole discretion of Oracle. This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle. This document and information contained herein may not be disclosed, copied, reproduced, or distributed to anyone outside Oracle without prior written consent of Oracle. This document is not part of your license agreement nor can it be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. 1 Oracle Metadata Management version 12.2.1.3.0 – October 12 th , 2018 New Features Overview Table of Contents Executive Overview ............................................................................ 3 Oracle Metadata Management 12.2.1.3.0 .......................................... 4 METADATA MANAGER VS METADATA EXPLORER UI .............. 4 METADATA HOME PAGES ........................................................... 5 METADATA QUICK ACCESS ........................................................ 6 METADATA REPORTING ............................................................. -
E6895 Advanced Big Data Analytics Lecture 4: Data Store
E6895 Advanced Big Data Analytics Lecture 4: Data Store Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Chief Scientist, Graph Computing, IBM Watson Research Center E6895 Advanced Big Data Analytics — Lecture 4 © CY Lin, 2016 Columbia University Reference 2 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Spark SQL 3 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Spark SQL 4 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Apache Hive 5 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using Hive to Create a Table 6 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Creating, Dropping, and Altering DBs in Apache Hive 7 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Another Hive Example 8 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Hive’s operation modes 9 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using HiveQL for Spark SQL 10 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Hive Language Manual 11 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using Spark SQL — Steps and Example 12 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Query testtweet.json Get it from Learning Spark Github ==> https://github.com/databricks/learning-spark/tree/master/files 13 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University SchemaRDD 14 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Row Objects Row objects represent records inside SchemaRDDs, and are simply fixed-length arrays of fields. -
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 -
Oracle Big Data SQL Release 4.1
ORACLE DATA SHEET Oracle Big Data SQL Release 4.1 The unprecedented explosion in data that can be made useful to enterprises – from the Internet of Things, to the social streams of global customer bases – has created a tremendous opportunity for businesses. However, with the enormous possibilities of Big Data, there can also be enormous complexity. Integrating Big Data systems to leverage these vast new data resources with existing information estates can be challenging. Valuable data may be stored in a system separate from where the majority of business-critical operations take place. Moreover, accessing this data may require significant investment in re-developing code for analysis and reporting - delaying access to data as well as reducing the ultimate value of the data to the business. Oracle Big Data SQL enables organizations to immediately analyze data across Apache Hadoop, Apache Kafka, NoSQL, object stores and Oracle Database leveraging their existing SQL skills, security policies and applications with extreme performance. From simplifying data science efforts to unlocking data lakes, Big Data SQL makes the benefits of Big Data available to the largest group of end users possible. KEY FEATURES Rich SQL Processing on All Data • Seamlessly query data across Oracle Oracle Big Data SQL is a data virtualization innovation from Oracle. It is a new Database, Hadoop, object stores, architecture and solution for SQL and other data APIs (such as REST and Node.js) on Kafka and NoSQL sources disparate data sets, seamlessly integrating data in Apache Hadoop, Apache Kafka, • Runs all Oracle SQL queries without modification – preserving application object stores and a number of NoSQL databases with data stored in Oracle Database. -
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. -
Hybrid Transactional/Analytical Processing: a Survey
Hybrid Transactional/Analytical Processing: A Survey Fatma Özcan Yuanyuan Tian Pınar Tözün IBM Resarch - Almaden IBM Research - Almaden IBM Research - Almaden [email protected] [email protected] [email protected] ABSTRACT To understand HTAP, we first need to look into OLTP The popularity of large-scale real-time analytics applications and OLAP systems and how they progressed over the years. (real-time inventory/pricing, recommendations from mobile Relational databases have been used for both transaction apps, fraud detection, risk analysis, IoT, etc.) keeps ris- processing as well as analytics. However, OLTP and OLAP ing. These applications require distributed data manage- systems have very different characteristics. OLTP systems ment systems that can handle fast concurrent transactions are identified by their individual record insert/delete/up- (OLTP) and analytics on the recent data. Some of them date statements, as well as point queries that benefit from even need running analytical queries (OLAP) as part of indexes. One cannot think about OLTP systems without transactions. Efficient processing of individual transactional indexing support. OLAP systems, on the other hand, are and analytical requests, however, leads to different optimiza- updated in batches and usually require scans of the tables. tions and architectural decisions while building a data man- Batch insertion into OLAP systems are an artifact of ETL agement system. (extract transform load) systems that consolidate and trans- For the kind of data processing that requires both ana- form transactional data from OLTP systems into an OLAP lytics and transactions, Gartner recently coined the term environment for analysis. Hybrid Transactional/Analytical Processing (HTAP). -
Hortonworks Data Platform Apache Spark Component Guide (December 15, 2017)
Hortonworks Data Platform Apache Spark Component Guide (December 15, 2017) docs.hortonworks.com Hortonworks Data Platform December 15, 2017 Hortonworks Data Platform: Apache Spark Component Guide 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. Feel free to 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. -
Schema Evolution in Hive Csv
Schema Evolution In Hive Csv Which Orazio immingled so anecdotally that Joey take-over her seedcake? Is Antin flowerless when Werner hypersensitise apodictically? Resolutely uraemia, Burton recalesced lance and prying frontons. In either format are informational and the file to collect important consideration to persist our introduction above image file processing with hadoop for evolution in Capabilities than that? Data while some standardized form scale as CSV TSV XML or JSON files. Have involved at each of spark sql engine for data in with malformed types are informational and to finish rendering before invoking file with. Spark csv files just storing data that this object and decision intelligence analytics queries to csv in bulk into another tab of lot of different in. Next button to choose to write that comes at query returns all he has very useful tutorial is coalescing around parquet evolution in hive schema evolution and other storage costs, query across partitions? Bulk load into an array types across some schema changes, which the views. Encrypt data come from the above schema evolution? This article helpful, only an error is more specialized for apis anywhere with the binary encoded in better than a dict where. Provide an evolution in column to manage data types and writing, analysts will be read json, which means the. This includes schema evolution partition evolution and table version rollback all. Apache hive to simplify your google cloud storage, the size of data cleansing, in schema hive csv files cannot be able to. Irs prior to create hive tables when querying using this guide for schema evolution in hive. -
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........................................................................................................................................................... -
Apache Hadoop Goes Realtime at Facebook
Apache Hadoop Goes Realtime at Facebook Dhruba Borthakur Joydeep Sen Sarma Jonathan Gray Kannan Muthukkaruppan Nicolas Spiegelberg Hairong Kuang Karthik Ranganathan Dmytro Molkov Aravind Menon Samuel Rash Rodrigo Schmidt Amitanand Aiyer Facebook {dhruba,jssarma,jgray,kannan, nicolas,hairong,kranganathan,dms, aravind.menon,rash,rodrigo, amitanand.s}@fb.com ABSTRACT 1. INTRODUCTION Facebook recently deployed Facebook Messages, its first ever Apache Hadoop [1] is a top-level Apache project that includes user-facing application built on the Apache Hadoop platform. open source implementations of a distributed file system [2] and Apache HBase is a database-like layer built on Hadoop designed MapReduce that were inspired by Googles GFS [5] and to support billions of messages per day. This paper describes the MapReduce [6] projects. The Hadoop ecosystem also includes reasons why Facebook chose Hadoop and HBase over other projects like Apache HBase [4] which is inspired by Googles systems such as Apache Cassandra and Voldemort and discusses BigTable, Apache Hive [3], a data warehouse built on top of the applications requirements for consistency, availability, Hadoop, and Apache ZooKeeper [8], a coordination service for partition tolerance, data model and scalability. We explore the distributed systems. enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the At Facebook, Hadoop has traditionally been used in conjunction system, and how this solution has significant advantages over the with Hive for storage and analysis of large data sets. Most of this sharded MySQL database scheme used in other applications at analysis occurs in offline batch jobs and the emphasis has been on Facebook and many other web-scale companies. -
Benchmarking Distributed Data Warehouse Solutions for Storing Genomic Variant Information
Research Collection Journal Article Benchmarking distributed data warehouse solutions for storing genomic variant information Author(s): Wiewiórka, Marek S.; Wysakowicz, David P.; Okoniewski, Michał J.; Gambin, Tomasz Publication Date: 2017-07-11 Permanent Link: https://doi.org/10.3929/ethz-b-000237893 Originally published in: Database 2017, http://doi.org/10.1093/database/bax049 Rights / License: Creative Commons Attribution 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Database, 2017, 1–16 doi: 10.1093/database/bax049 Original article Original article Benchmarking distributed data warehouse solutions for storing genomic variant information Marek S. Wiewiorka 1, Dawid P. Wysakowicz1, Michał J. Okoniewski2 and Tomasz Gambin1,3,* 1Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, Warsaw 00-665, Poland, 2Scientific IT Services, ETH Zurich, Weinbergstrasse 11, Zurich 8092, Switzerland and 3Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17a, Warsaw 01-211, Poland *Corresponding author: Tel.: þ48693175804; Fax: þ48222346091; Email: [email protected] Citation details: Wiewiorka,M.S., Wysakowicz,D.P., Okoniewski,M.J. et al. Benchmarking distributed data warehouse so- lutions for storing genomic variant information. Database (2017) Vol. 2017: article ID bax049; doi:10.1093/database/bax049 Received 15 September 2016; Revised 4 April 2017; Accepted 29 May 2017 Abstract Genomic-based personalized medicine encompasses storing, analysing and interpreting genomic variants as its central issues. At a time when thousands of patientss sequenced exomes and genomes are becoming available, there is a growing need for efficient data- base storage and querying.