Using Oracle Goldengate for Big Data
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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 -
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. -
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 ............................................................. -
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]. -
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 -
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. -
Getting Started with Apache Avro
Getting Started with Apache Avro By Reeshu Patel Getting Started with Apache Avro 1 Introduction Apache Avro Apache Avro is a remote procedure call and serialization framework developed with Apache's Hadoop project. This is uses JSON for defining data types and protocols, and tend to serializes data in a compact binary format. In other words, Apache Avro is a data serialization system. Its frist native use is in Apache Hadoop, where it's provide both a serialization format for persistent data, and a correct format for communication between Hadoop nodes, and from client programs to the apache Hadoop services. Avro is a data serialization system.It'sprovides: Rich data structures. A compact, fast, binary data format. A container file, to store persistent data. Remote procedure call . It's easily integration with dynamic languages. Code generation is not mendetory to read or write data files nor to use or implement Remote procedure call protocols. Code generation is as an optional optimization, only worth implementing for statically typewritten languages. Schemas of Apache Avro When Apache avro data is read, the schema use when writing it's always present. This permits every datum to be written in no per-value overheads, creating serialization both fast and small. It also facilitates used dynamic, scripting languages, and data, together with it's schema, is fully itself-describing. 2 Getting Started with Apache Avro When Apache avro data is storein a file, it's schema is store with it, so that files may be processe later by any program. If the program is reading the data expects a different schema this can be simply resolved, since twice schemas are present.