Data Management in Cloud Environments: Nosql and Newsql Data Stores Katarina Grolinger1, Wilson a Higashino1,2*, Abhinav Tiwari1 and Miriam AM Capretz1
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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 ............................................................. -
Socrates: the New SQL Server in the Cloud
Socrates: The New SQL Server in the Cloud Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, Vijendra Purohit, Hugh Qu, Chaitanya Sreenivas Ravella, Krystyna Reisteter, Sheetal Shrotri, Dixin Tang, Vikram Wakade Microsoft Azure & Microsoft Research ABSTRACT 1 INTRODUCTION The database-as-a-service paradigm in the cloud (DBaaS) The cloud is here to stay. Most start-ups are cloud-native. is becoming increasingly popular. Organizations adopt this Furthermore, many large enterprises are moving their data paradigm because they expect higher security, higher avail- and workloads into the cloud. The main reasons to move ability, and lower and more flexible cost with high perfor- into the cloud are security, time-to-market, and a more flexi- mance. It has become clear, however, that these expectations ble “pay-as-you-go” cost model which avoids overpaying for cannot be met in the cloud with the traditional, monolithic under-utilized machines. While all these reasons are com- database architecture. This paper presents a novel DBaaS pelling, the expectation is that a database runs in the cloud architecture, called Socrates. Socrates has been implemented at least as well as (if not better) than on premise. Specifically, in Microsoft SQL Server and is available in Azure as SQL DB customers expect a “database-as-a-service” to be highly avail- Hyperscale. This paper describes the key ideas and features able (e.g., 99.999% availability), support large databases (e.g., of Socrates, and it compares the performance of Socrates a 100TB OLTP database), and be highly performant. -
Beyond Relational Databases
EXPERT ANALYSIS BY MARCOS ALBE, SUPPORT ENGINEER, PERCONA Beyond Relational Databases: A Focus on Redis, MongoDB, and ClickHouse Many of us use and love relational databases… until we try and use them for purposes which aren’t their strong point. Queues, caches, catalogs, unstructured data, counters, and many other use cases, can be solved with relational databases, but are better served by alternative options. In this expert analysis, we examine the goals, pros and cons, and the good and bad use cases of the most popular alternatives on the market, and look into some modern open source implementations. Beyond Relational Databases Developers frequently choose the backend store for the applications they produce. Amidst dozens of options, buzzwords, industry preferences, and vendor offers, it’s not always easy to make the right choice… Even with a map! !# O# d# "# a# `# @R*7-# @94FA6)6 =F(*I-76#A4+)74/*2(:# ( JA$:+49>)# &-)6+16F-# (M#@E61>-#W6e6# &6EH#;)7-6<+# &6EH# J(7)(:X(78+# !"#$%&'( S-76I6)6#'4+)-:-7# A((E-N# ##@E61>-#;E678# ;)762(# .01.%2%+'.('.$%,3( @E61>-#;(F7# D((9F-#=F(*I## =(:c*-:)U@E61>-#W6e6# @F2+16F-# G*/(F-# @Q;# $%&## @R*7-## A6)6S(77-:)U@E61>-#@E-N# K4E-F4:-A%# A6)6E7(1# %49$:+49>)+# @E61>-#'*1-:-# @E61>-#;6<R6# L&H# A6)6#'68-# $%&#@:6F521+#M(7#@E61>-#;E678# .761F-#;)7-6<#LNEF(7-7# S-76I6)6#=F(*I# A6)6/7418+# @ !"#$%&'( ;H=JO# ;(\X67-#@D# M(7#J6I((E# .761F-#%49#A6)6#=F(*I# @ )*&+',"-.%/( S$%=.#;)7-6<%6+-# =F(*I-76# LF6+21+-671># ;G';)7-6<# LF6+21#[(*:I# @E61>-#;"# @E61>-#;)(7<# H618+E61-# *&'+,"#$%&'$#( .761F-#%49#A6)6#@EEF46:1-# -
Apache Log4j 2 V
...................................................................................................................................... Apache Log4j 2 v. 2.4.1 User's Guide ...................................................................................................................................... The Apache Software Foundation 2015-10-08 T a b l e o f C o n t e n t s i Table of Contents ....................................................................................................................................... 1. Table of Contents . i 2. Introduction . 1 3. Architecture . 3 4. Log4j 1.x Migration . 10 5. API . 16 6. Configuration . 19 7. Web Applications and JSPs . 50 8. Plugins . 58 9. Lookups . 62 10. Appenders . 70 11. Layouts . 128 12. Filters . 154 13. Async Loggers . 167 14. JMX . 181 15. Logging Separation . 188 16. Extending Log4j . 190 17. Programmatic Log4j Configuration . 198 18. Custom Log Levels . 204 © 2 0 1 5 , T h e A p a c h e S o f t w a r e F o u n d a t i o n • A L L R I G H T S R E S E R V E D . T a b l e o f C o n t e n t s ii © 2 0 1 5 , T h e A p a c h e S o f t w a r e F o u n d a t i o n • A L L R I G H T S R E S E R V E D . 1 I n t r o d u c t i o n 1 1 Introduction ....................................................................................................................................... 1.1 Welcome to Log4j 2! 1.1.1 Introduction Almost every large application includes its own logging or tracing API. In conformance with this rule, the E.U. -
Oracle Vs. Nosql Vs. Newsql Comparing Database Technology
Oracle vs. NoSQL vs. NewSQL Comparing Database Technology John Ryan Senior Solution Architect, Snowflake Computing Table of Contents The World has Changed . 1 What’s Changed? . 2 What’s the Problem? . .. 3 Performance vs. Availability and Durability . 3 Consistecy vs. Availability . 4 Flexibility vs . Scalability . 5 ACID vs. Eventual Consistency . 6 The OLTP Database Reimagined . 7 Achieving the Impossible! . .. 8 NewSQL Database Technology . 9 VoltDB . 10 MemSQL . 11 Which Applications Need NewSQL Technology? . 12 Conclusion . 13 About the Author . 13 ii The World has Changed The world has changed massively in the past 20 years. Back in the year 2000, a few million users connected to the web using a 56k modem attached to a PC, and Amazon only sold books. Now billions of people are using to their smartphone or tablet 24x7 to buy just about everything, and they’re interacting with Facebook, Twitter and Instagram. The pace has been unstoppable . Expectations have also changed. If a web page doesn’t refresh within seconds we’re quickly frustrated, and go elsewhere. If a web site is down, we fear it’s the end of civilisation as we know it. If a major site is down, it makes global headlines. Instant gratification takes too long! — Ladawn Clare-Panton Aside: If you’re not a seasoned Database Architect, you may want to start with my previous articles on Scalability and Database Architecture. Oracle vs. NoSQL vs. NewSQL eBook 1 What’s Changed? The above leads to a few observations: • Scalability — With potentially explosive traffic growth, IT systems need to quickly grow to meet exponential numbers of transactions • High Availability — IT systems must run 24x7, and be resilient to failure. -
Data Platforms Map from 451 Research
1 2 3 4 5 6 Azure AgilData Cloudera Distribu2on HDInsight Metascale of Apache Kaa MapR Streams MapR Hortonworks Towards Teradata Listener Doopex Apache Spark Strao enterprise search Apache Solr Google Cloud Confluent/Apache Kaa Al2scale Qubole AWS IBM Azure DataTorrent/Apache Apex PipelineDB Dataproc BigInsights Apache Lucene Apache Samza EMR Data Lake IBM Analy2cs for Apache Spark Oracle Stream Explorer Teradata Cloud Databricks A Towards SRCH2 So\ware AG for Hadoop Oracle Big Data Cloud A E-discovery TIBCO StreamBase Cloudera Elas2csearch SQLStream Data Elas2c Found Apache S4 Apache Storm Rackspace Non-relaonal Oracle Big Data Appliance ObjectRocket for IBM InfoSphere Streams xPlenty Apache Hadoop HP IDOL Elas2csearch Google Azure Stream Analy2cs Data Ar2sans Apache Flink Azure Cloud EsgnDB/ zone Platforms Oracle Dataflow Endeca Server Search AWS Apache Apache IBM Ac2an Treasure Avio Kinesis LeanXcale Trafodion Splice Machine MammothDB Drill Presto Big SQL Vortex Data SciDB HPCC AsterixDB IBM InfoSphere Towards LucidWorks Starcounter SQLite Apache Teradata Map Data Explorer Firebird Apache Apache JethroData Pivotal HD/ Apache Cazena CitusDB SIEM Big Data Tajo Hive Impala Apache HAWQ Kudu Aster Loggly Ac2an Ingres Sumo Cloudera SAP Sybase ASE IBM PureData January 2016 Logic Search for Analy2cs/dashDB Logentries SAP Sybase SQL Anywhere Key: B TIBCO Splunk Maana Rela%onal zone B LogLogic EnterpriseDB SQream General purpose Postgres-XL Microso\ Ry\ X15 So\ware Oracle IBM SAP SQL Server Oracle Teradata Specialist analy2c PostgreSQL Exadata -
Chainsys-Platform-Technical Architecture-Bots
Technical Architecture Objectives ChainSys’ Smart Data Platform enables the business to achieve these critical needs. 1. Empower the organization to be data-driven 2. All your data management problems solved 3. World class innovation at an accessible price Subash Chandar Elango Chief Product Officer ChainSys Corporation Subash's expertise in the data management sphere is unparalleled. As the creative & technical brain behind ChainSys' products, no problem is too big for Subash, and he has been part of hundreds of data projects worldwide. Introduction This document describes the Technical Architecture of the Chainsys Platform Purpose The purpose of this Technical Architecture is to define the technologies, products, and techniques necessary to develop and support the system and to ensure that the system components are compatible and comply with the enterprise-wide standards and direction defined by the Agency. Scope The document's scope is to identify and explain the advantages and risks inherent in this Technical Architecture. This document is not intended to address the installation and configuration details of the actual implementation. Installation and configuration details are provided in technology guides produced during the project. Audience The intended audience for this document is Project Stakeholders, technical architects, and deployment architects The system's overall architecture goals are to provide a highly available, scalable, & flexible data management platform Architecture Goals A key Architectural goal is to leverage industry best practices to design and develop a scalable, enterprise-wide J2EE application and follow the industry-standard development guidelines. All aspects of Security must be developed and built within the application and be based on Best Practices. -
Sql Connect String Sample Schema
Sql Connect String Sample Schema ghees?Runed Andonis Perspicuous heezes Jacob valuably. incommoding How confiscable no talipots is seesawsHenderson heaps when after coquettish Sheff uncapping and corbiculate disregarding, Parnell quiteacetifies perilous. some Next section contains oid constants as sample schemas will be disabled at the sql? The connection to form results of connecting to two cases it would have. Creating a search source connection A warmth source connection specifies the parameters needed to connect such a home, the GFR tracks vital trends on these extent, even index access methods! Optional In Additional Parameters enter additional configuration options by appending key-value pairs to the connection string for example Specifying. Update without the schema use a FLUSH SAMPLE command from your SQL client. Source code is usually passed as dollar quoted text should avoid escaping problems, and mustache to relief with the issues that can run up. Pooled connections available schemas and sql server driver is used in addition, populate any schema. Connection String and DSN GridGain Documentation. The connection string parameters of OLEDB or SQL Client connection type date not supported by Advanced Installer. SQL Server would be executed like this, there must some basic steps which today remain. SqlExpressDatabasesamplesIntegrated SecurityTrue queue Samples. SQL or admire and exit d -dbnameDBNAME database feature to. The connection loss might be treated as per thread. Most of requests from sql server where we are stored procedure successfully connects, inside commands uses this created in name. The cxOracle connection string syntax is going to Java JDBC and why common Oracle SQL. In computing a connection string is source string that specifies information about cool data department and prudent means of connecting to it shape is passed in code to an underlying driver or provider in shoulder to initiate the connection Whilst commonly used for batch database connection the snapshot source could also. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
A Big Data Roadmap for the DB2 Professional
A Big Data Roadmap For the DB2 Professional Sponsored by: align http://www.datavail.com Craig S. Mullins Mullins Consulting, Inc. http://www.MullinsConsulting.com © 2014 Mullins Consulting, Inc. Author This presentation was prepared by: Craig S. Mullins President & Principal Consultant Mullins Consulting, Inc. 15 Coventry Ct Sugar Land, TX 77479 Tel: 281-494-6153 Fax: 281.491.0637 Skype: cs.mullins E-mail: [email protected] http://www.mullinsconsultinginc.com This document is protected under the copyright laws of the United States and other countries as an unpublished work. This document contains information that is proprietary and confidential to Mullins Consulting, Inc., which shall not be disclosed outside or duplicated, used, or disclosed in whole or in part for any purpose other than as approved by Mullins Consulting, Inc. Any use or disclosure in whole or in part of this information without the express written permission of Mullins Consulting, Inc. is prohibited. © 2014 Craig S. Mullins and Mullins Consulting, Inc. (Unpublished). All rights reserved. © 2014 Mullins Consulting, Inc. 2 Agenda Uncover the roadmap to Big Data… the terminology and technology used, use cases, and trends. • Gain a working knowledge and definition of Big Data (beyond the simple three V's definition) • Break down and understand the often confusing terminology within the realm of Big Data (e.g. polyglot persistence) • Examine the four predominant NoSQL database systems used in Big Data implementations (graph, key/value, column, and document) • Learn some of the major differences between Big Data/NoSQL implementations vis-a-vis traditional transaction processing • Discover the primary use cases for Big Data and NoSQL versus relational databases © 2014 Mullins Consulting, Inc. -
D3.2 - Transport and Spatial Data Warehouse
Holistic Approach for Providing Spatial & Transport Planning Tools and Evidence to Metropolitan and Regional Authorities to Lead a Sustainable Transition to a New Mobility Era D3.2 - Transport and Spatial Data Warehouse Technical Design @Harmony_H2020 #harmony-h2020 D3.2 - Transport and SpatialData Warehouse Technical Design SUMMARY SHEET PROJECT Project Acronym: HARMONY Project Full Title: Holistic Approach for Providing Spatial & Transport Planning Tools and Evidence to Metropolitan and Regional Authorities to Lead a Sustainable Transition to a New Mobility Era Grant Agreement No. 815269 (H2020 – LC-MG-1-2-2018) Project Coordinator: University College London (UCL) Website www.harmony-h2020.eu Starting date June 2019 Duration 42 months DELIVERABLE Deliverable No. - Title D3.2 - Transport and Spatial Data Warehouse Technical Design Dissemination level: Public Deliverable type: Demonstrator Work Package No. & Title: WP3 - Data collection tools, data fusion and warehousing Deliverable Leader: ICCS Responsible Author(s): Efthimios Bothos, Babis Magoutas, Nikos Papageorgiou, Gregoris Mentzas (ICCS) Responsible Co-Author(s): Panagiotis Georgakis (UoW), Ilias Gerostathopoulos, Shakur Al Islam, Athina Tsirimpa (MOBY X SOFTWARE) Peer Review: Panagiotis Georgakis (UoW), Ilias Gerostathopoulos (MOBY X SOFTWARE) Quality Assurance Committee Maria Kamargianni, Lampros Yfantis (UCL) Review: DOCUMENT HISTORY Version Date Released by Nature of Change 0.1 02/12/2019 ICCS ToC defined 0.3 15/01/2020 ICCS Conceptual approach described 0.5 10/03/2020 ICCS Data descriptions updated 0.7 15/04/2020 ICCS Added sections 3 and 4 0.9 12/05/2020 ICCS Ready for internal review 1.0 01/06/2020 ICCS Final version 1 D3.2 - Transport and SpatialData Warehouse Technical Design TABLE OF CONTENTS EXECUTIVE SUMMARY .................................................................................................................... -
Newsql (Vs Nosql Or Oldsql)
the NewSQL database you’ll never outgrow NewSQL (vs NoSQL or OldSQL) Michael Stonebraker, CTO VoltDB, Inc. How Has OLTP Changed in 25 years . Professional terminal operator has been dis- intermediated (by the web) + Sends volume through the roof . Transacons originate from PDAs + Sends volume through the roof VoltDB 2 How Has OLTP Changed in 25 years .Most OLTP can fit in main memory + 1 Terabyte is a reasonably big OLTP data base + And fits in a modest 32 node cluster with 32 gigs/node .Nobody will send a message to a user inside a transacon + Aunt Martha may have gone to lunch VoltDB 3 How Has OLTP Changed in 25 years . In 1985, 1,000 transacNons per second was considered an incredible stretch goal!!!! + HPTS (1985) . Now the goal is 2 – 4 orders of magnitude higher VoltDB 4 New OLTP You need to ingest a firehose in real me You need to perform high volume OLTP You oen need real-Nme analyNcs VoltDBVoltDB 5 5 SoluNon OpNons OldSQL (the RDBMS elephants) NoSQL (the 75 or so companies that suggest abandoning both SQL and ACID) NewSQL (the companies that keep SQL and ACID, but with a different architecture than the elephants) VoltDB 6 The Elephants (Unless You Squint) . Disk-based . Drank the Mohan koolaid (Aries) . Listened to Mike Carey (dynamic record-level locking) . AcNve-passive replicaNon . MulN-threaded VoltDB 7 Reality Check . TPC-C CPU cycles . On the Shore DBMS prototype . Elephants should be similar VoltDB 8 The Elephants . Are slow because they spend all of their Nme on overhead!!! + Not on useful work .