1 An Oracle White Paper September 2011 Oracle Utilities Meter Data Management Demonstrates Extreme Performance on Oracle Exadata/Exalogic
2 Introduction New utilities technologies are bringing with them huge increases in data volumes. Residential smart meters alone will increase the consumption data processed for billing purposes from 12 meter reads per year to 35,000 or more. As a consequence, utility business managers and executives are asking: Does hardware and software technology currently exist that is capable of handling the large quantity of data from smart meters? Can today s technology process the data and produce the results needed to drive the meterto-cash process in a timely manner? The tests described in this white paper answer these two questions affirmatively. They demonstrate that Oracle Utilities Meter Data Management (MDM) 2.0.1, running on a full rack Oracle Exadata Database Machine X2-2 and Exalogic X2-2, can easily handle smart metering data for any size of utility. In just one hour, for instance, this hardware/software combination can: Process more than one billion meter reads. 1,2 This approximates the volume of data produced in an hour by 250 million meters recording four interval consumption measurements per hour. 3 1 A meter read may be thought of as an interval or scalar data point that is, a number reported by a meter representing consumption at the end of a given period of time. 2 For this and all results reported throughout this paper, please note that actual results may vary, based on a broad range of implementation-specific factors, such as transaction mix, hardware platform, network parameters, and database size. Oracle does not warrant or guarantee that customers will obtain the same or similar results, even if they use the same or similar equipment and/or software applications. Oracle does not warrant, endorse, or guarantee any performance of any products, any results desired or achieved, or any statements made within this document. 1
3 Calculate four bill determinants for each of 19.3 million bills a total of more than 77 million bill determinants. A bill determinant is defined as being the results of a calculation that produces a customer s consumption for a defined period of time. These numbers far exceed today s typical utility requirements. 3 It is unlikely that utilities would establish an AMI system that relied on interval data recorded on only one channel. Far more common is a scenario in which each meter reports intervals on multiple channels while also providing a check on consumption using a third channel. The scenario tested in this paper is, in fact, one of these more realistic ones; it uses three channels, two reporting 15-minute interval data and one providing a consumption check, for a total of 193 meter reads per day. 2
4 Methodology For this demonstration, MDM 2.0.1, running on a full rack Exadata and a full rack Exalogic, performed two separate tests: It processed meter data and subjected it to validation and correction via configurable validation, editing, and estimation (VEE) rules, storing both the raw and validated/corrected data in the database, where it is available for use and review by business users. It used the validated and corrected meter read data to calculate the bill determinants that a billing / CIS system would use to create a customer bill among many other possible uses. The tests covered two batch use cases, both based on meter reads from 5.5 million interval meters. Each meter had reads on three channels: one interval kwh channel, one interval kvarh channel, and one scalar kwh check channel. Both interval channels measured consumption every 15 minutes. The scalar check channel measured consumption once a day. Thus the total number of reads per meter was 193 per day or 5,790 per 30-day billing period. The purpose of these tests is to validate and document the performance of the MDM VEE and bill determinant processes. The creation of smart meter payloads and insertion into corresponding MDM staging table is not included in these test results. Subsequent tests (including tests of Oracle Utilities Smart Grid Gateway) will include conversion of the head-end-specific raw smart meter payload into the standard MDM format and the loading of data into MDM. The historic data as well as the meter readings were randomly generated using an internal data generation tool to closely emulate production scenarios and realistic distribution of the data. This was necessary to ensure that the database cache hit rate stayed in a realistic range during the benchmark. Test 1: Meter Read Processing. The raw meter data are read, edited, estimated, and stored as final measurements for the subsequent process. This process invokes the relevant set of VEE rules included in MDM, as detailed in Appendix 1. Test 2: Bill Determinant Processing. Validated and corrected meter read data for the 30-day billing period are summarized as billable usage. Measurements from the interval kwh channel (2,880 reads per meter) are mapped to time of use (TOU) quantities based on the TOU map defined in the usage rule in this case, three buckets representing consumption during on-peak, shoulder, and off-peak periods. A fourth bill determinant, obtained by summing the previous three bill determinants, represents total consumption. For more details on the methodology, see Appendix 1. 3
5 Technical Environment The tests were executed on an Oracle Exadata Database Machine X2-2 and Oracle Exalogic X2-2. This constitutes a full rack Exalogic server (30 application server nodes) in the application tier and a full rack Exadata server (8 database server nodes) in the database tier. Exalogic machines consist of Sun Fire X4170 M2 Servers as compute nodes, Sun ZFS Storage 7320 appliances, as well as required InfiniBand and Ethernet networking components. Each of the application servers in the application tier is connected to one of the database servers in the database tier using a round-robin algorithm. For more details on the technical environment, see Appendix 2. Technical Findings MDM running on Exadata and Exalogic exhibits strong horizontal scaling. Both meter read processing and bill determinant processing showed near linear horizontal scalability. In other words, throughput goes up proportionately as nodes (i.e. new servers on an application tier) are added to a system. (For details on horizontal scaling, see Appendix 3.). A compression ratio 4 of 1.8x was achieved for the database structures that store meter read data using hybrid columnar compression (HCC/Query High). The database partitioning strategy used demonstrates that rapid accumulation of large amounts of historical data does not have to affect Oracle application scalability. This is a key benefit of Exadata, which features highly tuned and balanced elements of computing, storage and networking that adapts and scale predictably. Business Findings These tests demonstrate that Oracle Utilities Meter Data Management 2.0.1, running on a full rack Exadata X2-2 and a full rack Exalogic X2-2 server: Validated, edited, estimated, and stored initial measurement data for smart meters at a rate of 1 billion meter reads per hour. Responded to 19.3 million requests for bill determinants with a total of 77.2 million bill determinants calculated in an hour. 4 Subsequent to these tests, in a separate study, compression ratio as high as 10x been achieved for the table structure that stores meter read data. 4
6 Furthermore, as Appendix 3 illustrates in detail, the measured throughput of 1 billion meter reads per hour was achieved using 24 compute nodes on Exalogic. Had all 30 compute nodes been used, MDM and the full rack Exalogic would have achieved much higher throughput, conservatively estimated at 1.4 billion per hour. Furthermore, as Appendix 3 illustrates in detail, the maximum measured bill determinant processing throughput of 19.3 million usage transactions per hour was limited by CPU resources on the Exadata hardware. Had there been more Exadata racks available, the full rack Exalogic could have conservatively achieved a throughput of 40 million usage transactions per hour. 5
7 Appendix 1: Additional Details on Methodology Historic Data The tests were conducted using 3 months of historical measurements for the 5.5 million meters. The historic data was randomly generated using a data generation tool to closely emulate production scenarios and realistic distributions of the data. This ensured that the database cache hit rate stayed in a realistic range during the tests. Testing For the measurement data processing test (i.e. applying the VEE rules), each test load consisted of slightly more than one billion meter reads. 5 For the bill determinant calculation test, the billing period was set at one month (30 days). Each request for bill determinants resulted in the aggregation of consumption measured in the kwh interval channels. The resulting 2,880 meter reads for each meter s kwh interval channel were processed into four bill determinants: the first three representing total consumption during the month for each of three time-of-use billing periods and a fourth a sum of the first three represented total. Note that for processing purposes, the tests grouped raw data into units of Initial Measurement Data (IMD). Each IMD consists of all the meter reads in a single channel for a single day. Each meter in these tests produced three IMDs per day. Note that IMDs are not identical in terms of the number of meter reads contained in each. For the tests described in this paper, the IMDs for each of the two interval channels contain 96 meter reads each, while the IMDs for the kwh scalar check channel contain only one meter read each. There are a total of 193 (96 kwh interval + 96 kvarh interval + 1 kwh scalar) reads per smart device (meter) million meters x 193 daily meter reads per meter = billion meter reads. 6
8 The following table represents the data volumes used to determine the workload profile for the tests. ENTITY TYPE NUMBER COMMENTS Meters 5,500,000 Service Point 5,500,000 Channels 16,500,000 Three channels per meter 1 kwh interval channel (15 minute), 1 kvarh interval channel (15 minute) and 1 kwh scalar check channel. Initial Measurement Data 1,485,000,000 Total number of channels x 30 days in the month x 3 months Final Measurements 95,535,000,000 Row counts in the database at the end of the tests. Repeated test runs may add data. VEE Rules VEE RULES FOR INTERVAL INITIAL MEASUREMENT DATA PROCESSING RULE UOM Check Interval Size Validation Spike Check Interval Interpolation 1 Interval Interpolation 2 Interval Interpolation 3 Interval Averaging Replacement Rule Sum Check Negative Consumption Check Hi Low Check DESCRIPTION This VEE rule checks the unit of measure (UOM) passed in with the Initial Measurement against the primary unit of measure configured on the measuring component's type This VEE rule algorithm validates that the seconds per interval (SPI) supplied with the Initial Measurement is equal to the interval size defined on measuring component's type. This VEE rule algorithm looks for spikes by taking the highest interval and the third-highest interval, and determining the percent difference between the two. This VEE rule attempts to interpolate gaps in Initial Measurement Data sets using prior and subsequent intervals as starting points for linear interpolation. This VEE rule attempts to interpolate gaps in Initial Measurement Data sets using prior and subsequent intervals as starting points for linear interpolation. This VEE rule attempts to interpolate gaps in Initial Measurement Data sets using prior and subsequent intervals as starting points for linear interpolation. This VEE rule performs estimation of missing consumption by aggregating MC's consumption history - the average consumption of which becomes the estimated amount. This VEE rule algorithm checks if the intervals in an Interval Initial Measurement will replace existing final measurements. This rule evaluates whether consumption for the current Initial Measurement Data is within a tolerance of the sum of the consumption during the same period for any measuring components related to the current one. A VEE rule using this algorithm logs a VEE exception using the exception type and severity configured on the rule if the total consumption (as calculated by summing all measurements from Initial Measurement Data) is less than zero. This VEE rule performs validation of measurements using the historical measurement data 7
9 VEE RULES FOR SCALAR INITIAL MEASUREMENT DATA (CONSUMPTION CHECK) PROCESSING RULE UOM Check Multiplier Check Replacement Rule Negative Consumption Hi Low Check DESCRIPTION This VEE rule checks the unit of measure (UOM) passed in with the Initial Measurement against the primary unit of measure configured on the measuring component's type This VEE rule validates that the register multiplier supplied with the Initial Measurement is equal to the multiplier stored on the measuring component. This VEE rule checks if there exists a final measurement with a date/time equal to the date/time in the current initial measurement being validated. A VEE rule using this algorithm logs a VEE exception using the exception type and severity configured on the rule if the total consumption (as calculated by summing all measurements from Initial Measurement Data) is less than zero. This VEE rule performs validation of measurements using the historical measurement data USAGE RULES FOR BILL DETERMINANT CALCULATION RULE Get TOU Mapped Usage Service Quantity Math DESCRIPTION This usage rule is used to get time of use quantities from interval measuring components installed in the service points linked to the usage subscription for the specified 'Interval' usage period. Only measuring components that match the UOM/SQI defined in the usage rule instance are processed. Measurements within the period are mapped to time of use quantities based on the TOU map defined in the usage rule. This rule is for deriving the total consumption by summing each TOU period. Validate Against Tolerance This will validate the total consumption during ON Peak to be less than 500,000 8
10 Appendix 2: Technical Environment Below is a high-level architecture topology diagram. Exadata For the Exadata configuration, the main database table for storing meter readings and usage was partitioned, and the data for that table was stored using Oracle Secure Files (an Oracle Database Enterprise Edition Advanced Compression feature) with the compression level set to medium. The following table further defines the specifications of the database and the host servers. DATABASE SERVERS/STORAGE SYSTEM Platform Exadata X2-2 Full Rack, Sunfire X4170M2 Operating System Oracle Enterprise Linux 5 64 bit O/S Version and Release el5 9
11 Database Software / Version Oracle Database Server 11gR2 with Automatic Storage Management (ASM) Number of CPUs 8 RAC nodes, each with 2 Sockets (12 Cores) Processor /CPU Speed Intel Xeon X5670 / 2.93GHz Memory 96 GB Storage System 14 Exadata Storage Servers, each with 12 High Performance SAS disks and 384 GB Smart Flash Cache Raid Level ASM 2-way normal mirror. Note that Exadata Storage Servers provide a high-bandwidth, massively parallel solution delivering up to 75 gigabytes per second of raw I/O bandwidth and up to 1,500,000 I/O operations per second (IOPS). These significant performance increases derive from the use of Exadata Smart Flash Cache in each Exadata Storage Server and hierarchical optimization of the Oracle Database. Exalogic For the Exalogic configuration, each Exalogic machine was connected to the database server via a tengigabit Ethernet interface. The following table outlines the application operating system and hardware specifications. One application server was used for the vertical scalability testing; two application servers were used for the horizontal scalability testing. (See Appendix for results of these tests.) APPLICATION/BATCH MIDDLE TIER Platform Exalogic X2-2 full Rack, Sunfire X4170 M2 Operating System Oracle Enterprise Linux 5 64 bit O/S Version and Release el5 Software / Version Oracle Utilities Meter Data Management version Weblogic 10.0 MP2 64 bit # CPU 30 compute nodes, each node has 2 Sockets and 12 Cores (Total 360 cores). Processor / CPU / Speed 2.93GHz Memory 96 GB on each compute node Partitioning Strategy The database storage data structures that keep high volume raw and finalized meter data reads were partitioned by the date-time of the readings. Furthermore, these structures were sub-partitioned by their primary access key. All associated index structures were created as local indexes to the partitions 10
12 and sub-partitions while making sure that the critical use cases contained the partitioning key during query and DML operations against these structures. This partitioning strategy not only helps by making query and DML operational performance on these data structures independent of the duration of the historical transactions to be kept for online access, it also helps by enabling archiving of this large volume of data in timely and efficient manner. 11
13 Appendix 3: Tests of Horizontal Scalability Horizontal scalability (i.e. scale out) is defined as the ability of an application to demonstrate higher throughput when more nodes are added to a system, such as adding a new server on the database tier. For these tests, horizontal scalability was measured by starting with 8 compute nodes and increasing the number (in increments of 8) up to a total of 24 compute nodes, while capturing a performance benchmark data point on each increment. Horizontal scalability was tested until the point at which the CPU on the application server or database tier was saturated. In the case of the usage transaction / bill determinant calculations, the scalability was limited by database CPU. The charts below depict the scalability characteristics of Oracle Utilities Meter Data Management v2.0.1, as measured during the tests. For the horizontal scalability evaluation tests, the execution threads were uniformly distributed across all the application servers. Horizontal scalability chart for interval read validate & load for Exalogic X2-2 compute node Throughput shown above is "Interval Read Validate & Load" per hour. CPU utilization shown here is the average CPU utilization per node. 12
14 Horizontal scalability chart for billing for Exalogic X2-2 compute notes Throughput shown above is expressed as "UTs/Hour (i.e. Usage Transaction requests per hour), where each usage transaction requested results in the production of four bill determinants. The extrapolated throughput for full rack Exalogic X2-2 with sufficient Exadata is estimated to be approximately 40 million usage transactions per hour. Both data processing and bill determinant processing, showed near linear scalability (horizontal scalability). Note: Horizontal scalability on the database tiers was not measured for the following reasons: Exadata is a database machine made of multiple RAC nodes that communicate with storage servers. Each RAC node communicates with all of the storage servers. Thus, even when only one of the RAC nodes is used, it still uses all the available storage servers. 13
15 Oracle Utilities Meter Data Management Demonstrates Extreme Performance on Oracle Exadata/Exalogic September 2011 Oracle Corporation World Headquarters 500 Oracle Parkway Redwood Shores, CA U.S.A. Worldwide Inquiries: Phone: Fax: Copyright 2011, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only and the contents hereof are subject to change without notice. This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. OUMDM_bmExs_0911 oracle.com
Oracle Database 12c: JMS Sharded Queues For high performance, scalable Advanced Queuing ORACLE WHITE PAPER MARCH 2015 Table of Contents Introduction 2 Architecture 3 PERFORMANCE OF AQ-JMS QUEUES 4 PERFORMANCE
Automatic Data Optimization with Oracle Database 12c O R A C L E W H I T E P A P E R S E P T E M B E R 2 0 1 7 Table of Contents Disclaimer 1 Introduction 2 Storage Tiering and Compression Tiering 3 Heat
Oracle Exadata Statement of Direction NOVEMBER 2017 Disclaimer The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated
An Oracle White Paper January 2013 Oracle Hyperion Planning on the Oracle Database Appliance using Oracle Transparent Data Encryption Executive Overview... 3 Introduction... 3 Hyperion Planning... 3 Oracle
Hybrid Columnar Compression (HCC) on Oracle Database 18c O R A C L E W H IT E P A P E R FE B R U A R Y 2 0 1 8 Disclaimer The following is intended to outline our general product direction. It is intended
An Oracle White Paper September 2010 Handling Memory Ordering in Multithreaded Applications with Oracle Solaris Studio 12 Update 2: Part 2, Memory Introduction... 1 What Is Memory Ordering?... 2 More About
An Oracle Technical White Paper October 2011 Sizing Guide for Single Click Configurations of Oracle s MySQL on Sun Fire x86 Servers Introduction... 1 Foundation for an Enterprise Infrastructure... 2 Sun
Rapid Bottleneck Identification A Better Way to do Load Testing An Oracle White Paper June 2008 Rapid Bottleneck Identification A Better Way to do Load Testing. RBI combines a comprehensive understanding
An Oracle White Paper: November 2011 Installation Instructions: Oracle XML DB XFILES Demonstration Table of Contents Installation Instructions: Oracle XML DB XFILES Demonstration... 1 Executive Overview...
An Oracle White Paper September, 2011 Oracle Real User Experience Insight Server Requirements Executive Overview Oracle Enterprise Manager is Oracle s integrated enterprise IT management product line and
New Oracle NoSQL Database APIs that Speed Insertion and Retrieval O R A C L E W H I T E P A P E R F E B R U A R Y 2 0 1 6 1 NEW ORACLE NoSQL DATABASE APIs that SPEED INSERTION AND RETRIEVAL Introduction
An Oracle Technical White Paper September 2012 Detecting and Resolving Oracle Solaris LUN Alignment Problems Overview... 1 LUN Alignment Challenges with Advanced Storage Devices... 2 Detecting and Resolving
Bulk Processing with Oracle Application Integration Architecture An Oracle White Paper January 2009 Bulk Processing with Oracle Application Integration Architecture Introduction... 3 Oracle Application
An Oracle White Paper September 2009 Methods for Upgrading to Oracle Database 11g Release 2 Introduction... 1 Database Upgrade Methods... 2 Database Upgrade Assistant (DBUA)... 2 Manual Upgrade... 3 Export
Oracle Data Provider for.net Microsoft.NET Core and Entity Framework Core O R A C L E S T A T E M E N T O F D I R E C T I O N F E B R U A R Y 2 0 1 8 Disclaimer The following is intended to outline our
An Oracle White Paper May 2014 Integrating Oracle SuperCluster Engineered Systems with a Data Center s 1 GbE and 10 GbE s Using Oracle Switch ES1-24 Introduction... 1 Integrating Oracle SuperCluster T5-8
An Oracle White Paper September 2010 Upgrade Methods for Upgrading to Oracle Database 11g Release 2 Introduction... 1 Database Upgrade Methods... 2 Database Upgrade Assistant (DBUA)... 2 Manual Upgrade...
An Oracle White Paper August 2010 Building Highly Scalable Web Applications with XStream Disclaimer The following is intended to outline our general product direction. It is intended for information purposes
Implementing Fibre Channel SAN Boot with the Oracle ZFS Storage Appliance January 2014 By Tom Hanvey; update by Peter Brouwer Version: 2.0 This paper describes how to implement a Fibre Channel (FC) SAN
An Oracle White Paper February 2012 Benefits of an Exclusive Multimaster Deployment of Oracle Directory Server Enterprise Edition Disclaimer The following is intended to outline our general product direction.
User Count O R A C L E E - B U S I N E S S B E N C H M A R K R EV. 1.0 E-BUSINESS SUITE APPLICATIONS R12 (R12.1.3) HR (OLTP) BENCHMARK - USING ORACLE DATABASE 11g ON FUJITSU S M10-4S SERVER RUNNING SOLARIS
An Oracle White Paper December 2010 Accelerating Deployment of Virtualized Infrastructures with the Oracle VM Blade Cluster Reference Configuration Introduction...1 Overview of the Oracle VM Blade Cluster
An Oracle White Paper October 2010 Minimizing Planned Downtime of SAP Systems with the Virtualization Technologies in Oracle Solaris 10 Introduction When business-critical systems are down for a variety
RAC Database on Oracle Ravello Cloud Service O R A C L E W H I T E P A P E R A U G U S T 2017 Oracle Ravello is an overlay cloud that enables enterprises to run their VMware and KVM applications with data-center-like
Migrating VMs from VMware vsphere to Oracle Private Cloud Appliance 2.3.1 O R A C L E W H I T E P A P E R O C T O B E R 2 0 1 7 Table of Contents Introduction 2 Environment 3 Install Coriolis VM on Oracle
Oracle Database Lite Automatic Synchronization White Paper An Oracle White Paper August 2008 Oracle Database Lite Automatic Synchronization White Paper OVERVIEW Oracle Database Lite allows field workers
An Oracle White Paper January 2014 Oracle Forms Services Oracle Traffic Director Configuration Introduction... 1 What is Oracle Traffic Director... 2 Requirements... 3 WebLogic Server... 3 Oracle Forms
Oracle Financial Consolidation and Close Cloud What s New in the December Update (16.12) December 2016 TABLE OF CONTENTS REVISION HISTORY... 3 ORACLE FINANCIAL CONSOLIDATION AND CLOSE CLOUD, DECEMBER UPDATE...
An Oracle White Paper September 2012 Security and the Oracle Database Cloud Service 1 Table of Contents Overview... 3 Security architecture... 4 User areas... 4 Accounts... 4 Identity Domains... 4 Database
Storage Optimization with Oracle Database 11g Terabytes of Data Reduce Storage Costs by Factor of 10x Data Growth Continues to Outpace Budget Growth Rate of Database Growth 1000 800 600 400 200 1998 2000
Oracle Enterprise Data Quality 126.96.36.199 New Features Overview Integrated Profiling, New Data Services, New Processors O R A C L E W H I T E P A P E R J U L Y 2 0 1 6 Table of Contents Executive Overview
An Oracle White Paper November 2009 Oracle RAC One Node 11g Release 2 User Guide Introduction... 1 Software Installation... 3 How to Configure an Oracle RAC One Node Database... 6 Rolling Patch Application
An Oracle White Paper December, 3 rd 2014 Oracle Metadata Management v188.8.131.52.1 Oracle Metadata Management version 184.108.40.206.1 - December, 3 rd 2014 Disclaimer This document is for informational purposes.
Was ist dran an einer spezialisierten Data Warehousing platform? Hermann Bär Oracle USA Redwood Shores, CA Schlüsselworte Data warehousing, Exadata, specialized hardware proprietary hardware Introduction
An Oracle White Paper June 2011 Oracle Service Registry - Oracle Enterprise Gateway Integration Guide 1 / 19 Disclaimer The following is intended to outline our general product direction. It is intended
Oracle Grid Infrastructure 12c Release 2 Cluster Domains O R A C L E W H I T E P A P E R N O V E M B E R 2 0 1 7 Table of Contents Introduction 2 Clustering with Oracle Clusterware 12c Release 2 3 Oracle
An Oracle White Paper September, 2009 Oracle Database 11g for Data Warehousing and Business Intelligence Introduction Oracle Database 11g is a comprehensive database platform for data warehousing and business
ORACLE EXADATA DATABASE MACHINE X2-8 FEATURES AND FACTS FEATURES 160 CPU cores and 4 TB of memory for database processing 168 CPU cores dedicated to SQL processing in storage 2 database servers 14 Oracle
Oracle WebLogic Server Multitenant: The World s First Cloud-Native Enterprise Java Platform KEY BENEFITS Enable container-like DevOps and 12-factor application management and delivery Accelerate application
Siebel CRM Applications on Oracle Ravello Cloud Service ORACLE WHITE PAPER AUGUST 2017 Oracle Ravello is an overlay cloud that enables enterprises to run their VMware and KVM applications with data-center-like
Oracle Utilities CC&B V2.3.1 and MDM V2.0.1 Integrations Utility Reference Model 5.6.4 Synchronize Master Data October 2011 Oracle Utilities CC&B V2.3.1 to MDM V2.0.1 Integration Utility Reference Model
An Oracle Technical White Paper September 2010 Oracle VM Templates for PeopleSoft 1 Disclaimer The following is intended to outline our general product direction. It is intended for information purposes
October 2017 Oracle Application Express Statement of Direction Disclaimer This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle.
Oracle Web Service Manager 11g Component Level Role Authorization (in SOA Suite) March, 2012 Step-by-Step Instruction Guide Author: Prakash Yamuna Senior Development Manager Oracle Corporation Table of
Oracle Fusion Middleware Getting Started with Oracle Data Integrator 12c Virtual Machine Installation Guide July 2017 Oracle Fusion Middleware Getting Started with Oracle Data Integrator, 12c Copyright
Oracle Big Data SQL Release 3.2 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
Increasing Network Agility through Intelligent Orchestration The Oracle Communications Application Orchestrator advantage ORACLE WHITE PAPER OCTOBER 2015 Table of Contents Introduction 1 Network Virtualization
Solution-in-a-box: Deploying Oracle FLEXCUBE v12.1 on Oracle Database Appliance Virtualized Platform ORACLE WHITE PAPER JULY 2016 Table of Contents Executive Overview 2 Scope 2 Introduction 3 The Benefits
April 2013 Understanding Federated Single Sign-On (SSO) Process Understanding Federated Single Sign-On Process (SSO) Disclaimer The following is intended to outline our general product direction. It is
An Oracle White Paper March 2009 Introduction to Groovy Support in JDeveloper and Oracle ADF 11g Oracle White Paper Introduction to Groovy support in JDeveloper and Oracle ADF 11g Introduction... 2 Introduction
Oracle Data Masking and Subsetting Frequently Asked Questions (FAQ) S E P T E M B E R 2 0 1 6 Product Overview Q: What is Data Masking and Subsetting? A: Data Masking or Static Data Masking is the process
An Oracle White Paper Oct 2014 Hard Partitioning With Oracle Solaris Zones Introduction This document describes hard partitioning with Oracle Solaris Zones (also known as Oracle Solaris Containers), and
PeopleSoft Fluid Navigation Standards Fluid User Experience ORACLE WHITE PAPER OCTOBER 2015 Disclaimer The following is intended to outline our general product direction. It is intended for information
Information Lifecycle Management for Business Data An Oracle White Paper September 2005 Information Lifecycle Management for Business Data Introduction... 3 Regulatory Requirements... 3 What is ILM?...
Hyper-converged storage for Oracle RAC based on NVMe SSDs and standard x86 servers White Paper rev. 2016-05-18 2015-2016 FlashGrid Inc. 1 www.flashgrid.io Abstract Oracle Real Application Clusters (RAC)
Oracle s Netra Modular System A Product Concept Introduction Table of Contents Table of Contents 1 Introduction 2 Blades Versus Rackmount Servers 3 Traditional Server Architectures Merged 3 Plug-and-Play
An Oracle White Paper February 2010 Comprehensive Testing for Siebel With Oracle Application Testing Suite Introduction Siebel provides a wide range of business-critical applications for Sales, Marketing,
Oracle Database Appliance X7-2 Model Family MANAGING ORACLE DATABASE WITH A PURPOSE BUILT SYSTEM ORACLE WHITE PAPER OCTOBER 2017 Introduction The Oracle Database Appliance, introduced in 2011, is an Oracle
Oracle Financial Consolidation and Close Cloud What s New in the February Update (17.02) February 2017 TABLE OF CONTENTS REVISION HISTORY... 3 ORACLE FINANCIAL CONSOLIDATION AND CLOSE CLOUD, FEBRUARY UPDATE...
An Oracle White Paper July 2011 Oracle WebCenter Portal: Copying a Runtime-Created Skin to a Portlet Producer Introduction This white paper describes a method for copying runtime-created skins from a WebCenter
StorageTek ACSLS Manager Software Management of distributed tape libraries is both time-consuming and costly involving multiple libraries, multiple backup applications, multiple administrators, and poor
An Oracle White Paper October 2013 Deploying and Developing Oracle Application Express with Oracle Database 12c Disclaimer The following is intended to outline our general product direction. It is intended
Differentiate Your Business with Oracle PartnerNetwork Specialized. Recognized by Oracle. Preferred by Customers. Joining Oracle PartnerNetwork differentiates your business, connects you with customers,
SUN ZFS STORAGE APPLIANCE DELIVERING BEST-IN-CLASS PERFORMANCE, EFFICIENCY, AND ORACLE INTEGRATION KEY FEATURES Advanced, intuitive management tools Real-time analysis and diagnosis of performance Oracle
Oracle Database Vault DBA Administrative Best Practices ORACLE WHITE PAPER MAY 2015 Table of Contents Introduction 2 Database Administration Tasks Summary 3 General Database Administration Tasks 4 Managing
SUN ZFS STORAGE APPLIANCE EASY DATA MANAGEMENT REAL TIME ANALYSIS MAXIMUM STORAGE EFFICIENCY KEY FEATURES Advanced, intuitive management tools Real-time analysis and diagnosis of performance Oracle s Sun
An Oracle White Paper September 2010 Handling Memory Ordering in Multithreaded Applications with Oracle Solaris Studio 12 Update 2: Part 1, Compiler Introduction... 1 What Is Memory Ordering?... 2 Compiler
Oracle Cloud Infrastructure Virtual Cloud Network Overview and Deployment Guide ORACLE WHITEPAPER JANUARY 2018 VERSION 1.0 Table of Contents Purpose of this Whitepaper 1 Scope & Assumptions 1 Virtual Cloud
Oracle Clusterware 12c Release 2 Technical Overview O R A C L E W H I T E P A P E R M A R C H 2 0 1 7 Table of Contents Introduction 1 Cluster Domains 2 Standalone Cluster 2 Cluster Domains 2 Node Weighting
An Oracle Technical Article August 2017 Certification with Oracle Linux 7 Oracle Technical Article Certification with Oracle Linux 7 Introduction... 1 Comparing Oracle Linux 7 and Red Hat Enterprise Linux
ORACLE FABRIC MANAGER MANAGE CONNECTIVITY IN REAL TIME KEY BENEFITS Control connectivity across servers from a single screen. Instantly replicate connectivity configurations across a group of servers with
Deploy VPN IPSec Tunnels on Oracle Cloud Infrastructure White Paper September 2017 Version 1.0 Disclaimer The following is intended to outline our general product direction. It is intended for information
An Oracle White Paper June 2011 Oracle Virtual Directory 11g Oracle Enterprise Gateway Integration Guide 1 / 25 Disclaimer The following is intended to outline our general product direction. It is intended
Implementing Information Lifecycle Management (ILM) with Oracle Database 18c O R A C L E W H I T E P A P E R F E B R U A R Y 2 0 1 8 Disclaimer 1 Introduction 2 Information Lifecycle Management 3 Automating
Oracle Communications Interactive Session Recorder and Broadsoft Broadworks Interoperability Testing Technical Application Note Disclaimer The following is intended to outline our general product direction.
Oracle Privileged Account Manager Disaster Recovery Deployment Considerations O R A C L E W H I T E P A P E R A U G U S T 2 0 1 5 Disclaimer The following is intended to outline our general product direction.
An Oracle White Paper April 2014 How to Use Tape Tiering Accelerator (Automatically Linked Partition) Introduction... 1 New Host Interface Commands... 3 Locate ALP... 3 Set ALP Mode... 3 Set Writable ALPs...
Oracle Warehouse Builder 10g Release 2 Integrating Packaged Applications Data June 2006 Note: This document is for informational purposes. It is not a commitment to deliver any material, code, or functionality,
Your consent to our cookies if you continue to use this website.