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  • Live Event: OTN Architect Day: Cloud Computing - Two weeks and counting

    - by Bob Rhubart
    In just two weeks architects and others will gather at the Oracle Conference Center in Redwood Shores, CA for the first Oracle Technology Network Architect Day event of 2013. This event focuses on Cloud Computing, and features sessions specifically focused on real-world examples of the implementation of cloud computing. When: Tuesday July 9, 2013              8:30am - 12:30pm Where: Oracle Conference Center              350 Oracle Pkwy              Redwood City, CA 94065 Register now. It's free! Here's the agenda: 8:30am - 9:00am Registration and Continental Breakfast 9:00am - 9:45am Keynote 21st Century IT | Dr. James Baty VP, Global Enterprise Architecture Program, Oracle Imagine a time long, long ago. A time when servers were certified and dedicated to specific applications, when anything posted on an enterprise web site was from restricted, approved channels, and when we tried to limit the growth of 'dirty' data and storage. Today, applications are services running in the muti-tenant hybrid cloud. Companies beg their customers to tweet them, friend them, and publicly rate their products. And constantly analyzing a deluge of Internet, social and sensor data is the key to creating the next super-successful product, or capturing an evil terrorist. The old IT architecture was planned, dedicated, stable, controlled, with separate and well-defined roles. The new architecture is shared, dynamic, continuous, XaaS, DevOps. This keynote session describes the challenges and opportunities that the new business / IT paradigms present to the IT architecture and architects. 9:45am - 10:30am Technical Session Oracle Cloud: A Case Study in Building a Cloud | Anbu Krishnaswami Enterprise Architect, Oracle Building a Cloud can be challenging thanks to the complex requirements unique to Cloud computing and the massive scale typically associated with Cloud. Cloud providers can take an Infrastructure as a Service (IaaS) approach and build a cloud on virtualized commodity hardware, or they can take the Platform as a Service (PaaS) path, a service-oriented approach based on pre-configured, integrated, engineered systems. This presentation uses the Oracle Cloud itself as a case study in the use of engineered systems, demonstrating how the technical design of engineered systems is leveraged for building PaaS and SaaS Cloud services and a Cloud management infrastructure. The presentation will also explore the principles, patterns, best practices, and architecture views provided in Oracle's Cloud reference architecture. 10:30 am -10:45 am Break 10:45am-11:30am Technical Session Database as a Service | Michael Timpanaro-Perrotta Director, Product Management, Oracle Database Cloud New applications are now commonly built in a Cloud model, where the database is consumed as a service, and many established business processes are beginning to migrate to database as a service (DBaaS). This adoption of DBaaS is made possible by the availability of new capabilities in the database that enable resource pooling, dynamic resource management, model-based provisioning, metered use, and effective quality-of-service controls. This session will examine the catalog of database services at a large commercial bank to understand how these capabilities are enabling DBaaS for a wide range of needs within the enterprise. 11:30 am - 12:00 pm Panel Q&A Dr. James Baty, Anbu Krishnaswami, and Michael Timpanaro-Perrotta respond to audience questions. Registration is free, but seating is limited, so register now.

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  • Live Event: OTN Architect Day: Cloud Computing - Two weeks and counting

    - by Bob Rhubart
    In just two weeks architects and others will gather at the Oracle Conference Center in Redwood Shores, CA for the first Oracle Technology Network Architect Day event of 2013. This event focuses on Cloud Computing, and features sessions specifically focused on real-world examples of the implementation of cloud computing. When: Tuesday July 9, 2013              8:30am - 12:30pm Where: Oracle Conference Center              350 Oracle Pkwy              Redwood City, CA 94065 Register now. It's free! Here's the agenda: 8:30am - 9:00am Registration and Continental Breakfast 9:00am - 9:45am Keynote 21st Century IT | Dr. James Baty VP, Global Enterprise Architecture Program, Oracle Imagine a time long, long ago. A time when servers were certified and dedicated to specific applications, when anything posted on an enterprise web site was from restricted, approved channels, and when we tried to limit the growth of 'dirty' data and storage. Today, applications are services running in the muti-tenant hybrid cloud. Companies beg their customers to tweet them, friend them, and publicly rate their products. And constantly analyzing a deluge of Internet, social and sensor data is the key to creating the next super-successful product, or capturing an evil terrorist. The old IT architecture was planned, dedicated, stable, controlled, with separate and well-defined roles. The new architecture is shared, dynamic, continuous, XaaS, DevOps. This keynote session describes the challenges and opportunities that the new business / IT paradigms present to the IT architecture and architects. 9:45am - 10:30am Technical Session Oracle Cloud: A Case Study in Building a Cloud | Anbu Krishnaswami Enterprise Architect, Oracle Building a Cloud can be challenging thanks to the complex requirements unique to Cloud computing and the massive scale typically associated with Cloud. Cloud providers can take an Infrastructure as a Service (IaaS) approach and build a cloud on virtualized commodity hardware, or they can take the Platform as a Service (PaaS) path, a service-oriented approach based on pre-configured, integrated, engineered systems. This presentation uses the Oracle Cloud itself as a case study in the use of engineered systems, demonstrating how the technical design of engineered systems is leveraged for building PaaS and SaaS Cloud services and a Cloud management infrastructure. The presentation will also explore the principles, patterns, best practices, and architecture views provided in Oracle's Cloud reference architecture. 10:30 am -10:45 am Break 10:45am-11:30am Technical Session Database as a Service | Michael Timpanaro-Perrotta Director, Product Management, Oracle Database Cloud New applications are now commonly built in a Cloud model, where the database is consumed as a service, and many established business processes are beginning to migrate to database as a service (DBaaS). This adoption of DBaaS is made possible by the availability of new capabilities in the database that enable resource pooling, dynamic resource management, model-based provisioning, metered use, and effective quality-of-service controls. This session will examine the catalog of database services at a large commercial bank to understand how these capabilities are enabling DBaaS for a wide range of needs within the enterprise. 11:30 am - 12:00 pm Panel Q&A Dr. James Baty, Anbu Krishnaswami, and Michael Timpanaro-Perrotta respond to audience questions. Registration is free, but seating is limited, so register now.

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  • Why We Do What We Do. (Part 3 of 5 Part Series on JDE 5G Postponed)

    - by Kem Butller-Oracle
    By Lyle Ekdahl - Oracle JD Edwards Sr. VP General Manager  In the closing of part two of this 5 part blog series, I stated that in the next installment I would explore the expected results of the digital overdrive era and the impact it will have on our economy. While I have full intentions of writing on that topic, I am inspired today to write about something that is top of mind. It’s top of mind because it has come up several times recently conversations with my Oracle’s JD Edwards team members, with customers and our partners, plus I feel passionately about why I do what I do…. It is not what we do but why we do that thing that we do Do you know what you do? For the most part, I bet you could tell me what you do even if your work has changed over the years.  My real question is, “Do you get excited about what you do, and are you fulfilled? Does your work deliver a sense of purpose, a cause to work for, and something to believe in?”  Alright, I guess that was not a single question. So let me just ask, “Why?” Why are you here, right now? Why do you get up in the morning? Why do you go to work? Of course, I can’t answer those questions for you but I can share with you my POV.   For starters, there are several things that drive me. As many of you know by now, I have a somewhat competitive nature but it is not solely the thrill of winning that actually fuels me. Now don’t get me wrong, I do like winning occasionally. However winning is only a potential result of competing and is clearly not guaranteed. So why compete? Why compete in business, and particularly why in this Enterprise Software business?  Here’s why! I am fascinated by creative and building processes. It is about making or producing things, causing something to come into existence. With the right skill, imagination and determination, whether it’s art or invention; the result can deliver value and inspire. In both avocation and vocation I always gravitate towards the create/build processes.  I believe one of the skills necessary for the create/build process is not just the aptitude but also, and especially, the desire and attitude that drives one to gain a deeper understanding. The more I learn about our customers, the more I seek to understand what makes the successful and what difficult issues cause them to struggle. I like to look for the complex, non-commodity process problems where streamlined design and modern technology can provide an easy and simple solution. It is especially gratifying to see our customers use our software to increase their own ability to deliver value to the market. What an incredible network effect! I know many of you share this customer obsession as well as the create/build addiction focused on simple and elegant design. This is what I believe is at the root of our common culture.  Are JD Edwards customers on a whole different than other ERP solutions’ customers? I would argue that for the most part, yes, they are. They selected our software, and our software is different. Why? Because I believe that the create/build process will generally result in solutions that reflect who built it and their culture. And a culture of people focused on why they create/build will attract different customers than one that is based on what is built or how the solution is delivered. In the past I have referred to this idea as character of the customer, and it transcends industry, size and run rate. Now some would argue that JD Edwards has some customers who are characters. But that is for a different post. As I have told you before, the JD Edwards culture is unique, and its resulting economy is valuable and deserving of our best efforts. 

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  • The battle between Java vs. C#

    The battle between Java vs. C# has been a big debate amongst the development community over the last few years. Both languages have specific pros and cons based on the needs of a particular project. In general both languages utilize a similar coding syntax that is based on C++, and offer developers similar functionality. This being said, the communities supporting each of these languages are very different. The divide amongst the communities is much like the political divide in America, where the Java community would represent the Democrats and the .Net community would represent the Republicans. The Democratic Party is a proponent of the working class and the general population. Currently, Java is deeply entrenched in the open source community that is distributed freely to anyone who has an interest in using it. Open source communities rely on developers to keep it alive by constantly contributing code to make applications better; essentially they develop code by the community. This is in stark contrast to the C# community that is typically a pay to play community meaning that you must pay for code that you want to use because it is developed as products to be marketed and sold for a profit. This ties back into my reference to the Republicans because they typically represent the needs of business and personal responsibility. This is emphasized by the belief that code is a commodity and that it can be sold for a profit which is in direct conflict to the laissez-faire beliefs of the open source community. Beyond the general differences between Java and C#, they also target two different environments. Java is developed to be environment independent and only requires that users have a Java virtual machine running in order for the java code to execute. C# on the other hand typically targets any system running a windows operating system and has the appropriate version of the .Net Framework installed. However, recently there has been push by a segment of the Open source community based around the Mono project that lets C# code run on other non-windows operating systems. In addition, another feature of C# is that it compiles into an intermediate language, and this is what is executed when the program runs. Because C# is reduced down to an intermediate language called Common Language Runtime (CLR) it can be combined with other languages that are also compiled in to the CLR like Visual Basic (VB) .Net, and F#. The allowance and interaction between multiple languages in the .Net Framework enables projects to utilize existing code bases regardless of the actual syntax because they can be compiled in to CLR and executed as one codebase. As a software engineer I personally feel that it is really important to learn as many languages as you can or at least be open to learn as many languages as you can because no one language will work in every situation.  In some cases Java may be a better choice for a project and others may be C#. It really depends on the requirements of a project and the time constraints. In addition, I feel that is really important to concentrate on understanding the logic of programming and be able to translate business requirements into technical requirements. If you can understand both programming logic and business requirements then deciding which language to use is just basically choosing what syntax to write for a given business problem or need. In regards to code refactoring and dynamic languages it really does not matter. Eventually all projects will be refactored or decommissioned to allow for progress. This is the way of life in the software development industry. The language of a project should not be chosen based on the fact that a project will eventually be refactored because they all will get refactored.

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  • Where can I find free and open data?

    - by kitsune
    Sooner or later, coders will feel the need to have access to "open data" in one of their projects, from knowing a city's zip to a more obscure information such as the axial tilt of Pluto. I know data.un.org which offers access to the UN's extensive array of databases that deal with human development and other socio-economic issues. The other usual suspects are NASA and the USGS for planetary data. There's an article at readwriteweb with more links. infochimps.org seems to stand out. Personally, I need to find historic commodity prices, stock values and other financial data. All these data sets seem to cost money however. Clarification To clarify, I'm interested in all kinds of open data, because sooner or later, I know I will be in a situation where I could need it. I will try to edit this answer and include the suggestions in a structured manners. A link for financial data was hidden in that readwriteweb article, doh! It's called opentick.com. Looks good so far! Update I stumbled over semantic data in another question of mine on here. There is opencyc ('the world's largest and most complete general knowledge base and commonsense reasoning engine'). A project called UMBEL provides a light-weight, distilled version of opencyc. Umbel has semantic data in rdf/owl/skos n3 syntax. The Worldbank also released a very nice API. It offers data from the last 50 years for about 200 countries

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  • Frameworks to manage dates (effective date and expiry dates)

    - by user214626
    Hello, We have an object that can has an effective date and expiry date.(Ex. i want to maintain the price of a commodity for a time period) Business Rules - Effective date is always a valid date (a datestamp) but, expiry date can be null to indicate that the object is active throughout. Also, both effective and expiry date can be set to some valid dates. Are there any frameworks that manage objects such that the objects are consistent,i.e there are no overlaps of the validity periods ? Ex. class XBOX { double price; Date effectiveDate; Date expiryDate; } XBOX x1 = new XBOX(400$, '2007-01-01','2008-12-31' ); XBOX x2 = new XBOX(200$, '2009-01-01',null ); Assume that we get a new rate from '2010-01-01' and a new XBOX object has to be created (to persist). Is there a framework/pattern that can do the following, so that the XBOX is consistent. x2.setExpiryDate('2009-12-31') XBOX x3 = new XBOX(150$, '2010-01-01',null ); Thanks in advance.

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  • Memory Bandwidth Performance for Modern Machines

    - by porgarmingduod
    I'm designing a real-time system that occasionally has to duplicate a large amount of memory. The memory consists of non-tiny regions, so I expect the copying performance will be fairly close to the maximum bandwidth the relevant components (CPU, RAM, MB) can do. This led me to wonder what kind of raw memory bandwidth modern commodity machine can muster? My aging Core2Duo gives me 1.5 GB/s if I use 1 thread to memcpy() (and understandably less if I memcpy() with both cores simultaneously.) While 1.5 GB is a fair amount of data, the real-time application I'm working on will have have something like 1/50th of a second, which means 30 MB. Basically, almost nothing. And perhaps worst of all, as I add multiple cores, I can process a lot more data without any increased performance for the needed duplication step. But a low-end Core2Due isn't exactly hot stuff these days. Are there any sites with information, such as actual benchmarks, on raw memory bandwidth on current and near-future hardware? Furthermore, for duplicating large amounts of data in memory, are there any shortcuts, or is memcpy() as good as it will get? Given a bunch of cores with nothing to do but duplicate as much memory as possible in a short amount of time, what's the best I can do?

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  • Avoiding seasonality assumption for stl() or decompose() in R

    - by user303922
    Hello everybody, I have high frequency commodity price data that I need to analyze. My objective is to not assume any seasonal component and just identify a trend. Here is where I run into problems with R. There are two main functions that I know of to analyze this time series: decompose() and stl(). The problem is that they both take a ts object type with a frequency parameter greater than or equal to 2. Is there some way I can assume a frequency of 1 per unit time and still analyze this time series using R? I'm afraid that if I assume frequency greater than 1 per unit time, and seasonality is calculated using the frequency parameter, then my forecasts are going to depend on that assumption. names(crude.data)=c('Date','Time','Price') names(crude.data) freq = 2 win.graph() plot(crude.data$Time,crude.data$Price, type="l") crude.data$Price = ts(crude.data$Price,frequency=freq) I want frequency to be 1 per unit time but then decompose() and stl() don't work! dim(crude.data$Price) decom = decompose(crude.data$Price) win.graph() plot(decom$random[2:200],type="line") acf(decom$random[freq:length(decom$random-freq)]) Thank you.

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  • Do RAID controllers commonly have SATA drive brand compatibility issues?

    - by Jeff Atwood
    We've struggled with the RAID controller in our database server, a Lenovo ThinkServer RD120. It is a rebranded Adaptec that Lenovo / IBM dubs the ServeRAID 8k. We have patched this ServeRAID 8k up to the very latest and greatest: RAID bios version RAID backplane bios version Windows Server 2008 driver This RAID controller has had multiple critical BIOS updates even in the short 4 month time we've owned it, and the change history is just.. well, scary. We've tried both write-back and write-through strategies on the logical RAID drives. We still get intermittent I/O errors under heavy disk activity. They are not common, but serious when they happen, as they cause SQL Server 2008 I/O timeouts and sometimes failure of SQL connection pools. We were at the end of our rope troubleshooting this problem. Short of hardcore stuff like replacing the entire server, or replacing the RAID hardware, we were getting desperate. When I first got the server, I had a problem where drive bay #6 wasn't recognized. Switching out hard drives to a different brand, strangely, fixed this -- and updating the RAID BIOS (for the first of many times) fixed it permanently, so I was able to use the original "incompatible" drive in bay 6. On a hunch, I began to assume that the Western Digital SATA hard drives I chose were somehow incompatible with the ServeRAID 8k controller. Buying 6 new hard drives was one of the cheaper options on the table, so I went for 6 Hitachi (aka IBM, aka Lenovo) hard drives under the theory that an IBM/Lenovo RAID controller is more likely to work with the drives it's typically sold with. Looks like that hunch paid off -- we've been through three of our heaviest load days (mon,tue,wed) without a single I/O error of any kind. Prior to this we regularly had at least one I/O "event" in this time frame. It sure looks like switching brands of hard drive has fixed our intermittent RAID I/O problems! While I understand that IBM/Lenovo probably tests their RAID controller exclusively with their own brand of hard drives, I'm disturbed that a RAID controller would have such subtle I/O problems with particular brands of hard drives. So my question is, is this sort of SATA drive incompatibility common with RAID controllers? Are there some brands of drives that work better than others, or are "validated" against particular RAID controller? I had sort of assumed that all commodity SATA hard drives were alike and would work reasonably well in any given RAID controller (of sufficient quality).

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  • Seriously, It’s Time to Get Your Content Act Together

    - by Mike Stiles
    Branded content, content marketing, social content, brand journalism, we’re seeing those terms more and more. Why? The technology tools are coming together. We should know. We can gather big data, crunch it, listen to the public, moderate, respond, get to know the customer intimately, know what they like, know what they want, we can target, distribute, amplify, measure engagement and reaction, modify strategy and even automate a great deal of all that. An amazing machine, a sleek, smooth-running engine has been built such that all the parts can interact and work together to deliver peak performance and maximum output. But that engine isn’t going anywhere without any gas. Content is the gas. Yes, we curate other people’s content. We can siphon their gas. There’s tech to help with that too. But as for the creation of original, worthwhile content made for a specific audience, our audience, machines can’t do that…at least not yet. Curated content is great. But somebody has to originate the content for it to be curated and shared. And since the need for good, curated content is obviously large and the desire to share is there, it’s a winning proposition for a brand to be a consistent producer of original content. And yet, it feels like content is an issue we’re avoiding. There’s a reluctance to build a massive pipeline if you have no idea what you’re going to run through it. The C-suite often doesn’t know what content is, that it’s different from ads, where to get it, who makes it, how long it should be, what the point of it is if there’s no hard sell of the product, what it costs, how to use it, how to measure it, how to make sure it’s good, or how to make sure it will keep flowing. It could be the reason many brands aren’t pulling the trigger on socially enabling the enterprise. And that’s a shame, because there are a lot of creative, daring, experimental, uniquely talented entertainers and journalists chomping at the bit to execute content for brands. But for many corporate executives, content is “weird,” and the people who make it are even weirder. The content side of the equation is human. It’s art, but art that can be informed by data. The natural inclination is for brands to turn to their agencies for such creative endeavors. But agencies are falling into one of two categories. They’re failing to transition from ads to content. In “Content Era, What’s the Role of Agencies?” Alexander Jutkowitz says agencies were made for one-hit campaigns, not ongoing content. Or, they’re ready and capable but can’t get clients to do the right things. Agencies have to make money, even if it means continuing to do the wrong things because that’s all the client will agree to. So what we wind up with in the pipeline is advertising, marketing-heavy content, content that was obviously created or spearheaded by non-creative executives, random & inconsistent content, copy written for SEO bots, and other completely uninteresting nightmares. Frank Rose, author of “The Art of Immersion,” writes, “Content without story and excitement is noise pollution.” In the old days, you made an ad and inserted it into shows made by people who knew what they were doing. You could bask in that show’s success and leverage their audience. Now, you are tasked with attracting, amassing and holding your own audience. You may just want to make, advertise and sell your widgets. But now there’s a war on for a precious commodity, attention. People are busy. They have filters to keep uninteresting and irrelevant things out. They value their time and expect value back when they give it up. Joe Pulizzi, founder of the Content Marketing Institute, says, "Your customers don't care about you, your products, your services…they care about themselves, their wants and their needs." Is it worth getting serious about content and doing it right? 61% of consumers feel better about a company that delivers custom content (Custom Content Council). Interesting content is one of the top 3 reasons people follow brands on social (Content+). 78% of consumers think organizations that provide custom content want to build good relationships with them (TMG Custom Media). On the B2B side, 80% of business decision makers prefer to get company info in a series of articles vs. an ad. So what’s the hang-up? Cited barriers to content marketing are lack of human resources (42%) and lack of budget (35%). 54% of brands don’t have a single on-site, dedicated content creator. And only 38% of brands have a content marketing strategy. Tech has built the biggest, most incredible stage for brands that’s ever been built. Putting something on that stage is your responsibility. Do a bad show, or no show at all, and you’ll be the beautiful, talented actress that never got discovered. @mikestilesPhoto: Gabriella Fabbri, stock.xchng

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  • Forcing an External Activation with Service Broker

    - by Davide Mauri
    In these last days I’ve been working quite a lot with Service Broker, a technology I’m really happy to work with, since it can give a lot of satisfaction. The scale-out solution one can easily build is simply astonishing. I’m helping a company to build a very scalable and – yet almost inexpensive – invoicing system that has to be able to scale out using commodity hardware. To offload the work from the main server to satellite “compute nodes” (yes, I’ve borrowed this term from PDW) we’re using Service Broker and the External Activator application available in the SQL Server Feature Pack. For those who are not used to work with SSB, the External Activation is a feature that allows you to intercept the arrival of a message in a queue right from your application code. http://msdn.microsoft.com/en-us/library/ms171617.aspx (Look for “Event-Based Activation”) In order to make life even more easier, Microsoft released the External Activation application that saves you even from writing even this code. http://blogs.msdn.com/b/sql_service_broker/archive/tags/external+activator/ The External Activator application can be configured to execute your own application so that each time a message – an invoice in my case – arrives in the target queue, the invoking application is executed and the invoice is calculated. The very nice feature of External Activator is that it can automatically execute as many configured application in order to process as many messages as your system can handle.  This also a lot of create a scale-out solution, leaving to the developer only a fraction of the problems that usually came with asynchronous programming. Developers are also shielded from Service Broker since everything can be encapsulated in Stored Procedures, so that – for them – developing such scale-out asynchronous solution is not much more complex than just executing a bunch of Stored Procedures. Now, if everything works correctly, you don’t have to bother of anything else. You put messages in the queue and your application, invoked by the External Activator, process them. But what happen if for some reason your application fails to process the messages. For examples, it crashes? The message is safe in the queue so you just need to process it again. But your application is invoked by the External Activator application, so now the question is, how do you wake up that app? Service Broker will engage the activation process only if certain conditions are met: http://msdn.microsoft.com/en-us/library/ms171601.aspx But how we can invoke the activation process manually, without having to wait for another message to arrive (the arrival of a new message is a condition that can fire the activation process)? The “trick” is to do manually with the activation process does: sending a system message to a queue in charge of handling External Activation messages: declare @conversationHandle uniqueidentifier; declare @n xml = N' <EVENT_INSTANCE>   <EventType>QUEUE_ACTIVATION</EventType>   <PostTime>' + CONVERT(CHAR(24),GETDATE(),126) + '</PostTime>   <SPID>' + CAST(@@SPID AS VARCHAR(9)) + '</SPID>   <ServerName>[your_server_name]</ServerName>   <LoginName>[your_login_name]</LoginName>   <UserName>[your_user_name]</UserName>   <DatabaseName>[your_database_name]</DatabaseName>   <SchemaName>[your_queue_schema_name]</SchemaName>   <ObjectName>[your_queue_name]</ObjectName>   <ObjectType>QUEUE</ObjectType> </EVENT_INSTANCE>' begin dialog conversation     @conversationHandle from service        [<your_initiator_service_name>] to service          '<your_event_notification_service>' on contract         [http://schemas.microsoft.com/SQL/Notifications/PostEventNotification] with     encryption = off,     lifetime = 6000 ; send on conversation     @conversationHandle message type     [http://schemas.microsoft.com/SQL/Notifications/EventNotification] (@n) ;     end conversation @conversationHandle; That’s it! Put the code in a Stored Procedure and you can add to your application a button that says “Force Queue Processing” (or something similar) in order to start the activation process whenever you need it (which should not occur too frequently but it may happen). PS I know that the “fire-and-forget” (ending the conversation without waiting for an answer) technique is not a best practice, but in this case I don’t see how it can hurts so I decided to stay very close to the KISS principle []

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  • NoSQL Memcached API for MySQL: Latest Updates

    - by Mat Keep
    With data volumes exploding, it is vital to be able to ingest and query data at high speed. For this reason, MySQL has implemented NoSQL interfaces directly to the InnoDB and MySQL Cluster (NDB) storage engines, which bypass the SQL layer completely. Without SQL parsing and optimization, Key-Value data can be written directly to MySQL tables up to 9x faster, while maintaining ACID guarantees. In addition, users can continue to run complex queries with SQL across the same data set, providing real-time analytics to the business or anonymizing sensitive data before loading to big data platforms such as Hadoop, while still maintaining all of the advantages of their existing relational database infrastructure. This and more is discussed in the latest Guide to MySQL and NoSQL where you can learn more about using the APIs to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database The native Memcached API is part of the MySQL 5.6 Release Candidate, and is already available in the GA release of MySQL Cluster. By using the ubiquitous Memcached API for writing and reading data, developers can preserve their investments in Memcached infrastructure by re-using existing Memcached clients, while also eliminating the need for application changes. Speed, when combined with flexibility, is essential in the world of growing data volumes and variability. Complementing NoSQL access, support for on-line DDL (Data Definition Language) operations in MySQL 5.6 and MySQL Cluster enables DevOps teams to dynamically update their database schema to accommodate rapidly changing requirements, such as the need to capture additional data generated by their applications. These changes can be made without database downtime. Using the Memcached interface, developers do not need to define a schema at all when using MySQL Cluster. Lets look a little more closely at the Memcached implementations for both InnoDB and MySQL Cluster. Memcached Implementation for InnoDB The Memcached API for InnoDB is previewed as part of the MySQL 5.6 Release Candidate. As illustrated in the following figure, Memcached for InnoDB is implemented via a Memcached daemon plug-in to the mysqld process, with the Memcached protocol mapped to the native InnoDB API. Figure 1: Memcached API Implementation for InnoDB With the Memcached daemon running in the same process space, users get very low latency access to their data while also leveraging the scalability enhancements delivered with InnoDB and a simple deployment and management model. Multiple web / application servers can remotely access the Memcached / InnoDB server to get direct access to a shared data set. With simultaneous SQL access, users can maintain all the advanced functionality offered by InnoDB including support for Foreign Keys, XA transactions and complex JOIN operations. Benchmarks demonstrate that the NoSQL Memcached API for InnoDB delivers up to 9x higher performance than the SQL interface when inserting new key/value pairs, with a single low-end commodity server supporting nearly 70,000 Transactions per Second. Figure 2: Over 9x Faster INSERT Operations The delivered performance demonstrates MySQL with the native Memcached NoSQL interface is well suited for high-speed inserts with the added assurance of transactional guarantees. You can check out the latest Memcached / InnoDB developments and benchmarks here You can learn how to configure the Memcached API for InnoDB here Memcached Implementation for MySQL Cluster Memcached API support for MySQL Cluster was introduced with General Availability (GA) of the 7.2 release, and joins an extensive range of NoSQL interfaces that are already available for MySQL Cluster Like Memcached, MySQL Cluster provides a distributed hash table with in-memory performance. MySQL Cluster extends Memcached functionality by adding support for write-intensive workloads, a full relational model with ACID compliance (including persistence), rich query support, auto-sharding and 99.999% availability, with extensive management and monitoring capabilities. All writes are committed directly to MySQL Cluster, eliminating cache invalidation and the overhead of data consistency checking to ensure complete synchronization between the database and cache. Figure 3: Memcached API Implementation with MySQL Cluster Implementation is simple: 1. The application sends reads and writes to the Memcached process (using the standard Memcached API). 2. This invokes the Memcached Driver for NDB (which is part of the same process) 3. The NDB API is called, providing for very quick access to the data held in MySQL Cluster’s data nodes. The solution has been designed to be very flexible, allowing the application architect to find a configuration that best fits their needs. It is possible to co-locate the Memcached API in either the data nodes or application nodes, or alternatively within a dedicated Memcached layer. The benefit of this flexible approach to deployment is that users can configure behavior on a per-key-prefix basis (through tables in MySQL Cluster) and the application doesn’t have to care – it just uses the Memcached API and relies on the software to store data in the right place(s) and to keep everything synchronized. Using Memcached for Schema-less Data By default, every Key / Value is written to the same table with each Key / Value pair stored in a single row – thus allowing schema-less data storage. Alternatively, the developer can define a key-prefix so that each value is linked to a pre-defined column in a specific table. Of course if the application needs to access the same data through SQL then developers can map key prefixes to existing table columns, enabling Memcached access to schema-structured data already stored in MySQL Cluster. Conclusion Download the Guide to MySQL and NoSQL to learn more about NoSQL APIs and how you can use them to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database See how to build a social app with MySQL Cluster and the Memcached API from our on-demand webinar or take a look at the docs Don't hesitate to use the comments section below for any questions you may have 

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  • Forcing an External Activation with Service Broker

    - by Davide Mauri
    In these last days I’ve been working quite a lot with Service Broker, a technology I’m really happy to work with, since it can give a lot of satisfaction. The scale-out solution one can easily build is simply astonishing. I’m helping a company to build a very scalable and – yet almost inexpensive – invoicing system that has to be able to scale out using commodity hardware. To offload the work from the main server to satellite “compute nodes” (yes, I’ve borrowed this term from PDW) we’re using Service Broker and the External Activator application available in the SQL Server Feature Pack. For those who are not used to work with SSB, the External Activation is a feature that allows you to intercept the arrival of a message in a queue right from your application code. http://msdn.microsoft.com/en-us/library/ms171617.aspx (Look for “Event-Based Activation”) In order to make life even more easier, Microsoft released the External Activation application that saves you even from writing even this code. http://blogs.msdn.com/b/sql_service_broker/archive/tags/external+activator/ The External Activator application can be configured to execute your own application so that each time a message – an invoice in my case – arrives in the target queue, the invoking application is executed and the invoice is calculated. The very nice feature of External Activator is that it can automatically execute as many configured application in order to process as many messages as your system can handle.  This also a lot of create a scale-out solution, leaving to the developer only a fraction of the problems that usually came with asynchronous programming. Developers are also shielded from Service Broker since everything can be encapsulated in Stored Procedures, so that – for them – developing such scale-out asynchronous solution is not much more complex than just executing a bunch of Stored Procedures. Now, if everything works correctly, you don’t have to bother of anything else. You put messages in the queue and your application, invoked by the External Activator, process them. But what happen if for some reason your application fails to process the messages. For examples, it crashes? The message is safe in the queue so you just need to process it again. But your application is invoked by the External Activator application, so now the question is, how do you wake up that app? Service Broker will engage the activation process only if certain conditions are met: http://msdn.microsoft.com/en-us/library/ms171601.aspx But how we can invoke the activation process manually, without having to wait for another message to arrive (the arrival of a new message is a condition that can fire the activation process)? The “trick” is to do manually with the activation process does: sending a system message to a queue in charge of handling External Activation messages: declare @conversationHandle uniqueidentifier; declare @n xml = N' <EVENT_INSTANCE>   <EventType>QUEUE_ACTIVATION</EventType>   <PostTime>' + CONVERT(CHAR(24),GETDATE(),126) + '</PostTime>   <SPID>' + CAST(@@SPID AS VARCHAR(9)) + '</SPID>   <ServerName>[your_server_name]</ServerName>   <LoginName>[your_login_name]</LoginName>   <UserName>[your_user_name]</UserName>   <DatabaseName>[your_database_name]</DatabaseName>   <SchemaName>[your_queue_schema_name]</SchemaName>   <ObjectName>[your_queue_name]</ObjectName>   <ObjectType>QUEUE</ObjectType> </EVENT_INSTANCE>' begin dialog conversation     @conversationHandle from service        [<your_initiator_service_name>] to service          '<your_event_notification_service>' on contract         [http://schemas.microsoft.com/SQL/Notifications/PostEventNotification] with     encryption = off,     lifetime = 6000 ; send on conversation     @conversationHandle message type     [http://schemas.microsoft.com/SQL/Notifications/EventNotification] (@n) ;     end conversation @conversationHandle; That’s it! Put the code in a Stored Procedure and you can add to your application a button that says “Force Queue Processing” (or something similar) in order to start the activation process whenever you need it (which should not occur too frequently but it may happen). PS I know that the “fire-and-forget” (ending the conversation without waiting for an answer) technique is not a best practice, but in this case I don’t see how it can hurts so I decided to stay very close to the KISS principle []

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  • Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Cloud in the Big Data Story. In this article we will understand the role of Operational Databases Supporting Big Data Story. Even though we keep on talking about Big Data architecture, it is extremely crucial to understand that Big Data system can’t just exist in the isolation of itself. There are many needs of the business can only be fully filled with the help of the operational databases. Just having a system which can analysis big data may not solve every single data problem. Real World Example Think about this way, you are using Facebook and you have just updated your information about the current relationship status. In the next few seconds the same information is also reflected in the timeline of your partner as well as a few of the immediate friends. After a while you will notice that the same information is now also available to your remote friends. Later on when someone searches for all the relationship changes with their friends your change of the relationship will also show up in the same list. Now here is the question – do you think Big Data architecture is doing every single of these changes? Do you think that the immediate reflection of your relationship changes with your family member is also because of the technology used in Big Data. Actually the answer is Facebook uses MySQL to do various updates in the timeline as well as various events we do on their homepage. It is really difficult to part from the operational databases in any real world business. Now we will see a few of the examples of the operational databases. Relational Databases (This blog post) NoSQL Databases (This blog post) Key-Value Pair Databases (Tomorrow’s post) Document Databases (Tomorrow’s post) Columnar Databases (The Day After’s post) Graph Databases (The Day After’s post) Spatial Databases (The Day After’s post) Relational Databases We have earlier discussed about the RDBMS role in the Big Data’s story in detail so we will not cover it extensively over here. Relational Database is pretty much everywhere in most of the businesses which are here for many years. The importance and existence of the relational database are always going to be there as long as there are meaningful structured data around. There are many different kinds of relational databases for example Oracle, SQL Server, MySQL and many others. If you are looking for Open Source and widely accepted database, I suggest to try MySQL as that has been very popular in the last few years. I also suggest you to try out PostgreSQL as well. Besides many other essential qualities PostgreeSQL have very interesting licensing policies. PostgreSQL licenses allow modifications and distribution of the application in open or closed (source) form. One can make any modifications and can keep it private as well as well contribute to the community. I believe this one quality makes it much more interesting to use as well it will play very important role in future. Nonrelational Databases (NOSQL) We have also covered Nonrelational Dabases in earlier blog posts. NoSQL actually stands for Not Only SQL Databases. There are plenty of NoSQL databases out in the market and selecting the right one is always very challenging. Here are few of the properties which are very essential to consider when selecting the right NoSQL database for operational purpose. Data and Query Model Persistence of Data and Design Eventual Consistency Scalability Though above all of the properties are interesting to have in any NoSQL database but the one which most attracts to me is Eventual Consistency. Eventual Consistency RDBMS uses ACID (Atomicity, Consistency, Isolation, Durability) as a key mechanism for ensuring the data consistency, whereas NonRelational DBMS uses BASE for the same purpose. Base stands for Basically Available, Soft state and Eventual consistency. Eventual consistency is widely deployed in distributed systems. It is a consistency model used in distributed computing which expects unexpected often. In large distributed system, there are always various nodes joining and various nodes being removed as they are often using commodity servers. This happens either intentionally or accidentally. Even though one or more nodes are down, it is expected that entire system still functions normally. Applications should be able to do various updates as well as retrieval of the data successfully without any issue. Additionally, this also means that system is expected to return the same updated data anytime from all the functioning nodes. Irrespective of when any node is joining the system, if it is marked to hold some data it should contain the same updated data eventually. As per Wikipedia - Eventual consistency is a consistency model used in distributed computing that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. In other words -  Informally, if no additional updates are made to a given data item, all reads to that item will eventually return the same value. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Reference Data Management and Master Data: Are Relation ?

    - by Mala Narasimharajan
    Submitted By:  Rahul Kamath  Oracle Data Relationship Management (DRM) has always been extremely powerful as an Enterprise Master Data Management (MDM) solution that can help manage changes to master data in a way that influences enterprise structure, whether it be mastering chart of accounts to enable financial transformation, or revamping organization structures to drive business transformation and operational efficiencies, or restructuring sales territories to enable equitable distribution of leads to sales teams following the acquisition of new products, or adding additional cost centers to enable fine grain control over expenses. Increasingly, DRM is also being utilized by Oracle customers for reference data management, an emerging solution space that deserves some explanation. What is reference data? How does it relate to Master Data? Reference data is a close cousin of master data. While master data is challenged with problems of unique identification, may be more rapidly changing, requires consensus building across stakeholders and lends structure to business transactions, reference data is simpler, more slowly changing, but has semantic content that is used to categorize or group other information assets – including master data – and gives them contextual value. In fact, the creation of a new master data element may require new reference data to be created. For example, when a European company acquires a US business, chances are that they will now need to adapt their product line taxonomy to include a new category to describe the newly acquired US product line. Further, the cross-border transaction will also result in a revised geo hierarchy. The addition of new products represents changes to master data while changes to product categories and geo hierarchy are examples of reference data changes.1 The following table contains an illustrative list of examples of reference data by type. Reference data types may include types and codes, business taxonomies, complex relationships & cross-domain mappings or standards. Types & Codes Taxonomies Relationships / Mappings Standards Transaction Codes Industry Classification Categories and Codes, e.g., North America Industry Classification System (NAICS) Product / Segment; Product / Geo Calendars (e.g., Gregorian, Fiscal, Manufacturing, Retail, ISO8601) Lookup Tables (e.g., Gender, Marital Status, etc.) Product Categories City à State à Postal Codes Currency Codes (e.g., ISO) Status Codes Sales Territories (e.g., Geo, Industry Verticals, Named Accounts, Federal/State/Local/Defense) Customer / Market Segment; Business Unit / Channel Country Codes (e.g., ISO 3166, UN) Role Codes Market Segments Country Codes / Currency Codes / Financial Accounts Date/Time, Time Zones (e.g., ISO 8601) Domain Values Universal Standard Products and Services Classification (UNSPSC), eCl@ss International Classification of Diseases (ICD) e.g., ICD9 à IC10 mappings Tax Rates Why manage reference data? Reference data carries contextual value and meaning and therefore its use can drive business logic that helps execute a business process, create a desired application behavior or provide meaningful segmentation to analyze transaction data. Further, mapping reference data often requires human judgment. Sample Use Cases of Reference Data Management Healthcare: Diagnostic Codes The reference data challenges in the healthcare industry offer a case in point. Part of being HIPAA compliant requires medical practitioners to transition diagnosis codes from ICD-9 to ICD-10, a medical coding scheme used to classify diseases, signs and symptoms, causes, etc. The transition to ICD-10 has a significant impact on business processes, procedures, contracts, and IT systems. Since both code sets ICD-9 and ICD-10 offer diagnosis codes of very different levels of granularity, human judgment is required to map ICD-9 codes to ICD-10. The process requires collaboration and consensus building among stakeholders much in the same way as does master data management. Moreover, to build reports to understand utilization, frequency and quality of diagnoses, medical practitioners may need to “cross-walk” mappings -- either forward to ICD-10 or backwards to ICD-9 depending upon the reporting time horizon. Spend Management: Product, Service & Supplier Codes Similarly, as an enterprise looks to rationalize suppliers and leverage their spend, conforming supplier codes, as well as product and service codes requires supporting multiple classification schemes that may include industry standards (e.g., UNSPSC, eCl@ss) or enterprise taxonomies. Aberdeen Group estimates that 90% of companies rely on spreadsheets and manual reviews to aggregate, classify and analyze spend data, and that data management activities account for 12-15% of the sourcing cycle and consume 30-50% of a commodity manager’s time. Creating a common map across the extended enterprise to rationalize codes across procurement, accounts payable, general ledger, credit card, procurement card (P-card) as well as ACH and bank systems can cut sourcing costs, improve compliance, lower inventory stock, and free up talent to focus on value added tasks. Change Management: Point of Sales Transaction Codes and Product Codes In the specialty finance industry, enterprises are confronted with usury laws – governed at the state and local level – that regulate financial product innovation as it relates to consumer loans, check cashing and pawn lending. To comply, it is important to demonstrate that transactions booked at the point of sale are posted against valid product codes that were on offer at the time of booking the sale. Since new products are being released at a steady stream, it is important to ensure timely and accurate mapping of point-of-sale transaction codes with the appropriate product and GL codes to comply with the changing regulations. Multi-National Companies: Industry Classification Schemes As companies grow and expand across geographies, a typical challenge they encounter with reference data represents reconciling various versions of industry classification schemes in use across nations. While the United States, Mexico and Canada conform to the North American Industry Classification System (NAICS) standard, European Union countries choose different variants of the NACE industry classification scheme. Multi-national companies must manage the individual national NACE schemes and reconcile the differences across countries. Enterprises must invest in a reference data change management application to address the challenge of distributing reference data changes to downstream applications and assess which applications were impacted by a given change. References 1 Master Data versus Reference Data, Malcolm Chisholm, April 1, 2006.

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  • I, Android

    - by andrewbrust
    I’m just back from the 2011 Consumer Electronics Show (CES).  I go to CES to get a sense of what Microsoft is doing in the consumer space, and how people are reacting to it.  When I first went to CES 2 years ago, Steve Ballmer announced the beta of Windows 7 at his keynote address, and the crowd went wild.  When I went again last year, everyone was hoping for a Windows tablet announcement at the Ballmer keynote.  Although they didn’t get one (unless you count the unreleased HP Slate running Windows 7), people continued to show anticipation around Project Natal (which became Xbox 360 Kinect) and around Windows Phone 7.  On the show floor last year, there were machines everywhere running Windows 7, including lots of netbooks.  Microsoft had a serious influence at the show both years. But this year, one brand, one product, one operating system evidenced itself over and over again: Android.  Whether in the multitude of tablet devices that were shown across the show, or the burgeoning number of smartphones shown (including all four forthcoming 4G-LTE handsets at Verizon Wireless’ booth) or the Google TV set top box from Logitech and the embedded implementation in new Sony TV models, Android was was there. There was excitement in the ubiquity of Android 2.2 (Froyo) and the emergence of Android 2.3 (Gingerbread).  There was anticipation around the tablet-optimized Android 3.0 (Honeycomb).  There were highly customized skins.  There was even an official CES Android app for navigating the exhibit halls and planning events.  Android was so ubiquitous, in fact, that it became surprising to find a device that was running anything else.  It was as if Android had become the de facto Original Equipment Manufacturing (OEM) operating system. Motorola’s booth was nothing less than an Android showcase.  And it was large, and it was packed.  Clearly Moto’s fortunes have improved dramatically in the last year and change.  The fact that the company morphed from being a core Windows Mobile OEM to an Android poster child seems non-coincidental to their improved fortunes. Even erstwhile WinMo OEMs who now do produce Windows Phone 7 devices were not pushing them.  Perhaps I missed them, but I couldn’t find WP7 handsets at Samsung’s booth, nor at LG’s.  And since the only carrier exhibiting at the show was Verizon Wireless, which doesn’t yet have WP7 devices, this left Microsoft’s booth as the only place to see the phones. Why is Android so popular with consumer electronics manufacturers in Japan, South Korea, China and Taiwan?  Yes, it’s free, but there’s more to it than that.  Android seems to have succeeded as an OEM OS because it’s directed at OEMs who are permitted to personalize it and extend it, and it provides enough base usability and touch-friendliness that OEMs want it.  In the process, it has become a de facto standard (which makes OEMs want it even more), and has done so in a remarkably short time: the OS was launched on a single phone in the US just 2 1/4 years ago. Despite its success and popularity, Apple’s iOS would never be used by OEMs, because it’s not meant to be embedded and customized, but rather to provide a fully finished experience.  Ironically, Windows Phone 7 is likewise disqualified from such embedded use.  Windows Mobile (6.x and earlier) may have been a candidate had it not atrophied so much in its final 5 years of life. What can Microsoft do?  It could start by developing a true touch-centric OS for tablets, whether that be within Windows 8, or derived from Windows Phone 7.  It would then need to deconstruct that finished product into components, via a new or altered version of Windows Embedded or Windows Embedded Compact.  And if Microsoft went that far, it would only make sense to work with its OEMs and mobile carriers to make certain they showcase their products using the OS at CES, and other consumer electronics venues, prominently. Mostly though, Microsoft would need to decide if it were really committed to putting sustained time, effort and money into a commodity product, especially given the far greater financial return that it now derives from its core Windows and Office franchises. Microsoft would need to see an OEM OS for what it is: a loss leader that helps build brand and platform momentum for up-level products.  Is that enough to make the investment worthwhile?  One thing is certain: if that question is not acknowledged and answered honestly, then any investment will be squandered.

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  • Easing the Journey to the Private Cloud with Oracle Consulting

    - by MichaelM-Oracle
    By Sanjai Marimadaiah, Senior Director, Strategy & Business Development – Cloud Solutions, Oracle Consulting Services Business leaders are now leading the charge on how their firms can profit from cloud solutions. Agility and innovation are becoming the primary drivers of the business case for the cloud, even more than the anticipated cost savings. Leaders need to find the right strategy and optimize the use of cloud-based applications across their enterprise-computing infrastructure. The Problem – Current State With prevalent IT practices, many organizations find that they run multiple IT solutions serving similar business needs. This has led to the proliferation of technology stacks, for example: Oracle 10g on Sun T4 running Solaris 9; Oracle 11g on Exadata running Linux; or Oracle 12c on commodity x86 servers. This variance has a huge impact on an organization’s agility and expenses, and requires IT professionals with varied skills as well as on-going training for different systems and tools. Fortunately there is a practical business strategy to overcome this unneeded redundancy. Thus begins a journey to the right cloud computing solution. The Solution – Cloud Services from Oracle Consulting Services (OCS) Oracle Consulting Services (OCS ) works closely with our clients as trusted advisors to proactively respond to business needs and IT concerns. OCS understands that making the transition to cloud solutions begins with a strategic conversation, based on its deep expertise for successfully completing private cloud service engagements with several companies. For a journey to the cloud, Oracle Consulting Services leads the client through four phases– standardization, consolidation, service delivery, and enterprise cloud – to achieve optimal returns. Phase 1 - Standardization Oracle Consulting Services (OCS) works with clients to evaluate their business requirements and propose a set of standard solutions stacks for various IT solutions. This is an opportune time to evaluate cloud ready solutions, such as Oracle 12c, Oracle Exadata, and the Oracle Database Appliance (ODA). The OCS consultants, together with the delivery team, then turn to upgrading and migrating existing solution stacks to standardized offerings. OCS has the expertise and tools to complete this stage in a fraction of the time required by other IT services companies. Clients quickly realize cost savings in tools, processes, and type/number of resources required. This standardization also improves agility of the IT organizations and their abilities to respond to the needs of various business units. Phase 2 - Consolidation During the consolidation phase, OCS consultants programmatically consolidate hundreds of databases into a smaller number of servers to improve utilization, reduce floor space, and optimize maintenance costs. Consolidation helps clients realize huge savings in CapEx investments and shrink OpEx costs. The use of engineered systems, such as Oracle Exadata, greatly reduces the client’s risk of moving to a new solution stack. OCS recommends clients to pursue Phase 1 (Standardization) and Phase 2 (Consolidation) simultaneously to reduce the overall time, effort, and expense of the cloud journey. Phase 3 - Service Delivery Once a client is on a path of standardization and consolidation, OCS consultants create Service Catalogues based on the SLAs requirements and the criticality of the solutions. The number and types of Service Catalogues (Platinum, Gold, Silver, Bronze, etc.) vary from client to client. OCS consultants also implement a variety of value-added cloud solutions, including monitoring, metering, and charge-back solutions. At this stage, clients are able to achieve a high level of understanding in their cloud journey. Their IT organizations are operating efficiently and are more agile in responding to the needs of business units. Phase 4 - Enterprise Cloud In the final phase of the cloud journey, the economics of the IT organizations change. Business units can request services on-demand; applications can be deployed and consumed on a pay-as-you-go model. OCS has the expertise and capabilities to establish processes, programs, and solutions required for IT organizations to transform how they interact with business units. The Promise of Cloud Solutions Depending the size and complexity of their business model, some clients are able to abbreviate some phases of their cloud journey. Cloud solutions are still evolving and there is rapid pace of innovation to transform how IT organizations operate. The lesson is clear. Cloud solutions hold a lot of promise for business agility. Business leaders can now leverage an additional set of capabilities and services. They can ramp up their pace of innovation. With cloud maturity, they can compete more effectively in their respective markets. But there are certainly challenges ahead. A skilled consulting services partner can play a pivotal role as a trusted advisor in the successful adoption of cloud solutions. Oracle Consulting Services has expertise and a portfolio of services to help clients succeed on their journey to the cloud.

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  • File Server - Storage configuration: RAID vs LVM vs ZFS something else... ?

    - by privatehuff
    We are a small company that does video editing, among other things, and need a place to keep backup copies of large media files and make it easy to share them. I've got a box set up with Ubuntu Server and 4 x 500 GB drives. They're currently set up with Samba as four shared folders that Mac/Windows workstations can see fine, but I want a better solution. There are two major reasons for this: 500 GB is not really big enough (some projects are larger) It is cumbersome to manage the current setup, because individual hard drives have different amounts of free space and duplicated data (for backup). It is confusing now and that will only get worse once there are multiple servers. ("the project is on sever2 in share4" etc) So, I need a way to combine hard drives in such a way as to avoid complete data loss with the failure of a single drive, and so users see only a single share on each server. I've done linux software RAID5 and had a bad experience with it, but would try it again. LVM looks ok but it seems like no one uses it. ZFS seems interesting but it is relatively "new". What is the most efficient and least risky way to to combine the hdd's that is convenient for my users? Edit: The Goal here is basically to create servers that contain an arbitrary number of hard drives but limit complexity from an end-user perspective. (i.e. they see one "folder" per server) Backing up data is not an issue here, but how each solution responds to hardware failure is a serious concern. That is why I lump RAID, LVM, ZFS, and who-knows-what together. My prior experience with RAID5 was also on an Ubuntu Server box and there was a tricky and unlikely set of circumstances that led to complete data loss. I could avoid that again but was left with a feeling that I was adding an unnecessary additional point of failure to the system. I haven't used RAID10 but we are on commodity hardware and the most data drives per box is pretty much fixed at 6. We've got a lot of 500 GB drives and 1.5 TB is pretty small. (Still an option for at least one server, however) I have no experience with LVM and have read conflicting reports on how it handles drive failure. If a (non-striped) LVM setup could handle a single drive failing and only loose whichever files had a portion stored on that drive (and stored most files on a single drive only) we could even live with that. But as long as I have to learn something totally new, I may as well go all the way to ZFS. Unlike LVM, though, I would also have to change my operating system (?) so that increases the distance between where I am and where I want to be. I used a version of solaris at uni and wouldn't mind it terribly, though. On the other end on the IT spectrum, I think I may also explore FreeNAS and/or Openfiler, but that doesn't really solve the how-to-combine-drives issue.

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  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • MySQL Cluster 7.2: Over 8x Higher Performance than Cluster 7.1

    - by Mat Keep
    0 0 1 893 5092 Homework 42 11 5974 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Summary The scalability enhancements delivered by extensions to multi-threaded data nodes enables MySQL Cluster 7.2 to deliver over 8x higher performance than the previous MySQL Cluster 7.1 release on a recent benchmark What’s New in MySQL Cluster 7.2 MySQL Cluster 7.2 was released as GA (Generally Available) in February 2012, delivering many enhancements to performance on complex queries, new NoSQL Key / Value API, cross-data center replication and ease-of-use. These enhancements are summarized in the Figure below, and detailed in the MySQL Cluster New Features whitepaper Figure 1: Next Generation Web Services, Cross Data Center Replication and Ease-of-Use Once of the key enhancements delivered in MySQL Cluster 7.2 is extensions made to the multi-threading processes of the data nodes. Multi-Threaded Data Node Extensions The MySQL Cluster 7.2 data node is now functionally divided into seven thread types: 1) Local Data Manager threads (ldm). Note – these are sometimes also called LQH threads. 2) Transaction Coordinator threads (tc) 3) Asynchronous Replication threads (rep) 4) Schema Management threads (main) 5) Network receiver threads (recv) 6) Network send threads (send) 7) IO threads Each of these thread types are discussed in more detail below. MySQL Cluster 7.2 increases the maximum number of LDM threads from 4 to 16. The LDM contains the actual data, which means that when using 16 threads the data is more heavily partitioned (this is automatic in MySQL Cluster). Each LDM thread maintains its own set of data partitions, index partitions and REDO log. The number of LDM partitions per data node is not dynamically configurable, but it is possible, however, to map more than one partition onto each LDM thread, providing flexibility in modifying the number of LDM threads. The TC domain stores the state of in-flight transactions. This means that every new transaction can easily be assigned to a new TC thread. Testing has shown that in most cases 1 TC thread per 2 LDM threads is sufficient, and in many cases even 1 TC thread per 4 LDM threads is also acceptable. Testing also demonstrated that in some instances where the workload needed to sustain very high update loads it is necessary to configure 3 to 4 TC threads per 4 LDM threads. In the previous MySQL Cluster 7.1 release, only one TC thread was available. This limit has been increased to 16 TC threads in MySQL Cluster 7.2. The TC domain also manages the Adaptive Query Localization functionality introduced in MySQL Cluster 7.2 that significantly enhanced complex query performance by pushing JOIN operations down to the data nodes. Asynchronous Replication was separated into its own thread with the release of MySQL Cluster 7.1, and has not been modified in the latest 7.2 release. To scale the number of TC threads, it was necessary to separate the Schema Management domain from the TC domain. The schema management thread has little load, so is implemented with a single thread. The Network receiver domain was bound to 1 thread in MySQL Cluster 7.1. With the increase of threads in MySQL Cluster 7.2 it is also necessary to increase the number of recv threads to 8. This enables each receive thread to service one or more sockets used to communicate with other nodes the Cluster. The Network send thread is a new thread type introduced in MySQL Cluster 7.2. Previously other threads handled the sending operations themselves, which can provide for lower latency. To achieve highest throughput however, it has been necessary to create dedicated send threads, of which 8 can be configured. It is still possible to configure MySQL Cluster 7.2 to a legacy mode that does not use any of the send threads – useful for those workloads that are most sensitive to latency. The IO Thread is the final thread type and there have been no changes to this domain in MySQL Cluster 7.2. Multiple IO threads were already available, which could be configured to either one thread per open file, or to a fixed number of IO threads that handle the IO traffic. Except when using compression on disk, the IO threads typically have a very light load. Benchmarking the Scalability Enhancements The scalability enhancements discussed above have made it possible to scale CPU usage of each data node to more than 5x of that possible in MySQL Cluster 7.1. In addition, a number of bottlenecks have been removed, making it possible to scale data node performance by even more than 5x. Figure 2: MySQL Cluster 7.2 Delivers 8.4x Higher Performance than 7.1 The flexAsynch benchmark was used to compare MySQL Cluster 7.2 performance to 7.1 across an 8-node Intel Xeon x5670-based cluster of dual socket commodity servers (6 cores each). As the results demonstrate, MySQL Cluster 7.2 delivers over 8x higher performance per data nodes than MySQL Cluster 7.1. More details of this and other benchmarks will be published in a new whitepaper – coming soon, so stay tuned! In a following blog post, I’ll provide recommendations on optimum thread configurations for different types of server processor. You can also learn more from the Best Practices Guide to Optimizing Performance of MySQL Cluster Conclusion MySQL Cluster has achieved a range of impressive benchmark results, and set in context with the previous 7.1 release, is able to deliver over 8x higher performance per node. As a result, the multi-threaded data node extensions not only serve to increase performance of MySQL Cluster, they also enable users to achieve significantly improved levels of utilization from current and future generations of massively multi-core, multi-thread processor designs.

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  • MySQL Cluster 7.3 Labs Release – Foreign Keys Are In!

    - by Mat Keep
    0 0 1 1097 6254 Homework 52 14 7337 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Summary (aka TL/DR): Support for Foreign Key constraints has been one of the most requested feature enhancements for MySQL Cluster. We are therefore extremely excited to announce that Foreign Keys are part of the first Labs Release of MySQL Cluster 7.3 – available for download, evaluation and feedback now! (Select the mysql-cluster-7.3-labs-June-2012 build) In this blog, I will attempt to discuss the design rationale, implementation, configuration and steps to get started in evaluating the first MySQL Cluster 7.3 Labs Release. Pace of Innovation It was only a couple of months ago that we announced the General Availability (GA) of MySQL Cluster 7.2, delivering 1 billion Queries per Minute, with 70x higher cross-shard JOIN performance, Memcached NoSQL key-value API and cross-data center replication.  This release has been a huge hit, with downloads and deployments quickly reaching record levels. The announcement of the first MySQL Cluster 7.3 Early Access lab release at today's MySQL Innovation Day event demonstrates the continued pace in Cluster development, and provides an opportunity for the community to evaluate and feedback on new features they want to see. What’s the Plan for MySQL Cluster 7.3? Well, Foreign Keys, as you may have gathered by now (!), and this is the focus of this first Labs Release. As with MySQL Cluster 7.2, we plan to publish a series of preview releases for 7.3 that will incrementally add new candidate features for a final GA release (subject to usual safe harbor statement below*), including: - New NoSQL APIs; - Features to automate the configuration and provisioning of multi-node clusters, on premise or in the cloud; - Performance and scalability enhancements; - Taking advantage of features in the latest MySQL 5.x Server GA. Design Rationale MySQL Cluster is designed as a “Not-Only-SQL” database. It combines attributes that enable users to blend the best of both relational and NoSQL technologies into solutions that deliver web scalability with 99.999% availability and real-time performance, including: Concurrent NoSQL and SQL access to the database; Auto-sharding with simple scale-out across commodity hardware; Multi-master replication with failover and recovery both within and across data centers; Shared-nothing architecture with no single point of failure; Online scaling and schema changes; ACID compliance and support for complex queries, across shards. Native support for Foreign Key constraints enables users to extend the benefits of MySQL Cluster into a broader range of use-cases, including: - Packaged applications in areas such as eCommerce and Web Content Management that prescribe databases with Foreign Key support. - In-house developments benefiting from Foreign Key constraints to simplify data models and eliminate the additional application logic needed to maintain data consistency and integrity between tables. Implementation The Foreign Key functionality is implemented directly within MySQL Cluster’s data nodes, allowing any client API accessing the cluster to benefit from them – whether using SQL or one of the NoSQL interfaces (Memcached, C++, Java, JPA or HTTP/REST.) The core referential actions defined in the SQL:2003 standard are implemented: CASCADE RESTRICT NO ACTION SET NULL In addition, the MySQL Cluster implementation supports the online adding and dropping of Foreign Keys, ensuring the Cluster continues to serve both read and write requests during the operation. An important difference to note with the Foreign Key implementation in InnoDB is that MySQL Cluster does not support the updating of Primary Keys from within the Data Nodes themselves - instead the UPDATE is emulated with a DELETE followed by an INSERT operation. Therefore an UPDATE operation will return an error if the parent reference is using a Primary Key, unless using CASCADE action, in which case the delete operation will result in the corresponding rows in the child table being deleted. The Engineering team plans to change this behavior in a subsequent preview release. Also note that when using InnoDB "NO ACTION" is identical to "RESTRICT". In the case of MySQL Cluster “NO ACTION” means “deferred check”, i.e. the constraint is checked before commit, allowing user-defined triggers to automatically make changes in order to satisfy the Foreign Key constraints. Configuration There is nothing special you have to do here – Foreign Key constraint checking is enabled by default. If you intend to migrate existing tables from another database or storage engine, for example from InnoDB, there are a couple of best practices to observe: 1. Analyze the structure of the Foreign Key graph and run the ALTER TABLE ENGINE=NDB in the correct sequence to ensure constraints are enforced 2. Alternatively drop the Foreign Key constraints prior to the import process and then recreate when complete. Getting Started Read this blog for a demonstration of using Foreign Keys with MySQL Cluster.  You can download MySQL Cluster 7.3 Labs Release with Foreign Keys today - (select the mysql-cluster-7.3-labs-June-2012 build) If you are new to MySQL Cluster, the Getting Started guide will walk you through installing an evaluation cluster on a singe host (these guides reflect MySQL Cluster 7.2, but apply equally well to 7.3) Post any questions to the MySQL Cluster forum where our Engineering team will attempt to assist you. Post any bugs you find to the MySQL bug tracking system (select MySQL Cluster from the Category drop-down menu) And if you have any feedback, please post them to the Comments section of this blog. Summary MySQL Cluster 7.2 is the GA, production-ready release of MySQL Cluster. This first Labs Release of MySQL Cluster 7.3 gives you the opportunity to preview and evaluate future developments in the MySQL Cluster database, and we are very excited to be able to share that with you. Let us know how you get along with MySQL Cluster 7.3, and other features that you want to see in future releases. * Safe Harbor Statement This information is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. 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 for Oracle’s products remains at the sole discretion of Oracle.

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  • SQL SERVER – Core Concepts – Elasticity, Scalability and ACID Properties – Exploring NuoDB an Elastically Scalable Database System

    - by pinaldave
    I have been recently exploring Elasticity and Scalability attributes of databases. You can see that in my earlier blog posts about NuoDB where I wanted to look at Elasticity and Scalability concepts. The concepts are very interesting, and intriguing as well. I have discussed these concepts with my friend Joyti M and together we have come up with this interesting read. The goal of this article is to answer following simple questions What is Elasticity? What is Scalability? How ACID properties vary from NOSQL Concepts? What are the prevailing problems in the current database system architectures? Why is NuoDB  an innovative and welcome change in database paradigm? Elasticity This word’s original form is used in many different ways and honestly it does do a decent job in holding things together over the years as a person grows and contracts. Within the tech world, and specifically related to software systems (database, application servers), it has come to mean a few things - allow stretching of resources without reaching the breaking point (on demand). What are resources in this context? Resources are the usual suspects – RAM/CPU/IO/Bandwidth in the form of a container (a process or bunch of processes combined as modules). When it is about increasing resources the simplest idea which comes to mind is the addition of another container. Another container means adding a brand new physical node. When it is about adding a new node there are two questions which comes to mind. 1) Can we add another node to our software system? 2) If yes, does adding new node cause downtime for the system? Let us assume we have added new node, let us see what the new needs of the system are when a new node is added. Balancing incoming requests to multiple nodes Synchronization of a shared state across multiple nodes Identification of “downstate” and resolution action to bring it to “upstate” Well, adding a new node has its advantages as well. Here are few of the positive points Throughput can increase nearly horizontally across the node throughout the system Response times of application will increase as in-between layer interactions will be improved Now, Let us put the above concepts in the perspective of a Database. When we mention the term “running out of resources” or “application is bound to resources” the resources can be CPU, Memory or Bandwidth. The regular approach to “gain scalability” in the database is to look around for bottlenecks and increase the bottlenecked resource. When we have memory as a bottleneck we look at the data buffers, locks, query plans or indexes. After a point even this is not enough as there needs to be an efficient way of managing such large workload on a “single machine” across memory and CPU bound (right kind of scheduling)  workload. We next move on to either read/write separation of the workload or functionality-based sharing so that we still have control of the individual. But this requires lots of planning and change in client systems in terms of knowing where to go/update/read and for reporting applications to “aggregate the data” in an intelligent way. What we ideally need is an intelligent layer which allows us to do these things without us getting into managing, monitoring and distributing the workload. Scalability In the context of database/applications, scalability means three main things Ability to handle normal loads without pressure E.g. X users at the Y utilization of resources (CPU, Memory, Bandwidth) on the Z kind of hardware (4 processor, 32 GB machine with 15000 RPM SATA drives and 1 GHz Network switch) with T throughput Ability to scale up to expected peak load which is greater than normal load with acceptable response times Ability to provide acceptable response times across the system E.g. Response time in S milliseconds (or agreed upon unit of measure) – 90% of the time The Issue – Need of Scale In normal cases one can plan for the load testing to test out normal, peak, and stress scenarios to ensure specific hardware meets the needs. With help from Hardware and Software partners and best practices, bottlenecks can be identified and requisite resources added to the system. Unfortunately this vertical scale is expensive and difficult to achieve and most of the operational people need the ability to scale horizontally. This helps in getting better throughput as there are physical limits in terms of adding resources (Memory, CPU, Bandwidth and Storage) indefinitely. Today we have different options to achieve scalability: Read & Write Separation The idea here is to do actual writes to one store and configure slaves receiving the latest data with acceptable delays. Slaves can be used for balancing out reads. We can also explore functional separation or sharing as well. We can separate data operations by a specific identifier (e.g. region, year, month) and consolidate it for reporting purposes. For functional separation the major disadvantage is when schema changes or workload pattern changes. As the requirement grows one still needs to deal with scale need in manual ways by providing an abstraction in the middle tier code. Using NOSQL solutions The idea is to flatten out the structures in general to keep all values which are retrieved together at the same store and provide flexible schema. The issue with the stores is that they are compromising on mostly consistency (no ACID guarantees) and one has to use NON-SQL dialect to work with the store. The other major issue is about education with NOSQL solutions. Would one really want to make these compromises on the ability to connect and retrieve in simple SQL manner and learn other skill sets? Or for that matter give up on ACID guarantee and start dealing with consistency issues? Hybrid Deployment – Mac, Linux, Cloud, and Windows One of the challenges today that we see across On-premise vs Cloud infrastructure is a difference in abilities. Take for example SQL Azure – it is wonderful in its concepts of throttling (as it is shared deployment) of resources and ability to scale using federation. However, the same abilities are not available on premise. This is not a mistake, mind you – but a compromise of the sweet spot of workloads, customer requirements and operational SLAs which can be supported by the team. In today’s world it is imperative that databases are available across operating systems – which are a commodity and used by developers of all hues. An Ideal Database Ability List A system which allows a linear scale of the system (increase in throughput with reasonable response time) with the addition of resources A system which does not compromise on the ACID guarantees and require developers to learn new paradigms A system which does not force fit a new way interacting with database by learning Non-SQL dialect A system which does not force fit its mechanisms for providing availability across its various modules. Well NuoDB is the first database which has all of the above abilities and much more. In future articles I will cover my hands-on experience with it. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

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  • Tutorial: Getting Started with the NoSQL JavaScript / Node.js API for MySQL Cluster

    - by Mat Keep
    Tutorial authored by Craig Russell and JD Duncan  The MySQL Cluster team are working on a new NoSQL JavaScript connector for MySQL. The objectives are simplicity and high performance for JavaScript users: - allows end-to-end JavaScript development, from the browser to the server and now to the world's most popular open source database - native "NoSQL" access to the storage layer without going first through SQL transformations and parsing. Node.js is a complete web platform built around JavaScript designed to deliver millions of client connections on commodity hardware. With the MySQL NoSQL Connector for JavaScript, Node.js users can easily add data access and persistence to their web, cloud, social and mobile applications. While the initial implementation is designed to plug and play with Node.js, the actual implementation doesn't depend heavily on Node, potentially enabling wider platform support in the future. Implementation The architecture and user interface of this connector are very different from other MySQL connectors in a major way: it is an asynchronous interface that follows the event model built into Node.js. To make it as easy as possible, we decided to use a domain object model to store the data. This allows for users to query data from the database and have a fully-instantiated object to work with, instead of having to deal with rows and columns of the database. The domain object model can have any user behavior that is desired, with the NoSQL connector providing the data from the database. To make it as fast as possible, we use a direct connection from the user's address space to the database. This approach means that no SQL (pun intended) is needed to get to the data, and no SQL server is between the user and the data. The connector is being developed to be extensible to multiple underlying database technologies, including direct, native access to both the MySQL Cluster "ndb" and InnoDB storage engines. The connector integrates the MySQL Cluster native API library directly within the Node.js platform itself, enabling developers to seamlessly couple their high performance, distributed applications with a high performance, distributed, persistence layer delivering 99.999% availability. The following sections take you through how to connect to MySQL, query the data and how to get started. Connecting to the database A Session is the main user access path to the database. You can get a Session object directly from the connector using the openSession function: var nosql = require("mysql-js"); var dbProperties = {     "implementation" : "ndb",     "database" : "test" }; nosql.openSession(dbProperties, null, onSession); The openSession function calls back into the application upon creating a Session. The Session is then used to create, delete, update, and read objects. Reading data The Session can read data from the database in a number of ways. If you simply want the data from the database, you provide a table name and the key of the row that you want. For example, consider this schema: create table employee (   id int not null primary key,   name varchar(32),   salary float ) ENGINE=ndbcluster; Since the primary key is a number, you can provide the key as a number to the find function. function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find('employee', 0, onData); }; function onData = function(err, data) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(data));   ... use data in application }; If you want to have the data stored in your own domain model, you tell the connector which table your domain model uses, by specifying an annotation, and pass your domain model to the find function. var annotations = new nosql.Annotations(); function Employee = function(id, name, salary) {   this.id = id;   this.name = name;   this.salary = salary;   this.giveRaise = function(percent) {     this.salary *= percent;   } }; annotations.mapClass(Employee, {'table' : 'employee'}); function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData); }; Updating data You can update the emp instance in memory, but to make the raise persistent, you need to write it back to the database, using the update function. function onData = function(err, emp) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp); // oops, session is out of scope here }; Using JavaScript can be tricky because it does not have the concept of block scope for variables. You can create a closure to handle these variables, or use a feature of the connector to remember your variables. The connector api takes a fixed number of parameters and returns a fixed number of result parameters to the callback function. But the connector will keep track of variables for you and return them to the callback. So in the above example, change the onSession function to remember the session variable, and you can refer to it in the onData function: function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData, session); }; function onData = function(err, emp, session) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp, onUpdate); // session is now in scope }; function onUpdate = function(err, emp) {   if (err) {     console.log(err);     ... error handling   } Inserting data Inserting data requires a mapped JavaScript user function (constructor) and a session. Create a variable and persist it: function onSession = function(err, session) {   var data = new Employee(999, 'Mat Keep', 20000000);   session.persist(data, onInsert);   } }; Deleting data To remove data from the database, use the session remove function. You use an instance of the domain object to identify the row you want to remove. Only the key field is relevant. function onSession = function(err, session) {   var key = new Employee(999);   session.remove(Employee, onDelete);   } }; More extensive queries We are working on the implementation of more extensive queries along the lines of the criteria query api. Stay tuned. How to evaluate The MySQL Connector for JavaScript is available for download from labs.mysql.com. Select the build: MySQL-Cluster-NoSQL-Connector-for-Node-js You can also clone the project on GitHub Since it is still early in development, feedback is especially valuable (so don't hesitate to leave comments on this blog, or head to the MySQL Cluster forum). Try it out and see how easy (and fast) it is to integrate MySQL Cluster into your Node.js platforms. You can learn more about other previewed functionality of MySQL Cluster 7.3 here

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  • EM12c Release 4: Database as a Service Enhancements

    - by Adeesh Fulay
    Oracle Enterprise Manager 12.1.0.4 (or simply put EM12c R4) is the latest update to the product. As previous versions, this release provides tons of enhancements and bug fixes, attributing to improved stability and quality. One of the areas that is most exciting and has seen tremendous growth in the last few years is that of Database as a Service. EM12c R4 provides a significant update to Database as a Service. The key themes are: Comprehensive Database Service Catalog (includes single instance, RAC, and Data Guard) Additional Storage Options for Snap Clone (includes support for Database feature CloneDB) Improved Rapid Start Kits Extensible Metering and Chargeback Miscellaneous Enhancements 1. Comprehensive Database Service Catalog Before we get deep into implementation of a service catalog, lets first understand what it is and what benefits it provides. Per ITIL, a service catalog is an exhaustive list of IT services that an organization provides or offers to its employees or customers. Service catalogs have been widely popular in the space of cloud computing, primarily as the medium to provide standardized and pre-approved service definitions. There is already some good collateral out there that talks about Oracle database service catalogs. The two whitepapers i recommend reading are: Service Catalogs: Defining Standardized Database Service High Availability Best Practices for Database Consolidation: The Foundation for Database as a Service [Oracle MAA] EM12c comes with an out-of-the-box service catalog and self service portal since release 1. For the customers, it provides the following benefits: Present a collection of standardized database service definitions, Define standardized pools of hardware and software for provisioning, Role based access to cater to different class of users, Automated procedures to provision the predefined database definitions, Setup chargeback plans based on service tiers and database configuration sizes, etc Starting Release 4, the scope of services offered via the service catalog has been expanded to include databases with varying levels of availability - Single Instance (SI) or Real Application Clusters (RAC) databases with multiple data guard based standby databases. Some salient points of the data guard integration: Standby pools can now be defined across different datacenters or within the same datacenter as the primary (this helps in modelling the concept of near and far DR sites) The standby databases can be single instance, RAC, or RAC One Node databases Multiple standby databases can be provisioned, where the maximum limit is determined by the version of database software The standby databases can be in either mount or read only (requires active data guard option) mode All database versions 10g to 12c supported (as certified with EM 12c) All 3 protection modes can be used - Maximum availability, performance, security Log apply can be set to sync or async along with the required apply lag The different service levels or service tiers are popularly represented using metals - Platinum, Gold, Silver, Bronze, and so on. The Oracle MAA whitepaper (referenced above) calls out the various service tiers as defined by Oracle's best practices, but customers can choose any logical combinations from the table below:  Primary  Standby [1 or more]  EM 12cR4  SI  -  SI  SI  RAC -  RAC SI  RAC RAC  RON -  RON RON where RON = RAC One Node is supported via custom post-scripts in the service template A sample service catalog would look like the image below. Here we have defined 4 service levels, which have been deployed across 2 data centers, and have 3 standardized sizes. Again, it is important to note that this is just an example to get the creative juices flowing. I imagine each customer would come up with their own catalog based on the application requirements, their RTO/RPO goals, and the product licenses they own. In the screenwatch titled 'Build Service Catalog using EM12c DBaaS', I walk through the complete steps required to setup this sample service catalog in EM12c. 2. Additional Storage Options for Snap Clone In my previous blog posts, i have described the snap clone feature in detail. Essentially, it provides a storage agnostic, self service, rapid, and space efficient approach to solving your data cloning problems. The net benefit is that you get incredible amounts of storage savings (on average 90%) all while cloning databases in a matter of minutes. Space and Time, two things enterprises would love to save on. This feature has been designed with the goal of providing data cloning capabilities while protecting your existing investments in server, storage, and software. With this in mind, we have pursued with the dual solution approach of Hardware and Software. In the hardware approach, we connect directly to your storage appliances and perform all low level actions required to rapidly clone your databases. While in the software approach, we use an intermediate software layer to talk to any storage vendor or any storage configuration to perform the same low level actions. Thus delivering the benefits of database thin cloning, without requiring you to drastically changing the infrastructure or IT's operating style. In release 4, we expand the scope of options supported by snap clone with the addition of database CloneDB. While CloneDB is not a new feature, it was first introduced in 11.2.0.2 patchset, it has over the years become more stable and mature. CloneDB leverages a combination of Direct NFS (or dNFS) feature of the database, RMAN image copies, sparse files, and copy-on-write technology to create thin clones of databases from existing backups in a matter of minutes. It essentially has all the traits that we want to present to our customers via the snap clone feature. For more information on cloneDB, i highly recommend reading the following sources: Blog by Tim Hall: Direct NFS (DNFS) CloneDB in Oracle Database 11g Release 2 Oracle OpenWorld Presentation by Cern: Efficient Database Cloning using Direct NFS and CloneDB The advantages of the new CloneDB integration with EM12c Snap Clone are: Space and time savings Ease of setup - no additional software is required other than the Oracle database binary Works on all platforms Reduce the dependence on storage administrators Cloning process fully orchestrated by EM12c, and delivered to developers/DBAs/QA Testers via the self service portal Uses dNFS to delivers better performance, availability, and scalability over kernel NFS Complete lifecycle of the clones managed by EM12c - performance, configuration, etc 3. Improved Rapid Start Kits DBaaS deployments tend to be complex and its setup requires a series of steps. These steps are typically performed across different users and different UIs. The Rapid Start Kit provides a single command solution to setup Database as a Service (DBaaS) and Pluggable Database as a Service (PDBaaS). One command creates all the Cloud artifacts like Roles, Administrators, Credentials, Database Profiles, PaaS Infrastructure Zone, Database Pools and Service Templates. Once the Rapid Start Kit has been successfully executed, requests can be made to provision databases and PDBs from the self service portal. Rapid start kit can create complex topologies involving multiple zones, pools and service templates. It also supports standby databases and use of RMAN image backups. The Rapid Start Kit in reality is a simple emcli script which takes a bunch of xml files as input and executes the complete automation in a matter of seconds. On a full rack Exadata, it took only 40 seconds to setup PDBaaS end-to-end. This kit works for both Oracle's engineered systems like Exadata, SuperCluster, etc and also on commodity hardware. One can draw parallel to the Exadata One Command script, which again takes a bunch of inputs from the administrators and then runs a simple script that configures everything from network to provisioning the DB software. Steps to use the kit: The kit can be found under the SSA plug-in directory on the OMS: EM_BASE/oracle/MW/plugins/oracle.sysman.ssa.oms.plugin_12.1.0.8.0/dbaas/setup It can be run from this default location or from any server which has emcli client installed For most scenarios, you would use the script dbaas/setup/database_cloud_setup.py For Exadata, special integration is provided to reduce the number of inputs even further. The script to use for this scenario would be dbaas/setup/exadata_cloud_setup.py The database_cloud_setup.py script takes two inputs: Cloud boundary xml: This file defines the cloud topology in terms of the zones and pools along with host names, oracle home locations or container database names that would be used as infrastructure for provisioning database services. This file is optional in case of Exadata, as the boundary is well know via the Exadata system target available in EM. Input xml: This file captures inputs for users, roles, profiles, service templates, etc. Essentially, all inputs required to define the DB services and other settings of the self service portal. Once all the xml files have been prepared, invoke the script as follows for PDBaaS: emcli @database_cloud_setup.py -pdbaas -cloud_boundary=/tmp/my_boundary.xml -cloud_input=/tmp/pdb_inputs.xml          The script will prompt for passwords a few times for key users like sysman, cloud admin, SSA admin, etc. Once complete, you can simply log into EM as the self service user and request for databases from the portal. More information available in the Rapid Start Kit chapter in Cloud Administration Guide.  4. Extensible Metering and Chargeback  Last but not the least, Metering and Chargeback in release 4 has been made extensible in all possible regards. The new extensibility features allow customer, partners, system integrators, etc to : Extend chargeback to any target type managed in EM Promote any metric in EM as a chargeback entity Extend list of charge items via metric or configuration extensions Model abstract entities like no. of backup requests, job executions, support requests, etc  A slew of emcli verbs have also been added that allows administrators to create, edit, delete, import/export charge plans, and assign cost centers all via the command line. More information available in the Chargeback API chapter in Cloud Administration Guide. 5. Miscellaneous Enhancements There are other miscellaneous, yet important, enhancements that are worth a mention. These mostly have been asked by customers like you. These are: Custom naming of DB Services Self service users can provide custom names for DB SID, DB service, schemas, and tablespaces Every custom name is validated for uniqueness in EM 'Create like' of Service Templates Now creating variants of a service template is only a click away. This would be vital when you publish service templates to represent different database sizes or service levels. Profile viewer View the details of a profile like datafile, control files, snapshot ids, export/import files, etc prior to its selection in the service template Cleanup automation - for failed and successful requests Single emcli command to cleanup all remnant artifacts of a failed request Cleanup can be performed on a per request bases or by the entire pool As an extension, you can also delete successful requests Improved delete user workflow Allows administrators to reassign cloud resources to another user or delete all of them Support for multiple tablespaces for schema as a service In addition to multiple schemas, user can also specify multiple tablespaces per request I hope this was a good introduction to the new Database as a Service enhancements in EM12c R4. I encourage you to explore many of these new and existing features and give us feedback. Good luck! References: Cloud Management Page on OTN Cloud Administration Guide [Documentation] -- Adeesh Fulay (@adeeshf)

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  • Reference Data Management

    - by rahulkamath
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} table.MsoTableColorfulListAccent2 {mso-style-name:"Colorful List - Accent 2"; mso-tstyle-rowband-size:1; mso-tstyle-colband-size:1; mso-style-priority:72; mso-style-unhide:no; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-tstyle-shading:#F8EDED; mso-tstyle-shading-themecolor:accent2; mso-tstyle-shading-themetint:25; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; color:black; mso-themecolor:text1;} table.MsoTableColorfulListAccent2FirstRow {mso-style-name:"Colorful List - Accent 2"; mso-table-condition:first-row; mso-style-priority:72; mso-style-unhide:no; mso-tstyle-shading:#9E3A38; mso-tstyle-shading-themecolor:accent2; mso-tstyle-shading-themeshade:204; mso-tstyle-border-bottom:1.5pt solid white; mso-tstyle-border-bottom-themecolor:background1; color:white; mso-themecolor:background1; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableColorfulListAccent2LastRow {mso-style-name:"Colorful List - Accent 2"; mso-table-condition:last-row; mso-style-priority:72; mso-style-unhide:no; mso-tstyle-shading:white; mso-tstyle-shading-themecolor:background1; mso-tstyle-border-top:1.5pt solid black; mso-tstyle-border-top-themecolor:text1; color:#9E3A38; mso-themecolor:accent2; mso-themeshade:204; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableColorfulListAccent2FirstCol {mso-style-name:"Colorful List - Accent 2"; mso-table-condition:first-column; mso-style-priority:72; mso-style-unhide:no; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableColorfulListAccent2LastCol {mso-style-name:"Colorful List - Accent 2"; mso-table-condition:last-column; mso-style-priority:72; mso-style-unhide:no; mso-ansi-font-weight:bold; mso-bidi-font-weight:bold;} table.MsoTableColorfulListAccent2OddColumn {mso-style-name:"Colorful List - Accent 2"; mso-table-condition:odd-column; mso-style-priority:72; mso-style-unhide:no; mso-tstyle-shading:#EFD3D2; mso-tstyle-shading-themecolor:accent2; mso-tstyle-shading-themetint:63; mso-tstyle-border-top:cell-none; mso-tstyle-border-left:cell-none; mso-tstyle-border-bottom:cell-none; mso-tstyle-border-right:cell-none; mso-tstyle-border-insideh:cell-none; mso-tstyle-border-insidev:cell-none;} table.MsoTableColorfulListAccent2OddRow {mso-style-name:"Colorful List - Accent 2"; mso-table-condition:odd-row; mso-style-priority:72; mso-style-unhide:no; mso-tstyle-shading:#F2DBDB; mso-tstyle-shading-themecolor:accent2; mso-tstyle-shading-themetint:51;} Reference Data Management Oracle Data Relationship Management (DRM) has always been extremely powerful as an Enterprise MDM solution that can help manage changes to master data in a way that influences enterprise structure, whether it be mastering chart of accounts to enable financial transformation, or revamping organization structures to drive business transformation and operational efficiencies, or mastering sales territories in light of rapid fire acquisitions that require frequent sales territory refinement, equitable distribution of leads and accounts to salespersons, and alignment of budget/forecast with results to optimize sales coverage. Increasingly, DRM is also being utilized by Oracle customers for reference data management, an emerging solution space that deserves some explanation. What is reference data? Reference data is a close cousin of master data. While master data may be more rapidly changing, requires consensus building across stakeholders and lends structure to business transactions, reference data is simpler, more slowly changing, but has semantic content that is used to categorize or group other information assets – including master data – and give them contextual value. The following table contains an illustrative list of examples of reference data by type. Reference data types may include types and codes, business taxonomies, complex relationships & cross-domain mappings or standards. Types & Codes Taxonomies Relationships / Mappings Standards Transaction Codes Industry Classification Categories and Codes, e.g., North America Industry Classification System (NAICS) Product / Segment; Product / Geo Calendars (e.g., Gregorian, Fiscal, Manufacturing, Retail, ISO8601) Lookup Tables (e.g., Gender, Marital Status, etc.) Product Categories City à State à Postal Codes Currency Codes (e.g., ISO) Status Codes Sales Territories (e.g., Geo, Industry Verticals, Named Accounts, Federal/State/Local/Defense) Customer / Market Segment; Business Unit / Channel Country Codes (e.g., ISO 3166, UN) Role Codes Market Segments Country Codes / Currency Codes / Financial Accounts Date/Time, Time Zones (e.g., ISO 8601) Domain Values Universal Standard Products and Services Classification (UNSPSC), eCl@ss International Classification of Diseases (ICD) e.g., ICD9 à IC10 mappings Tax Rates Why manage reference data? Reference data carries contextual value and meaning and therefore its use can drive business logic that helps execute a business process, create a desired application behavior or provide meaningful segmentation to analyze transaction data. Further, mapping reference data often requires human judgment. Sample Use Cases of Reference Data Management Healthcare: Diagnostic Codes The reference data challenges in the healthcare industry offer a case in point. Part of being HIPAA compliant requires medical practitioners to transition diagnosis codes from ICD-9 to ICD-10, a medical coding scheme used to classify diseases, signs and symptoms, causes, etc. The transition to ICD-10 has a significant impact on business processes, procedures, contracts, and IT systems. Since both code sets ICD-9 and ICD-10 offer diagnosis codes of very different levels of granularity, human judgment is required to map ICD-9 codes to ICD-10. The process requires collaboration and consensus building among stakeholders much in the same way as does master data management. Moreover, to build reports to understand utilization, frequency and quality of diagnoses, medical practitioners may need to “cross-walk” mappings -- either forward to ICD-10 or backwards to ICD-9 depending upon the reporting time horizon. Spend Management: Product, Service & Supplier Codes Similarly, as an enterprise looks to rationalize suppliers and leverage their spend, conforming supplier codes, as well as product and service codes requires supporting multiple classification schemes that may include industry standards (e.g., UNSPSC, eCl@ss) or enterprise taxonomies. Aberdeen Group estimates that 90% of companies rely on spreadsheets and manual reviews to aggregate, classify and analyze spend data, and that data management activities account for 12-15% of the sourcing cycle and consume 30-50% of a commodity manager’s time. Creating a common map across the extended enterprise to rationalize codes across procurement, accounts payable, general ledger, credit card, procurement card (P-card) as well as ACH and bank systems can cut sourcing costs, improve compliance, lower inventory stock, and free up talent to focus on value added tasks. Specialty Finance: Point of Sales Transaction Codes and Product Codes In the specialty finance industry, enterprises are confronted with usury laws – governed at the state and local level – that regulate financial product innovation as it relates to consumer loans, check cashing and pawn lending. To comply, it is important to demonstrate that transactions booked at the point of sale are posted against valid product codes that were on offer at the time of booking the sale. Since new products are being released at a steady stream, it is important to ensure timely and accurate mapping of point-of-sale transaction codes with the appropriate product and GL codes to comply with the changing regulations. Multi-National Companies: Industry Classification Schemes As companies grow and expand across geographies, a typical challenge they encounter with reference data represents reconciling various versions of industry classification schemes in use across nations. While the United States, Mexico and Canada conform to the North American Industry Classification System (NAICS) standard, European Union countries choose different variants of the NACE industry classification scheme. Multi-national companies must manage the individual national NACE schemes and reconcile the differences across countries. Enterprises must invest in a reference data change management application to address the challenge of distributing reference data changes to downstream applications and assess which applications were impacted by a given change.

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