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  • How Do You Actually Model Data?

    Since the 1970’s Developers, Analysts and DBAs have been able to represent concepts and relations in the form of data through the use of generic symbols.  But what is data modeling?  The first time I actually heard this term I could not understand why anyone would want to display a computer on a fashion show runway. Hey, what do you expect? At that time I was a freshman in community college, and obviously this was a long time ago.  I have since had the chance to learn what data modeling truly is through using it. Data modeling is a process of breaking down information and/or requirements in to common categories called objects. Once objects start being defined then relationships start to form based on dependencies found amongst other existing objects.  Currently, there are several tools on the market that help data designer actually map out objects and their relationships through the use of symbols and lines.  These diagrams allow for designs to be review from several perspectives so that designers can ensure that they have the optimal data design for their project and that the design is flexible enough to allow for potential changes and/or extension in the future. Additionally these basic models can always be further refined to show different levels of details depending on the target audience through the use of three different types of models. Conceptual Data Model(CDM)Conceptual Data Models include all key entities and relationships giving a viewer a high level understanding of attributes. Conceptual data model are created by gathering and analyzing information from various sources pertaining to a project during the typical planning phase of a project. Logical Data Model (LDM)Logical Data Models are conceptual data models that have been expanded to include implementation details pertaining to the data that it will store. Additionally, this model typically represents an origination’s business requirements and business rules by defining various attribute data types and relationships regarding each entity. This additional information can be directly translated to the Physical Data Model which reduces the actual time need to implement it. Physical Data Model(PDMs)Physical Data Model are transformed Logical Data Models that include the necessary tables, columns, relationships, database properties for the creation of a database. This model also allows for considerations regarding performance, indexing and denormalization that are applied through database rules, data integrity. Further expanding on why we actually use models in modern application/database development can be seen in the benefits that data modeling provides for data modelers and projects themselves, Benefits of Data Modeling according to Applied Information Science Abstraction that allows data designers remove concepts and ideas form hard facts in the form of data. This gives the data designers the ability to express general concepts and/or ideas in a generic form through the use of symbols to represent data items and the relationships between the items. Transparency through the use of data models allows complex ideas to be translated in to simple symbols so that the concept can be understood by all viewpoints and limits the amount of confusion and misunderstanding. Effectiveness in regards to tuning a model for acceptable performance while maintaining affordable operational costs. In addition it allows systems to be built on a solid foundation in terms of data. I shudder at the thought of a world without data modeling, think about it? Data is everywhere in our lives. Data modeling allows for optimizing a design for performance and the reduction of duplication. If one was to design a database without data modeling then I would think that the first things to get impacted would be database performance due to poorly designed database and there would be greater chances of unnecessary data duplication that would also play in to the excessive query times because unneeded records would need to be processed. You could say that a data designer designing a database is like a box of chocolates. You will never know what kind of database you will get until after it is built.

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  • Data Structures usage and motivational aspects

    - by Aubergine
    For long student life I was always wondering why there are so many of them yet there seems to be lack of usage at all in many of them. The opinion didn't really change when I got a job. We have brilliant books on what they are and their complexities, but I never encounter resources which would actually give a good hint of practical usage. I perfectly understand that I have to look at problem , analyse required operations, look for data structure that does them efficiently. However in practice I never do that, not because of human laziness syndrome, but because when it comes to work I acknowledge time priority over self-development. Over time I thought that when I would be better developer I will automatically use more of them - that didn't happen at all or maybe I just didn't. Then I found that the colleagues usually in the same plate as me - knowing more or less some three of data structures and being totally happy about it and refusing to discuss this matter further with me, coming back to conversations about 'cool new languages' 'libraries that do jobs for you' and the joy to work under scrumban etc. I am stuck with ArrayLists, Arrays and SortedMap , which no matter what I do always suffice or either I tweak them to be capable of fulfilling my task. Yes, it might be inefficient but do we really have to care if Intel increases performance over years no matter if we improve our skills? Does new Xeon or IBM machines really care what we use? What if I like build things, but I am not particularly excited whether it is n log(n) or just n? Over twenty years the processing power increased enormously, which gives us freedom of not being critical about which one to use? On top of that new more optimized languages appear which support multiple cores more efficiently. To be more specific: I would like to find motivational material on complex real areas/cases of possible effective usages of data structures. I would be really grateful if you would provide relevant resources. There is similar question ,but in the end the links again mostly describe or do dumb example(vehicles, students or holy grail quest - yes, very relevant) them and people keep referring to the "scenario decides the data structure to use". I want to know these complex scenarios to be able to identify similarities to my scenario and then use them. The complex scenarios where it really matters and not necessarily of quantitive nature. It seems that data structures only concern is efficiency and nothing else? There seems to be no particular convenience for developer in use one over another. (only when I found scientific resources on why exactly simple carbohydrates are evil I stopped eating sugar and candies completely replacing it with less harmful fruits - I hope you can see the analogy)

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  • Unleash AutoVue on Your Unmanaged Data

    - by [email protected]
    Over the years, I've spoken to hundreds of customers who use AutoVue to collaborate on their "managed" data stored in content management systems, product lifecycle management systems, etc. via our many integrations. Through these conversations I've also learned a harsh reality - we will never fully move away from unmanaged data (desktops, file servers, emails, etc). If you use AutoVue today you already know that even if your primary use is viewing content stored in a content management system, you can still open files stored locally on your computer. But did you know that AutoVue actually has - built-in - a great solution for viewing, printing and redlining your data stored on file servers? Using the 'Server protocol' you can point AutoVue directly to a top-level location on any networked file server and provide your users with a link or shortcut to access an interface similar to the sample page shown below. Many customers link to pages just like this one from their internal company intranets. Through this webpage, users can easily search and browse through file server data with a 'click-and-view' interface to find the specific image, document, drawing or model they're looking for. Any markups created on a document will be accessible to everyone else viewing that document and of course real-time collaboration is supported as well. Customers on maintenance can consult the AutoVue Admin guide or My Oracle Support Doc ID 753018.1 for an introduction to the server protocol. Contact your local AutoVue Solutions Consultant for help setting up the sample shown above.

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  • SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS

    - by pinaldave
    Data Quality Services is a very important concept of SQL Server. I have recently started to explore the same and I am really learning some good concepts. Here are two very important blog posts which one should go over before continuing this blog post. Installing Data Quality Services (DQS) on SQL Server 2012 Connecting Error to Data Quality Services (DQS) on SQL Server 2012 This article is introduction to Data Quality Services for beginners. We will be using an Excel file Click on the image to enlarge the it. In the first article we learned to install DQS. In this article we will see how we can learn about building Knowledge Base and using it to help us identify the quality of the data as well help correct the bad quality of the data. Here are the two very important steps we will be learning in this tutorial. Building a New Knowledge Base  Creating a New Data Quality Project Let us start the building the Knowledge Base. Click on New Knowledge Base. In our project we will be using the Excel as a knowledge base. Here is the Excel which we will be using. There are two columns. One is Colors and another is Shade. They are independent columns and not related to each other. The point which I am trying to show is that in Column A there are unique data and in Column B there are duplicate records. Clicking on New Knowledge Base will bring up the following screen. Enter the name of the new knowledge base. Clicking NEXT will bring up following screen where it will allow to select the EXCE file and it will also let users select the source column. I have selected Colors and Shade both as a source column. Creating a domain is very important. Here you can create a unique domain or domain which is compositely build from Colors and Shade. As this is the first example, I will create unique domain – for Colors I will create domain Colors and for Shade I will create domain Shade. Here is the screen which will demonstrate how the screen will look after creating domains. Clicking NEXT it will bring you to following screen where you can do the data discovery. Clicking on the START will start the processing of the source data provided. Pre-processed data will show various information related to the source data. In our case it shows that Colors column have unique data whereas Shade have non-unique data and unique data rows are only two. In the next screen you can actually add more rows as well see the frequency of the data as the values are listed unique. Clicking next will publish the knowledge base which is just created. Now the knowledge base is created. We will try to take any random data and attempt to do DQS implementation over it. I am using another excel sheet here for simplicity purpose. In reality you can easily use SQL Server table for the same. Click on New Data Quality Project to see start DQS Project. In the next screen it will ask which knowledge base to use. We will be using our Colors knowledge base which we have recently created. In the Colors knowledge base we had two columns – 1) Colors and 2) Shade. In our case we will be using both of the mappings here. User can select one or multiple column mapping over here. Now the most important phase of the complete project. Click on Start and it will make the cleaning process and shows various results. In our case there were two columns to be processed and it completed the task with necessary information. It demonstrated that in Colors columns it has not corrected any value by itself but in Shade value there is a suggestion it has. We can train the DQS to correct values but let us keep that subject for future blog posts. Now click next and keep the domain Colors selected left side. It will demonstrate that there are two incorrect columns which it needs to be corrected. Here is the place where once corrected value will be auto-corrected in future. I manually corrected the value here and clicked on Approve radio buttons. As soon as I click on Approve buttons the rows will be disappeared from this tab and will move to Corrected Tab. If I had rejected tab it would have moved the rows to Invalid tab as well. In this screen you can see how the corrected 2 rows are demonstrated. You can click on Correct tab and see previously validated 6 rows which passed the DQS process. Now let us click on the Shade domain on the left side of the screen. This domain shows very interesting details as there DQS system guessed the correct answer as Dark with the confidence level of 77%. It is quite a high confidence level and manual observation also demonstrate that Dark is the correct answer. I clicked on Approve and the row moved to corrected tab. On the next screen DQS shows the summary of all the activities. It also demonstrates how the correction of the quality of the data was performed. The user can explore their data to a SQL Server Table, CSV file or Excel. The user also has an option to either explore data and all the associated cleansing info or data only. I will select Data only for demonstration purpose. Clicking explore will generate the files. Let us open the generated file. It will look as following and it looks pretty complete and corrected. Well, we have successfully completed DQS Process. The process is indeed very easy. I suggest you try this out yourself and you will find it very easy to learn. In future we will go over advanced concepts. Are you using this feature on your production server? If yes, would you please leave a comment with your environment and business need. It will be indeed interesting to see where it is implemented. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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  • Validating Data Using Data Annotation Attributes in ASP.NET MVC

    - by bipinjoshi
    The data entered by the end user in various form fields must be validated before it is saved in the database. Developers often use validation HTML helpers provided by ASP.NET MVC to perform the input validations. Additionally, you can also use data annotation attributes from the System.ComponentModel.DataAnnotations namespace to perform validations at the model level. Data annotation attributes are attached to the properties of the model class and enforce some validation criteria. They are capable of performing validation on the server side as well as on the client side. This article discusses the basics of using these attributes in an ASP.NET MVC application.http://www.bipinjoshi.net/articles/0a53f05f-b58c-47b1-a544-f032f5cfca58.aspx       

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  • WCF Error - Security processor was unable to find a security header in the message

    - by quinntheeskimo
    Hi, I'm getting what appears now to be a security error in my WCF Service. Originally my error was about a falted state(removed using around client proxy to clear this error), but have found more information through enabling trace. I have been unable to get my solution running after encountering this error, and even my backup copy now gets the same error. I'm not sure what has caused this to happen, I undone the changes I made (nothing relating to WCF) and still get the same error. The error from trace is - System.ServiceModel.Security.MessageSecurityException: Security processor was unable to find a security header in the message. This might be because the message is an unsecured fault or because there is a binding mismatch between the communicating parties. This can occur if the service is configured for security and the client is not using security. I'm not really sure what I need to do to fix this, any help would be usefull. The application was previously working.

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  • More Value From Data Using Data Mining Presentation

    Here is a presentation I gave at the SQLBits conference in September which was recorded by Microsoft.  Usually I speak about SSIS but on this particular event I thought people would like to hear something different from me. Microsoft are making a big play for making Data Mining more accessible to everyone and not just boffins.  In this presentation I give an overview of data mining and then do some demonstrations using the excellent Excel Add-Ins available from Microsoft SQL Server 2008 SQL Server 2005 I hope you enjoy this presentation http://go.microsoft.com/?linkid=9633764

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  • Move data from others user accounts in my user account

    - by user118136
    I had problems with compiz setting and I make multiple accounts, now I want to transfer my information from all deleted users in my current account, some data I can not copy because I am not right to read, I type in terminal "sudo nautilus" and I get the permission for read, but the copied data is available only for superusers and I must charge the permissions for each file and each folder. How I can copy the information with out the superuser rights OR how I can charge the permissions for selected folder and all files and folders included in it?

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  • [GEEK SCHOOL] Network Security 8: Keep Your System Updated for Security and Stability

    - by Ciprian Rusen
    Another important step in securing any computer or device is setting up automated updates. Your device’s security relies on your operating system, apps, plug-ins, and programs always being up to date. For example, using outdated Internet browsers and plug-ins like Adobe Flash, Java, or Silverlight represents a big security problem. There are many websites on the Internet that exploit security bugs in your browser or the plug-ins you have installed.Click Here to Continue Reading

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  • Anyone have BlazeDS working with WebLogic Security (j_security_check) ??

    - by Brian
    I'm working on a Flex implementation (currently using SDK 3.5) on WebLogic 10.3 (11G.) We originally used Glassfish v2.1.1 with zero issues (there was an active directory group lookup bug but it did not hinder our progress.) Since transitioning to WebLogic we have an issue where the flexsession is invalidated after logging in using j_security_check: [BlazeDS]Unexpected error encountered in Message Broker servlet flex.messaging.LocalizedException: The FlexSession is invalid. at flex.messaging.FlexSession.checkValid(FlexSession.java:943) at flex.messaging.FlexSession.getUserPrincipal(FlexSession.java:254) at flex.messaging.HttpFlexSession.getUserPrincipal(HttpFlexSession.java:286) at flex.messaging.MessageBrokerServlet.service(MessageBrokerServlet.java:296) at javax.servlet.http.HttpServlet.service(HttpServlet.java:820) at weblogic.servlet.internal.StubSecurityHelper$ServletServiceAction.run(StubSecurityHelper.java:227) at weblogic.servlet.internal.StubSecurityHelper.invokeServlet(StubSecurityHelper.java:125) at weblogic.servlet.internal.ServletStubImpl.execute(ServletStubImpl.java:292) at weblogic.servlet.internal.ServletStubImpl.execute(ServletStubImpl.java:175) at weblogic.servlet.internal.WebAppServletContext$ServletInvocationAction.run(WebAppServletContext.java:3594) at weblogic.security.acl.internal.AuthenticatedSubject.doAs(AuthenticatedSubject.java:321) at weblogic.security.service.SecurityManager.runAs(SecurityManager.java:121) at weblogic.servlet.internal.WebAppServletContext.securedExecute(WebAppServletContext.java:2202) at weblogic.servlet.internal.WebAppServletContext.execute(WebAppServletContext.java:2108) at weblogic.servlet.internal.ServletRequestImpl.run(ServletRequestImpl.java:1432) at weblogic.work.ExecuteThread.execute(ExecuteThread.java:201) at weblogic.work.ExecuteThread.run(ExecuteThread.java:173) I've tried almost every option in services-config.xml: [security [login-command class="flex.messaging.security.WeblogicLoginCommand" server="Weblogic"/> [!-- Uncomment the correct app server [login-command class="flex.messaging.security.TomcatLoginCommand" server="JBoss"> [login-command class="flex.messaging.security.JRunLoginCommand" server="JRun"/> [login-command class="flex.messaging.security.TomcatLoginCommand" server="Tomcat"/> [login-command class="flex.messaging.security.WebSphereLoginCommand" server="WebSphere"/> --> [/security> I've even completely removed this section with no luck. The login functions correctly from a non-BlazeDS perspective. It authenticates the user correctly. Without authentication, BlazeDS works fine (no errors for any remote calls.) Together its a big ball of fail (invalid flexsession every time.) Has anyone got this working? Any tips?

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  • SQL SERVER – Guest Post – Architecting Data Warehouse – Niraj Bhatt

    - by pinaldave
    Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group. Here is the guest post by Niraj Bhatt. As data in your applications grows it’s the database that usually becomes a bottleneck. It’s hard to scale a relational DB and the preferred approach for large scale applications is to create separate databases for writes and reads. These databases are referred as transactional database and reporting database. Though there are tools / techniques which can allow you to create snapshot of your transactional database for reporting purpose, sometimes they don’t quite fit the reporting requirements of an enterprise. These requirements typically are data analytics, effective schema (for an Information worker to self-service herself), historical data, better performance (flat data, no joins) etc. This is where a need for data warehouse or an OLAP system arises. A Key point to remember is a data warehouse is mostly a relational database. It’s built on top of same concepts like Tables, Rows, Columns, Primary keys, Foreign Keys, etc. Before we talk about how data warehouses are typically structured let’s understand key components that can create a data flow between OLTP systems and OLAP systems. There are 3 major areas to it: a) OLTP system should be capable of tracking its changes as all these changes should go back to data warehouse for historical recording. For e.g. if an OLTP transaction moves a customer from silver to gold category, OLTP system needs to ensure that this change is tracked and send to data warehouse for reporting purpose. A report in context could be how many customers divided by geographies moved from sliver to gold category. In data warehouse terminology this process is called Change Data Capture. There are quite a few systems that leverage database triggers to move these changes to corresponding tracking tables. There are also out of box features provided by some databases e.g. SQL Server 2008 offers Change Data Capture and Change Tracking for addressing such requirements. b) After we make the OLTP system capable of tracking its changes we need to provision a batch process that can run periodically and takes these changes from OLTP system and dump them into data warehouse. There are many tools out there that can help you fill this gap – SQL Server Integration Services happens to be one of them. c) So we have an OLTP system that knows how to track its changes, we have jobs that run periodically to move these changes to warehouse. The question though remains is how warehouse will record these changes? This structural change in data warehouse arena is often covered under something called Slowly Changing Dimension (SCD). While we will talk about dimensions in a while, SCD can be applied to pure relational tables too. SCD enables a database structure to capture historical data. This would create multiple records for a given entity in relational database and data warehouses prefer having their own primary key, often known as surrogate key. As I mentioned a data warehouse is just a relational database but industry often attributes a specific schema style to data warehouses. These styles are Star Schema or Snowflake Schema. The motivation behind these styles is to create a flat database structure (as opposed to normalized one), which is easy to understand / use, easy to query and easy to slice / dice. Star schema is a database structure made up of dimensions and facts. Facts are generally the numbers (sales, quantity, etc.) that you want to slice and dice. Fact tables have these numbers and have references (foreign keys) to set of tables that provide context around those facts. E.g. if you have recorded 10,000 USD as sales that number would go in a sales fact table and could have foreign keys attached to it that refers to the sales agent responsible for sale and to time table which contains the dates between which that sale was made. These agent and time tables are called dimensions which provide context to the numbers stored in fact tables. This schema structure of fact being at center surrounded by dimensions is called Star schema. A similar structure with difference of dimension tables being normalized is called a Snowflake schema. This relational structure of facts and dimensions serves as an input for another analysis structure called Cube. Though physically Cube is a special structure supported by commercial databases like SQL Server Analysis Services, logically it’s a multidimensional structure where dimensions define the sides of cube and facts define the content. Facts are often called as Measures inside a cube. Dimensions often tend to form a hierarchy. E.g. Product may be broken into categories and categories in turn to individual items. Category and Items are often referred as Levels and their constituents as Members with their overall structure called as Hierarchy. Measures are rolled up as per dimensional hierarchy. These rolled up measures are called Aggregates. Now this may seem like an overwhelming vocabulary to deal with but don’t worry it will sink in as you start working with Cubes and others. Let’s see few other terms that we would run into while talking about data warehouses. ODS or an Operational Data Store is a frequently misused term. There would be few users in your organization that want to report on most current data and can’t afford to miss a single transaction for their report. Then there is another set of users that typically don’t care how current the data is. Mostly senior level executives who are interesting in trending, mining, forecasting, strategizing, etc. don’t care for that one specific transaction. This is where an ODS can come in handy. ODS can use the same star schema and the OLAP cubes we saw earlier. The only difference is that the data inside an ODS would be short lived, i.e. for few months and ODS would sync with OLTP system every few minutes. Data warehouse can periodically sync with ODS either daily or weekly depending on business drivers. Data marts are another frequently talked about topic in data warehousing. They are subject-specific data warehouse. Data warehouses that try to span over an enterprise are normally too big to scope, build, manage, track, etc. Hence they are often scaled down to something called Data mart that supports a specific segment of business like sales, marketing, or support. Data marts too, are often designed using star schema model discussed earlier. Industry is divided when it comes to use of data marts. Some experts prefer having data marts along with a central data warehouse. Data warehouse here acts as information staging and distribution hub with spokes being data marts connected via data feeds serving summarized data. Others eliminate the need for a centralized data warehouse citing that most users want to report on detailed data. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Business Intelligence, Data Warehousing, Database, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Applied Security for Oracle Business Intelligence Podcast

    - by Tim Dexter
    Listen to BI Security Meister, Bryan Wise talk about his recent book Applied Oracle Security and learn about the cutting edge techniques for Oracle Business Intelligence from a leading security expert. http://www.oracle.com/podcasts/author-podcasts.html - Applied Security for Oracle Business Intelligence Well worth the listen and of course the book is available at all discerning bookstores!

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  • Data Mining Resources

    - by Dejan Sarka
    There are many different types of analyses, each one with its own pros and cons. Relational reports have a predefined structure, and end users cannot change it. They are simple to use for end users. Reports can use real-time data and snapshots of data to show the state of a report at specific points in time. One of the drawbacks is that report authoring is limited to IT pros and advanced users. Any kind of dynamic restructuring is very limited. If real-time data is used for a report, the report has a negative impact on the performance of the source system. Processing of the reports might be slow because the data comes from relational database management systems, which are not optimized for reporting only. If you create a semantic model of your data, your end users can create ad-hoc report structures. However, the development is more complex because a developer is needed to create these semantic models. For OLAP, you typically use specialized database management systems. You get lightning speed of analyses. End users can use rich and thin clients to interactively change the structure of the report. Typically, they do it graphically. However, the development of an OLAP system is many times quite complex. It involves the preparation and maintenance of an enterprise data warehouse and OLAP cubes. In order to exploit the possibility of real-time restructuring of reports, the users must be both active and educated. The data is usually stale, as it is loaded into data warehouses and OLAP cubes with a scheduled process. With data mining, a structure is not selected in advance; it searches for the structure. As a result, data mining can give you the most valuable results because you can discover patterns you did not expect. A data mining model structure is limited only by the attributes that you use to train the model. One of the drawbacks is that a lot of knowledge is needed for a successful data mining project. End users have to understand the results. Subject matter experts and IT professionals need to understand business problem thoroughly. The development might be sometimes even more complex than the development of OLAP cubes. Each type of analysis has its own place in an enterprise system. SQL Server has tools for all kinds of analyses. However, data mining is the most advanced way of analyzing the data; this is the “I” in BI. In order to get the most out of it, you need to learn quite a lot. In this blog post, I am gathering together resources for learning, including forthcoming events. Books Multiple authors: SQL Server MVP Deep Dives – I wrote an introductory data mining chapter there. Erik Veerman, Teo Lachev and Dejan Sarka: MCTS Self-Paced Training Kit (Exam 70-448): Microsoft SQL Server 2008 - Business Intelligence Development and Maintenance – you can find a good overview of a complete BI solution, including data mining, in this book. Jamie MacLennan, ZhaoHui Tang, and Bogdan Crivat: Data Mining with Microsoft SQL Server 2008 – can’t miss this book if you want to mine your data with SQL Server tools. Michael Berry, Gordon Linoff: Mastering Data Mining: The Art and Science of Customer Relationship Management – data mining from both, business and technical perspective. Dorian Pyle: Data Preparation for Data Mining – an in-depth book about data preparation. Thomas and Ronald Wonnacott: Introductory Statistics – if you thought that you could get away without statistics, then you are not serious about data mining. Jiawei Han and Micheline Kamber: Data Mining Concepts and Techniques – in-depth explanation of the most popular data mining algorithms. Michael Berry and Gordon Linoff: Data Mining Techniques – another book that explains data mining algorithms, more fro a business perspective. Paolo Guidici: Applied Data Mining – very mathematical book, only if you enjoy statistics and mathematics in general. Forthcoming presentations I am presenting two data mining related sessions during the PASS Summit in Charlotte, NC: Wednesday, October 16th, 2013 - Fraud Detection: Notes from the Field – I am showing how to use data mining for a specific business problem. The presentation is based on real-life projects. Friday, October 18th: Excel 2013 Advanced Analytics – I am focusing on Excel Data Mining Add-ins, and how to use them together with Power Pivot and other add-ins. This is the most you can get out of Excel. Sinergija 2013, Belgrade, Serbia Tuesday, October 22nd: Excel 2013 Analytics to the Max – another presentation focusing on the most advanced analytics you can get in Excel. SQL Rally Amsterdam, Netherlands Thursday, November 7th: Advanced Analytics in Excel 2013 – and again I am presenting about data mining in Excel. Why three different titles for the same presentation? I don’t know, I guess I forgot the name I proposed every time right after I sent the proposal. Courses Data Mining with SQL Server 2012 – I wrote a 3-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. OK, now you know: no more excuses, start learning data mining, get the most out of your data

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  • Oracle Security Inside Out Newsletter – June Edition

    - by Troy Kitch
    This month’s Information In Depth Newsletter, Security Inside Out Edition is now available. In this edition we look at the Gartner Security and Risk Management Summit 2011, discuss safeguarding data from threats with Oracle Database Vault, and reveal the latest database security webcasts, videos, training, events and more. If you don’t have a subscription to this bi-monthly security information update, you can sign up here at the bottom of the page.

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  • Master Data Services Employees Sample Model

    - by Davide Mauri
    I’ve been playing with Master Data Services quite a lot in those last days and I’m also monitoring the web for all available resources on it. Today I’ve found this freshly released sample available on MSDN Code Gallery: SQL Server Master Data Services Employee Sample Model http://code.msdn.microsoft.com/SSMDSEmployeeSample This sample shows how Recursive Hierarchies can be modeled in order to represent a typical organizational chart scenario where a self-relationship exists on the Employee entity. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Taking the fear out of a Cloud initiative through the use of security tools

    - by user736511
    Typical employees, constituents, and business owners  interact with online services at a level where their knowledge of back-end systems is low, and most of the times, there is no interest in knowing the systems' architecture.  Most application administrators, while partially responsible for these systems' upkeep, have very low interactions with them, at least at an operational, platform level.  Of greatest interest to these groups is the consistent, reliable, and manageable operation of the interfaces with which they communicate.  Introducing the "Cloud" topic in any evolving architecture automatically raises the concerns for data and identity security simply because of the perception that when owning the silicon, enterprises are not able to manage its content.  But is this really true?   In the majority of traditional architectures, data and applications that access it are physically distant from the organization that owns it.  It may reside in a shared data center, or a geographically convenient location that spans large organizations' connectivity capabilities.  In the end, very often, the model of a "traditional" architecture is fairly close to the "new" Cloud architecture.  Most notable difference is that by nature, a Cloud setup uses security as a core function, and not as a necessary add-on. Therefore, following best practices, one can say that data can be safer in the Cloud than in traditional, stove-piped environments where data access is segmented and difficult to audit. The caveat is, of course, what "best practices" consist of, and here is where Oracle's security tools are perfectly suited for the task.  Since Oracle's model is to support very large organizations, it is fundamentally concerned about distributed applications, databases etc and their security, and the related Identity Management Products, or DB Security options reflect that concept.  In the end, consumers of applications and their data are to be served more safely in a controlled Cloud environment, while realizing the many cost savings associated with it. Having very fast resources to serve them (such as the Exa* platform) makes the concept even more attractive.  Finally, if a Cloud strategy does not seem feasible, consider the pros and cons of a traditional vs. a Cloud architecture.  Using the exact same criteria and business goals/traditions, and with Oracle's technology, you might be hard pressed to justify maintaining the technical status quo on security alone. For additional information please visit Oracle's Cloud Security page at: http://www.oracle.com/us/technologies/cloud/cloud-security-428855.html

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  • Looking for Cutting-Edge Data Integration: 2010 Innovation Awards

    - by dain.hansen
    This year's Oracle Fusion Middleware Innovation Awards will honor customers and partners who are creatively using to various products across Oracle Fusion Middleware. Brand new to this year's awards is a category for Data Integration. Think you have something unique and innovative with one of our Oracle Data Integration products? We'd love to hear from you! Please submit today The deadline for the nomination is 5 p.m. PT Friday, August 6th 2010, and winning organizations will be notified by late August 2010. What you win! FREE pass to Oracle OpenWorld 2010 in San Francisco for select winners in each category. Honored by Oracle executives at awards ceremony held during Oracle OpenWorld 2010 in San Francisco. Oracle Middleware Innovation Award Winner Plaque 1-3 meetings with Oracle Executives during Oracle OpenWorld 2010 Feature article placement in Oracle Magazine and placement in Oracle Press Release Customer snapshot and video testimonial opportunity, to be hosted on oracle.com Podcast interview opportunity with Senior Oracle Executive

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  • Transparent Data Encryption Helps Customers Address Regulatory Compliance

    - by Troy Kitch
    Regulations such as the Payment Card Industry Data Security Standards (PCI DSS), U.S. state security breach notification laws, HIPAA HITECH and more, call for the use of data encryption or redaction to protect sensitive personally identifiable information (PII). From the outset, Oracle has delivered the industry's most advanced technology to safeguard data where it lives—in the database. Oracle provides a comprehensive portfolio of security solutions to ensure data privacy, protect against insider threats, and enable regulatory compliance for both Oracle and non-Oracle Databases. Organizations worldwide rely on Oracle Database Security solutions to help address industry and government regulatory compliance. Specifically, Oracle Advanced Security helps organizations like Educational Testing Service, TransUnion Interactive, Orbitz, and the National Marrow Donor Program comply with privacy and regulatory mandates by transparently encrypting sensitive information such as credit cards, social security numbers, and personally identifiable information (PII). By encrypting data at rest and whenever it leaves the database over the network or via backups, Oracle Advanced Security provides organizations the most cost-effective solution for comprehensive data protection. Watch the video and learn why organizations choose Oracle Advanced Security with transparent data encryption.

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  • Data Integration 12c Raising the Big Data Roof at Oracle OpenWorld

    - by Tanu Sood
    Normal 0 false false false EN-US X-NONE 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-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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Author: Dain Hansen, Director, Oracle It was an exciting OpenWorld 2013 for us in the Data Integration track. Our theme this year was all about ‘being future ready’ - previewing one of our biggest releases this year: Oracle Data Integration 12c. Just this week we followed up with this preview by announcing the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. Normal 0 false false false EN-US X-NONE 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-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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Mark Hurd's keynote on day one set the tone for the Data Integration sessions. Mark focused on big data analytics and the changing consumer expectations. Especially real-time insight is a key theme for Oracle overall and data integration products. In Mark Hurd's keynote we heard from key customers, such as Airbus and Thomson Reuters, how real-time analysis of operational data including machine data creates value, in some cases even saves lives. Thomas Kurian gave a deeper look into Oracle's big data and fast data solutions. In the initial lead Data Integration track session - Brad Adelberg, VP of Development, presented Oracle’s Data Integration 12c product strategy based on key trends from the initial OpenWorld keynotes. Brad talked about how Oracle's data integration products address the new data integration requirements that evolved with cloud computing, big data, and changing consumer expectations and how they set the key themes in our products’ road map. Brad explained why and how fast-time to value, high-performance and future-ready solutions is the top focus areas for product development. If you were not able to attend OpenWorld or this session I recommend reading the white paper: Five New Data Integration Requirements and How to Meet them with Oracle Data Integration, which provides an in-depth look into how Oracle addresses the new trends in the DI market. Following Brad’s session, Nick Wagner provided in depth review of Oracle GoldenGate’s latest features and roadmap. Nick discussed how Oracle GoldenGate’s tight integration with Oracle Database sets the product apart from the competition. We also heard that heterogeneity of the product is still a major focus for GoldenGate’s development and there will be more news on that front when there is a major release. Normal 0 false false false EN-US X-NONE 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-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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} After GoldenGate’s product strategy session, Denis Gray from the PM team presented Oracle Data Integrator’s product strategy session, talking about the latest and greatest on ODI. Another good session was delivered by long-time GoldenGate users, Comcast.  Jason Hurd and Amit Patel of Comcast talked about the various use cases they deploy Oracle GoldenGate throughout their enterprise, from database upgrades, feeding reporting systems, to active-active database synchronization.  The Comcast team shared many good tips on how to use GoldenGate for both zero downtime upgrades and active-active replication with conflict management requirement. One of our other important goals we had this year for the Data Integration track at OpenWorld was hearing from our customers. We ended day 1 on just that, with a wonderful award ceremony for Oracle Excellence Awards for Oracle Fusion Middleware Innovation. The ceremony was held in the Yerba Buena Center for the Arts. Congratulations to Royal Bank of Scotland and Yalumba Wine Company, the winners in the Data Integration category. You can find more information on the award and the winners in our previous blog post: 2013 Oracle Excellence Awards for Fusion Middleware Innovation… Selected for their innovation use of Oracle’s Data Integration products; the winners for the Data Integration Category are Royal Bank of Scotland and The Yalumba Wine Company. Congratulations!!! Royal Bank of Scotland’s Market and International Banking division provides clients across the globe with seamless trading and competitive pricing, underpinned by a deep knowledge of risk management across the full spectrum of financial products. They handle millions of transactions daily to keep the lifeblood of their clients’ businesses flowing – whether through payment management solutions or through bespoke trade finance solutions. Royal Bank of Scotland is leveraging Oracle GoldenGate and Oracle Data Integrator along with Oracle Business Intelligence Enterprise Edition and the Oracle Database for a variety of solutions. Mainly, Oracle GoldenGate and Oracle Data Integrator are used to feed their data warehouse – providing a real-time data integration solution that feeds transactional data to their analytics system in minutes to enable improved decision making with timely, accurate data for their business users. Oracle Data Integrator’s in-database transformation capabilities and its ability to integrate with Oracle GoldenGate for real-time data capture is the foundation of this implementation. This solution makes it such that changes happening in the analytics systems are available the same day they are deployed on the operational system with 100% data quality guaranteed. Additionally, the solution has helped to reduce their operational database size from 150GB to 10GB. Impressive! Now what if I told you this solution was built in 3 months and had a less than 6 month return on investment? That’s outstanding! The Yalumba Wine Company is situated in the Barossa Valley of Australia. It is the oldest family owned winery in Australia with a unique way of aging their wines in specially crafted 100 liter barrels. Did you know that “Yalumba” is Aboriginal for “all the land around”? The Yalumba Wine Company is growing rapidly, and was in need of introducing a more modern standard to the existing manufacturing processes to meet globalization demands, overall time-to-market, and better operational efficiency objectives of product development. The Yalumba Wine Company worked with a partner, Bristlecone to develop a unique solution whereby Oracle Data Integrator is leveraged to pull data from Salesforce.com and JD Edwards, in addition to their other pre-existing source systems, for consumption into their data warehouse. They have emphasized the overall ease of developing integration workflows with Oracle Data Integrator. The solution has brought better visibility for the business users, shorter data loading and transformation performance to their data warehouse with rapid incorporation of new data sources, and a solid future-proof foundation for their organization. Moving forward, they plan on leveraging more from Oracle’s Data Integration portfolio. Terrific! In addition to these two customers on Tuesday we featured many other important Oracle Data Integrator and Oracle GoldenGate customers. On Tuesday the GoldenGate panel included: Land O’Lakes, Smuckers, and Veolia Water. Besides giving us yummy nutrition and healthy water, these companies have another aspect in common. They all use GoldenGate to boost their ERP application. Please read the recap by Irem Radzik. On Wednesday, the ODI Panel included: Barry Ralston and Ryan Weber of Infinity Insurance, Paul Stracke of Paychex Inc., and Ian Wall of Vertex Pharmaceuticals for a session filled with interesting projects, use cases and approaches to leveraging Oracle Data Integrator. Please read the recap by Sandrine Riley for more. Thanks to everyone who joined with us and we hope to stay connected! To hear more about our Data Integration12c products join us in an upcoming webcast to learn more. Follow us www.twitter.com/ORCLGoldenGate or goto our website at www.oracle.com/goto/dataintegration

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  • Internet Explorer Security: How to Configure Settings

    Before jumping into the steps that are needed to configure Internet Explorer's security settings, let us first take a closer look at the four separate security zones that Microsoft has established for the browser. You will be able to tweak the settings of each of these four zones when we get into the configuration part of this tutorial, so it is best that you learn what they represent first. Internet Explorer Security Zones Internet Zone This Internet Explorer security zone refers to websites that are not on your computer or are not designated to your local intranet, which we will discuss in ...

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  • Spring Security Configuration Leads to Perpetual Authentication Request

    - by Sammy
    Hello, I have configured my web application with the following config file: <beans xmlns="http://www.springframework.org/schema/beans" xmlns:security="http://www.springframework.org/schema/security" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/security http://www.springframework.org/schema/security/spring-security-3.0.xsd"> <security:global-method-security secured-annotations="enabled" pre-post-annotations="enabled" /> <!-- Filter chain; this is referred to from the web.xml file. Each filter is defined and configured as a bean later on. --> <!-- Note: anonumousProcessingFilter removed. --> <bean id="filterChainProxy" class="org.springframework.security.web.FilterChainProxy"> <security:filter-chain-map path-type="ant"> <security:filter-chain pattern="/**" filters="securityContextPersistenceFilter, basicAuthenticationFilter, exceptionTranslationFilter, filterSecurityInterceptor" /> </security:filter-chain-map> </bean> <!-- This filter is responsible for session management, or rather the lack thereof. --> <bean id="securityContextPersistenceFilter" class="org.springframework.security.web.context.SecurityContextPersistenceFilter"> <property name="securityContextRepository"> <bean class="org.springframework.security.web.context.HttpSessionSecurityContextRepository"> <property name="allowSessionCreation" value="false" /> </bean> </property> </bean> <!-- Basic authentication filter. --> <bean id="basicAuthenticationFilter" class="org.springframework.security.web.authentication.www.BasicAuthenticationFilter"> <property name="authenticationManager" ref="authenticationManager" /> <property name="authenticationEntryPoint" ref="authenticationEntryPoint" /> </bean> <!-- Basic authentication entry point. --> <bean id="authenticationEntryPoint" class="org.springframework.security.web.authentication.www.BasicAuthenticationEntryPoint"> <property name="realmName" value="Ayudo Web Service" /> </bean> <!-- An anonymous authentication filter, which is chained after the normal authentication mechanisms and automatically adds an AnonymousAuthenticationToken to the SecurityContextHolder if there is no existing Authentication held there. --> <!-- <bean id="anonymousProcessingFilter" class="org.springframework.security.web.authentication.AnonymousProcessingFilter"> <property name="key" value="ayudo" /> <property name="userAttribute" value="anonymousUser, ROLE_ANONYMOUS" /> </bean> --> <!-- Authentication manager that chains our main authentication provider and anonymous authentication provider. --> <bean id="authenticationManager" class="org.springframework.security.authentication.ProviderManager"> <property name="providers"> <list> <ref local="daoAuthenticationProvider" /> <ref local="inMemoryAuthenticationProvider" /> <!-- <ref local="anonymousAuthenticationProvider" /> --> </list> </property> </bean> <!-- Main authentication provider; in this case, memory implementation. --> <bean id="inMemoryAuthenticationProvider" class="org.springframework.security.authentication.dao.DaoAuthenticationProvider"> <property name="userDetailsService" ref="propertiesUserDetails" /> </bean> <security:user-service id="propertiesUserDetails" properties="classpath:operators.properties" /> <!-- Main authentication provider. --> <bean id="daoAuthenticationProvider" class="org.springframework.security.authentication.dao.DaoAuthenticationProvider"> <property name="userDetailsService" ref="userDetailsService" /> </bean> <!-- An anonymous authentication provider which is chained into the ProviderManager so that AnonymousAuthenticationTokens are accepted. --> <!-- <bean id="anonymousAuthenticationProvider" class="org.springframework.security.authentication.AnonymousAuthenticationProvider"> <property name="key" value="ayudo" /> </bean> --> <bean id="userDetailsService" class="org.springframework.security.core.userdetails.jdbc.JdbcDaoImpl"> <property name="dataSource" ref="dataSource" /> </bean> <bean id="exceptionTranslationFilter" class="org.springframework.security.web.access.ExceptionTranslationFilter"> <property name="authenticationEntryPoint" ref="authenticationEntryPoint" /> <property name="accessDeniedHandler"> <bean class="org.springframework.security.web.access.AccessDeniedHandlerImpl" /> </property> </bean> <bean id="filterSecurityInterceptor" class="org.springframework.security.web.access.intercept.FilterSecurityInterceptor"> <property name="securityMetadataSource"> <security:filter-security-metadata-source use-expressions="true"> <security:intercept-url pattern="/*.html" access="permitAll" /> <security:intercept-url pattern="/version" access="permitAll" /> <security:intercept-url pattern="/users/activate" access="permitAll" /> <security:intercept-url pattern="/**" access="isAuthenticated()" /> </security:filter-security-metadata-source> </property> <property name="authenticationManager" ref="authenticationManager" /> <property name="accessDecisionManager" ref="accessDecisionManager" /> </bean> <bean id="accessDecisionManager" class="org.springframework.security.access.vote.AffirmativeBased"> <property name="decisionVoters"> <list> <bean class="org.springframework.security.access.vote.RoleVoter" /> <bean class="org.springframework.security.web.access.expression.WebExpressionVoter" /> </list> </property> </bean> As soon as I run my application on tomcat, I get a request for username/password basic authentication dialog. Even when I try to access: localhost:8080/myapp/version, which is explicitly set to permitAll, I get the authentication request dialog. Help! Thank, Sammy

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  • Fast Data - Big Data's achilles heel

    - by thegreeneman
    At OOW 2013 in Mark Hurd and Thomas Kurian's keynote, they discussed Oracle's Fast Data software solution stack and discussed a number of customers deploying Oracle's Big Data / Fast Data solutions and in particular Oracle's NoSQL Database.  Since that time, there have been a large number of request seeking clarification on how the Fast Data software stack works together to deliver on the promise of real-time Big Data solutions.   Fast Data is a software solution stack that deals with one aspect of Big Data, high velocity.   The software in the Fast Data solution stack involves 3 key pieces and their integration:  Oracle Event Processing, Oracle Coherence, Oracle NoSQL Database.   All three of these technologies address a high throughput, low latency data management requirement.   Oracle Event Processing enables continuous query to filter the Big Data fire hose, enable intelligent chained events to real-time service invocation and augments the data stream to provide Big Data enrichment. Extended SQL syntax allows the definition of sliding windows of time to allow SQL statements to look for triggers on events like breach of weighted moving average on a real-time data stream.    Oracle Coherence is a distributed, grid caching solution which is used to provide very low latency access to cached data when the data is too big to fit into a single process, so it is spread around in a grid architecture to provide memory latency speed access.  It also has some special capabilities to deploy remote behavioral execution for "near data" processing.   The Oracle NoSQL Database is designed to ingest simple key-value data at a controlled throughput rate while providing data redundancy in a cluster to facilitate highly concurrent low latency reads.  For example, when large sensor networks are generating data that need to be captured while analysts are simultaneously extracting the data using range based queries for upstream analytics.  Another example might be storing cookies from user web sessions for ultra low latency user profile management, also leveraging that data using holistic MapReduce operations with your Hadoop cluster to do segmented site analysis.  Understand how NoSQL plays a critical role in Big Data capture and enrichment while simultaneously providing a low latency and scalable data management infrastructure thru clustered, always on, parallel processing in a shared nothing architecture. Learn how easily a NoSQL cluster can be deployed to provide essential services in industry specific Fast Data solutions. See these technologies work together in a demonstration highlighting the salient features of these Fast Data enabling technologies in a location based personalization service. The question then becomes how do these things work together to deliver an end to end Fast Data solution.  The answer is that while different applications will exhibit unique requirements that may drive the need for one or the other of these technologies, often when it comes to Big Data you may need to use them together.   You may have the need for the memory latencies of the Coherence cache, but just have too much data to cache, so you use a combination of Coherence and Oracle NoSQL to handle extreme speed cache overflow and retrieval.   Here is a great reference to how these two technologies are integrated and work together.  Coherence & Oracle NoSQL Database.   On the stream processing side, it is similar as with the Coherence case.  As your sliding windows get larger, holding all the data in the stream can become difficult and out of band data may need to be offloaded into persistent storage.  OEP needs an extreme speed database like Oracle NoSQL Database to help it continue to perform for the real time loop while dealing with persistent spill in the data stream.  Here is a great resource to learn more about how OEP and Oracle NoSQL Database are integrated and work together.  OEP & Oracle NoSQL Database.

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  • Oracle Announces Oracle Big Data Appliance X3-2 and Enhanced Oracle Big Data Connectors

    - by jgelhaus
    Enables Customers to Easily Harness the Business Value of Big Data at Lower Cost Engineered System Simplifies Big Data for the Enterprise Oracle Big Data Appliance X3-2 hardware features the latest 8-core Intel® Xeon E5-2600 series of processors, and compared with previous generation, the 18 compute and storage servers with 648 TB raw storage now offer: 33 percent more processing power with 288 CPU cores; 33 percent more memory per node with 1.1 TB of main memory; and up to a 30 percent reduction in power and cooling Oracle Big Data Appliance X3-2 further simplifies implementation and management of big data by integrating all the hardware and software required to acquire, organize and analyze big data. It includes: Support for CDH4.1 including software upgrades developed collaboratively with Cloudera to simplify NameNode High Availability in Hadoop, eliminating the single point of failure in a Hadoop cluster; Oracle NoSQL Database Community Edition 2.0, the latest version that brings better Hadoop integration, elastic scaling and new APIs, including JSON and C support; The Oracle Enterprise Manager plug-in for Big Data Appliance that complements Cloudera Manager to enable users to more easily manage a Hadoop cluster; Updated distributions of Oracle Linux and Oracle Java Development Kit; An updated distribution of open source R, optimized to work with high performance multi-threaded math libraries Read More   Data sheet: Oracle Big Data Appliance X3-2 Oracle Big Data Appliance: Datacenter Network Integration Big Data and Natural Language: Extracting Insight From Text Thomson Reuters Discusses Oracle's Big Data Platform Connectors Integrate Hadoop with Oracle Big Data Ecosystem Oracle Big Data Connectors is a suite of software built by Oracle to integrate Apache Hadoop with Oracle Database, Oracle Data Integrator, and Oracle R Distribution. Enhancements to Oracle Big Data Connectors extend these data integration capabilities. With updates to every connector, this release includes: Oracle SQL Connector for Hadoop Distributed File System, for high performance SQL queries on Hadoop data from Oracle Database, enhanced with increased automation and querying of Hive tables and now supported within the Oracle Data Integrator Application Adapter for Hadoop; Transparent access to the Hive Query language from R and introduction of new analytic techniques executing natively in Hadoop, enabling R developers to be more productive by increasing access to Hadoop in the R environment. Read More Data sheet: Oracle Big Data Connectors High Performance Connectors for Load and Access of Data from Hadoop to Oracle Database

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  • Best approach to accessing multiple data source in a web application

    - by ced
    I've a base web application developed with .net technologies (asp.net) used into our LAN by 30 users simultanousley. From this web application I've developed two verticalization used from online users. In future i expect hundreds users simultanousley. Our company has different locations. Each site use its own database. The web application needs to retrieve information from all existing databases. Currently there are 3 database, but it's not excluded in the future expansion of new offices. My question then is: What is the best strategy for a web application to retrieve information from different databases (which have the same schema) whereas the main objective performance data access and high fault tolerance? There are case studies in the literature that I can take as an example? Do you know some good documents to study? Do you have any tips to implement this task so efficient? Intuitively I would say that two possible strategy are: perform queries from different sources in real time and aggregate data on the fly; create a repository that contains the union of the entities of interest and perform queries directly on repository;

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