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  • HTML5 data-* (custom data attribute)

    - by Renso
    Goal: Store custom data with the data attribute on any DOM element and retrieve it. Previously under HTML4 we used to use classes to store custom data, something to the affect of <input class="account void limit-5000 over-4999" /> and then have to parse the data out of the class In a book published by Peter-Paul Koch in 2007, ppk on JavaScript, he explains why and how to use custom attributes to make data more accessible to JavaScript, using name-value pairs. Accessing a custom attribute account-limit=5000 is much easier and more intuitive than trying to parse it out of a class, Plus, what if the class name for example "color-5" has a representative class definition in a CSS stylesheet that hides it away or worse some JavaScript plugin that automatically adds 5000 to it, or something crazy like that, just because it is a valid class name. As you can see there are quite a few reasons why using classes is a bad design and why it was important to define custom data attributes in HTML5. Syntax: You define the data attribute by simply prefixing any data item you want to store with any HTML element with "data-". For example to store our customers account data with a hidden input element: <input type="hidden" data-account="void" data-limit=5000 data-over=4999  /> How to access the data: account  -     element.dataset.account limit    -     element.dataset.limit You can also access it by using the more traditional get/setAttribute method or if using jQuery $('#element').attr('data-account','void') Browser support: All except for IE. There is an IE hack around this at http://gist.github.com/362081. Special Note: Be AWARE, do not use upper-case when defining your data elements as it is all converted to lower-case when reading it, so: data-myAccount="A1234" will not be found when you read it with: element.dataset.myAccount Use only lowercase when reading so this will work: element.dataset.myaccount

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  • Master Data Management – A Foundation for Big Data Analysis

    - by Manouj Tahiliani
    While Master Data Management has crossed the proverbial chasm and is on its way to becoming mainstream, businesses are being hammered by a new megatrend called Big Data. Big Data is characterized by massive volumes, its high frequency, the variety of less structured data sources such as email, sensors, smart meters, social networks, and Weblogs, and the need to analyze vast amounts of data to determine value to improve upon management decisions. Businesses that have embraced MDM to get a single, enriched and unified view of Master data by resolving semantic discrepancies and augmenting the explicit master data information from within the enterprise with implicit data from outside the enterprise like social profiles will have a leg up in embracing Big Data solutions. This is especially true for large and medium-sized businesses in industries like Retail, Communications, Financial Services, etc that would find it very challenging to get comprehensive analytical coverage and derive long-term success without resolving the limitations of the heterogeneous topology that leads to disparate, fragmented and incomplete master data. For analytical success from Big Data or in other words ROI from Big Data Investments, businesses need to acquire, organize and analyze the deluge of data to make better decisions. There will need to be a coexistence of structured and unstructured data and to maintain a tight link between the two to extract maximum insights. MDM is the catalyst that helps maintain that tight linkage by providing an understanding about the identity, characteristics of Persons, Companies, Products, Suppliers, etc. associated with the Big Data and thereby help accelerate ROI. In my next post I will discuss about patterns for co-existing Big Data Solutions and MDM. Feel free to provide comments and thoughts on above as well as Integration or Architectural patterns.

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  • VS 2010 SP1 and SQL CE

    - by ScottGu
    Last month we released the Beta of VS 2010 Service Pack 1 (SP1).  You can learn more about the VS 2010 SP1 Beta from Jason Zander’s two blog posts about it, and from Scott Hanselman’s blog post that covers some of the new capabilities enabled with it.   You can download and install the VS 2010 SP1 Beta here. Last week I blogged about the new Visual Studio support for IIS Express that we are adding with VS 2010 SP1. In today’s post I’m going to talk about the new VS 2010 SP1 tooling support for SQL CE, and walkthrough some of the cool scenarios it enables.  SQL CE – What is it and why should you care? SQL CE is a free, embedded, database engine that enables easy database storage. No Database Installation Required SQL CE does not require you to run a setup or install a database server in order to use it.  You can simply copy the SQL CE binaries into the \bin directory of your ASP.NET application, and then your web application can use it as a database engine.  No setup or extra security permissions are required for it to run. You do not need to have an administrator account on the machine. Just copy your web application onto any server and it will work. This is true even of medium-trust applications running in a web hosting environment. SQL CE runs in-memory within your ASP.NET application and will start-up when you first access a SQL CE database, and will automatically shutdown when your application is unloaded.  SQL CE databases are stored as files that live within the \App_Data folder of your ASP.NET Applications. Works with Existing Data APIs SQL CE 4 works with existing .NET-based data APIs, and supports a SQL Server compatible query syntax.  This means you can use existing data APIs like ADO.NET, as well as use higher-level ORMs like Entity Framework and NHibernate with SQL CE.  This enables you to use the same data programming skills and data APIs you know today. Supports Development, Testing and Production Scenarios SQL CE can be used for development scenarios, testing scenarios, and light production usage scenarios.  With the SQL CE 4 release we’ve done the engineering work to ensure that SQL CE won’t crash or deadlock when used in a multi-threaded server scenario (like ASP.NET).  This is a big change from previous releases of SQL CE – which were designed for client-only scenarios and which explicitly blocked running in web-server environments.  Starting with SQL CE 4 you can use it in a web-server as well. There are no license restrictions with SQL CE.  It is also totally free. Easy Migration to SQL Server SQL CE is an embedded database – which makes it ideal for development, testing, and light-usage scenarios.  For high-volume sites and applications you’ll probably want to migrate your database to use SQL Server Express (which is free), SQL Server or SQL Azure.  These servers enable much better scalability, more development features (including features like Stored Procedures – which aren’t supported with SQL CE), as well as more advanced data management capabilities. We’ll ship migration tools that enable you to optionally take SQL CE databases and easily upgrade them to use SQL Server Express, SQL Server, or SQL Azure.  You will not need to change your code when upgrading a SQL CE database to SQL Server or SQL Azure.  Our goal is to enable you to be able to simply change the database connection string in your web.config file and have your application just work. New Tooling Support for SQL CE in VS 2010 SP1 VS 2010 SP1 includes much improved tooling support for SQL CE, and adds support for using SQL CE within ASP.NET projects for the first time.  With VS 2010 SP1 you can now: Create new SQL CE Databases Edit and Modify SQL CE Database Schema and Indexes Populate SQL CE Databases within Data Use the Entity Framework (EF) designer to create model layers against SQL CE databases Use EF Code First to define model layers in code, then create a SQL CE database from them, and optionally edit the DB with VS Deploy SQL CE databases to remote servers using Web Deploy and optionally convert them to full SQL Server databases You can take advantage of all of the above features from within both ASP.NET Web Forms and ASP.NET MVC based projects. Download You can enable SQL CE tooling support within VS 2010 by first installing VS 2010 SP1 (beta). Once SP1 is installed, you’ll also then need to install the SQL CE Tools for Visual Studio download.  This is a separate download that enables the SQL CE tooling support for VS 2010 SP1. Walkthrough of Two Scenarios In this blog post I’m going to walkthrough how you can take advantage of SQL CE and VS 2010 SP1 using both an ASP.NET Web Forms and an ASP.NET MVC based application. Specifically, we’ll walkthrough: How to create a SQL CE database using VS 2010 SP1, then use the EF4 visual designers in Visual Studio to construct a model layer from it, and then display and edit the data using an ASP.NET GridView control. How to use an EF Code First approach to define a model layer using POCO classes and then have EF Code-First “auto-create” a SQL CE database for us based on our model classes.  We’ll then look at how we can use the new VS 2010 SP1 support for SQL CE to inspect the database that was created, populate it with data, and later make schema changes to it.  We’ll do all this within the context of an ASP.NET MVC based application. You can follow the two walkthroughs below on your own machine by installing VS 2010 SP1 (beta) and then installing the SQL CE Tools for Visual Studio download (which is a separate download that enables SQL CE tooling support for VS 2010 SP1). Walkthrough 1: Create a SQL CE Database, Create EF Model Classes, Edit the Data with a GridView This first walkthrough will demonstrate how to create and define a SQL CE database within an ASP.NET Web Form application.  We’ll then build an EF model layer for it and use that model layer to enable data editing scenarios with an <asp:GridView> control. Step 1: Create a new ASP.NET Web Forms Project We’ll begin by using the File->New Project menu command within Visual Studio to create a new ASP.NET Web Forms project.  We’ll use the “ASP.NET Web Application” project template option so that it has a default UI skin implemented: Step 2: Create a SQL CE Database Right click on the “App_Data” folder within the created project and choose the “Add->New Item” menu command: This will bring up the “Add Item” dialog box.  Select the “SQL Server Compact 4.0 Local Database” item (new in VS 2010 SP1) and name the database file to create “Store.sdf”: Note that SQL CE database files have a .sdf filename extension. Place them within the /App_Data folder of your ASP.NET application to enable easy deployment. When we clicked the “Add” button above a Store.sdf file was added to our project: Step 3: Adding a “Products” Table Double-clicking the “Store.sdf” database file will open it up within the Server Explorer tab.  Since it is a new database there are no tables within it: Right click on the “Tables” icon and choose the “Create Table” menu command to create a new database table.  We’ll name the new table “Products” and add 4 columns to it.  We’ll mark the first column as a primary key (and make it an identify column so that its value will automatically increment with each new row): When we click “ok” our new Products table will be created in the SQL CE database. Step 4: Populate with Data Once our Products table is created it will show up within the Server Explorer.  We can right-click it and choose the “Show Table Data” menu command to edit its data: Let’s add a few sample rows of data to it: Step 5: Create an EF Model Layer We have a SQL CE database with some data in it – let’s now create an EF Model Layer that will provide a way for us to easily query and update data within it. Let’s right-click on our project and choose the “Add->New Item” menu command.  This will bring up the “Add New Item” dialog – select the “ADO.NET Entity Data Model” item within it and name it “Store.edmx” This will add a new Store.edmx item to our solution explorer and launch a wizard that allows us to quickly create an EF model: Select the “Generate From Database” option above and click next.  Choose to use the Store.sdf SQL CE database we just created and then click next again.  The wizard will then ask you what database objects you want to import into your model.  Let’s choose to import the “Products” table we created earlier: When we click the “Finish” button Visual Studio will open up the EF designer.  It will have a Product entity already on it that maps to the “Products” table within our SQL CE database: The VS 2010 SP1 EF designer works exactly the same with SQL CE as it does already with SQL Server and SQL Express.  The Product entity above will be persisted as a class (called “Product”) that we can programmatically work against within our ASP.NET application. Step 6: Compile the Project Before using your model layer you’ll need to build your project.  Do a Ctrl+Shift+B to compile the project, or use the Build->Build Solution menu command. Step 7: Create a Page that Uses our EF Model Layer Let’s now create a simple ASP.NET Web Form that contains a GridView control that we can use to display and edit the our Products data (via the EF Model Layer we just created). Right-click on the project and choose the Add->New Item command.  Select the “Web Form from Master Page” item template, and name the page you create “Products.aspx”.  Base the master page on the “Site.Master” template that is in the root of the project. Add an <h2>Products</h2> heading the new Page, and add an <asp:gridview> control within it: Then click the “Design” tab to switch into design-view. Select the GridView control, and then click the top-right corner to display the GridView’s “Smart Tasks” UI: Choose the “New data source…” drop down option above.  This will bring up the below dialog which allows you to pick your Data Source type: Select the “Entity” data source option – which will allow us to easily connect our GridView to the EF model layer we created earlier.  This will bring up another dialog that allows us to pick our model layer: Select the “StoreEntities” option in the dropdown – which is the EF model layer we created earlier.  Then click next – which will allow us to pick which entity within it we want to bind to: Select the “Products” entity in the above dialog – which indicates that we want to bind against the “Product” entity class we defined earlier.  Then click the “Enable automatic updates” checkbox to ensure that we can both query and update Products.  When you click “Finish” VS will wire-up an <asp:EntityDataSource> to your <asp:GridView> control: The last two steps we’ll do will be to click the “Enable Editing” checkbox on the Grid (which will cause the Grid to display an “Edit” link on each row) and (optionally) use the Auto Format dialog to pick a UI template for the Grid. Step 8: Run the Application Let’s now run our application and browse to the /Products.aspx page that contains our GridView.  When we do so we’ll see a Grid UI of the Products within our SQL CE database. Clicking the “Edit” link for any of the rows will allow us to edit their values: When we click “Update” the GridView will post back the values, persist them through our EF Model Layer, and ultimately save them within our SQL CE database. Learn More about using EF with ASP.NET Web Forms Read this tutorial series on the http://asp.net site to learn more about how to use EF with ASP.NET Web Forms.  The tutorial series uses SQL Express as the database – but the nice thing is that all of the same steps/concepts can also now also be done with SQL CE.   Walkthrough 2: Using EF Code-First with SQL CE and ASP.NET MVC 3 We used a database-first approach with the sample above – where we first created the database, and then used the EF designer to create model classes from the database.  In addition to supporting a designer-based development workflow, EF also enables a more code-centric option which we call “code first development”.  Code-First Development enables a pretty sweet development workflow.  It enables you to: Define your model objects by simply writing “plain old classes” with no base classes or visual designer required Use a “convention over configuration” approach that enables database persistence without explicitly configuring anything Optionally override the convention-based persistence and use a fluent code API to fully customize the persistence mapping Optionally auto-create a database based on the model classes you define – allowing you to start from code first I’ve done several blog posts about EF Code First in the past – I really think it is great.  The good news is that it also works very well with SQL CE. The combination of SQL CE, EF Code First, and the new VS tooling support for SQL CE, enables a pretty nice workflow.  Below is a simple example of how you can use them to build a simple ASP.NET MVC 3 application. Step 1: Create a new ASP.NET MVC 3 Project We’ll begin by using the File->New Project menu command within Visual Studio to create a new ASP.NET MVC 3 project.  We’ll use the “Internet Project” template so that it has a default UI skin implemented: Step 2: Use NuGet to Install EFCodeFirst Next we’ll use the NuGet package manager (automatically installed by ASP.NET MVC 3) to add the EFCodeFirst library to our project.  We’ll use the Package Manager command shell to do this.  Bring up the package manager console within Visual Studio by selecting the View->Other Windows->Package Manager Console menu command.  Then type: install-package EFCodeFirst within the package manager console to download the EFCodeFirst library and have it be added to our project: When we enter the above command, the EFCodeFirst library will be downloaded and added to our application: Step 3: Build Some Model Classes Using a “code first” based development workflow, we will create our model classes first (even before we have a database).  We create these model classes by writing code. For this sample, we will right click on the “Models” folder of our project and add the below three classes to our project: The “Dinner” and “RSVP” model classes above are “plain old CLR objects” (aka POCO).  They do not need to derive from any base classes or implement any interfaces, and the properties they expose are standard .NET data-types.  No data persistence attributes or data code has been added to them.   The “NerdDinners” class derives from the DbContext class (which is supplied by EFCodeFirst) and handles the retrieval/persistence of our Dinner and RSVP instances from a database. Step 4: Listing Dinners We’ve written all of the code necessary to implement our model layer for this simple project.  Let’s now expose and implement the URL: /Dinners/Upcoming within our project.  We’ll use it to list upcoming dinners that happen in the future. We’ll do this by right-clicking on our “Controllers” folder and select the “Add->Controller” menu command.  We’ll name the Controller we want to create “DinnersController”.  We’ll then implement an “Upcoming” action method within it that lists upcoming dinners using our model layer above.  We will use a LINQ query to retrieve the data and pass it to a View to render with the code below: We’ll then right-click within our Upcoming method and choose the “Add-View” menu command to create an “Upcoming” view template that displays our dinners.  We’ll use the “empty” template option within the “Add View” dialog and write the below view template using Razor: Step 4: Configure our Project to use a SQL CE Database We have finished writing all of our code – our last step will be to configure a database connection-string to use. We will point our NerdDinners model class to a SQL CE database by adding the below <connectionString> to the web.config file at the top of our project: EF Code First uses a default convention where context classes will look for a connection-string that matches the DbContext class name.  Because we created a “NerdDinners” class earlier, we’ve also named our connectionstring “NerdDinners”.  Above we are configuring our connection-string to use SQL CE as the database, and telling it that our SQL CE database file will live within the \App_Data directory of our ASP.NET project. Step 5: Running our Application Now that we’ve built our application, let’s run it! We’ll browse to the /Dinners/Upcoming URL – doing so will display an empty list of upcoming dinners: You might ask – but where did it query to get the dinners from? We didn’t explicitly create a database?!? One of the cool features that EF Code-First supports is the ability to automatically create a database (based on the schema of our model classes) when the database we point it at doesn’t exist.  Above we configured  EF Code-First to point at a SQL CE database in the \App_Data\ directory of our project.  When we ran our application, EF Code-First saw that the SQL CE database didn’t exist and automatically created it for us. Step 6: Using VS 2010 SP1 to Explore our newly created SQL CE Database Click the “Show all Files” icon within the Solution Explorer and you’ll see the “NerdDinners.sdf” SQL CE database file that was automatically created for us by EF code-first within the \App_Data\ folder: We can optionally right-click on the file and “Include in Project" to add it to our solution: We can also double-click the file (regardless of whether it is added to the project) and VS 2010 SP1 will open it as a database we can edit within the “Server Explorer” tab of the IDE. Below is the view we get when we double-click our NerdDinners.sdf SQL CE file.  We can drill in to see the schema of the Dinners and RSVPs tables in the tree explorer.  Notice how two tables - Dinners and RSVPs – were automatically created for us within our SQL CE database.  This was done by EF Code First when we accessed the NerdDinners class by running our application above: We can right-click on a Table and use the “Show Table Data” command to enter some upcoming dinners in our database: We’ll use the built-in editor that VS 2010 SP1 supports to populate our table data below: And now when we hit “refresh” on the /Dinners/Upcoming URL within our browser we’ll see some upcoming dinners show up: Step 7: Changing our Model and Database Schema Let’s now modify the schema of our model layer and database, and walkthrough one way that the new VS 2010 SP1 Tooling support for SQL CE can make this easier.  With EF Code-First you typically start making database changes by modifying the model classes.  For example, let’s add an additional string property called “UrlLink” to our “Dinner” class.  We’ll use this to point to a link for more information about the event: Now when we re-run our project, and visit the /Dinners/Upcoming URL we’ll see an error thrown: We are seeing this error because EF Code-First automatically created our database, and by default when it does this it adds a table that helps tracks whether the schema of our database is in sync with our model classes.  EF Code-First helpfully throws an error when they become out of sync – making it easier to track down issues at development time that you might otherwise only find (via obscure errors) at runtime.  Note that if you do not want this feature you can turn it off by changing the default conventions of your DbContext class (in this case our NerdDinners class) to not track the schema version. Our model classes and database schema are out of sync in the above example – so how do we fix this?  There are two approaches you can use today: Delete the database and have EF Code First automatically re-create the database based on the new model class schema (losing the data within the existing DB) Modify the schema of the existing database to make it in sync with the model classes (keeping/migrating the data within the existing DB) There are a couple of ways you can do the second approach above.  Below I’m going to show how you can take advantage of the new VS 2010 SP1 Tooling support for SQL CE to use a database schema tool to modify our database structure.  We are also going to be supporting a “migrations” feature with EF in the future that will allow you to automate/script database schema migrations programmatically. Step 8: Modify our SQL CE Database Schema using VS 2010 SP1 The new SQL CE Tooling support within VS 2010 SP1 makes it easy to modify the schema of our existing SQL CE database.  To do this we’ll right-click on our “Dinners” table and choose the “Edit Table Schema” command: This will bring up the below “Edit Table” dialog.  We can rename, change or delete any of the existing columns in our table, or click at the bottom of the column listing and type to add a new column.  Below I’ve added a new “UrlLink” column of type “nvarchar” (since our property is a string): When we click ok our database will be updated to have the new column and our schema will now match our model classes. Because we are manually modifying our database schema, there is one additional step we need to take to let EF Code-First know that the database schema is in sync with our model classes.  As i mentioned earlier, when a database is automatically created by EF Code-First it adds a “EdmMetadata” table to the database to track schema versions (and hash our model classes against them to detect mismatches between our model classes and the database schema): Since we are manually updating and maintaining our database schema, we don’t need this table – and can just delete it: This will leave us with just the two tables that correspond to our model classes: And now when we re-run our /Dinners/Upcoming URL it will display the dinners correctly: One last touch we could do would be to update our view to check for the new UrlLink property and render a <a> link to it if an event has one: And now when we refresh our /Dinners/Upcoming we will see hyperlinks for the events that have a UrlLink stored in the database: Summary SQL CE provides a free, embedded, database engine that you can use to easily enable database storage.  With SQL CE 4 you can now take advantage of it within ASP.NET projects and applications (both Web Forms and MVC). VS 2010 SP1 provides tooling support that enables you to easily create, edit and modify SQL CE databases – as well as use the standard EF designer against them.  This allows you to re-use your existing skills and data knowledge while taking advantage of an embedded database option.  This is useful both for small applications (where you don’t need the scalability of a full SQL Server), as well as for development and testing scenarios – where you want to be able to rapidly develop/test your application without having a full database instance.  SQL CE makes it easy to later migrate your data to a full SQL Server or SQL Azure instance if you want to – without having to change any code in your application.  All we would need to change in the above two scenarios is the <connectionString> value within the web.config file in order to have our code run against a full SQL Server.  This provides the flexibility to scale up your application starting from a small embedded database solution as needed. Hope this helps, Scott P.S. In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu

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  • SQL SERVER – Why Do We Need Data Quality Services – Importance and Significance of Data Quality Services (DQS)

    - by pinaldave
    Databases are awesome.  I’m sure my readers know my opinion about this – I have made SQL Server my life’s work after all!  I love technology and all things computer-related.  Of course, even with my love for technology, I have to admit that it has its limits.  For example, it takes a human brain to notice that data has been input incorrectly.  Computer “brains” might be faster than humans, but human brains are still better at pattern recognition.  For example, a human brain will notice that “300” is a ridiculous age for a human to be, but to a computer it is just a number.  A human will also notice similarities between “P. Dave” and “Pinal Dave,” but this would stump most computers. In a database, these sorts of anomalies are incredibly important.  Databases are often used by multiple people who rely on this data to be true and accurate, so data quality is key.  That is why the improved SQL Server features Master Data Management talks about Data Quality Services.  This service has the ability to recognize and flag anomalies like out of range numbers and similarities between data.  This allows a human brain with its pattern recognition abilities to double-check and ensure that P. Dave is the same as Pinal Dave. A nice feature of Data Quality Services is that once you set the rules for the program to follow, it will not only keep your data organized in the future, but go to the past and “fix up” any data that has already been entered.  It also allows you do combine data from multiple places and it will apply these rules across the board, so that you don’t have any weird issues that crop up when trying to fit a round peg into a square hole. There are two parts of Data Quality Services that help you accomplish all these neat things.  The first part is DQL Server, which you can think of as the hardware component of the system.  It is installed on the side of (it needs to install separately after SQL Server is installed) SQL Server and runs quietly in the background, performing all its cleanup services. DQS Client is the user interface that you can interact with to set the rules and check over your data.  There are three main aspects of Client: knowledge base management, data quality projects and administration.  Knowledge base management is the part of the system that allows you to set the rules, or program the “knowledge base,” so that your database is clean and consistent. Data Quality projects are what run in the background and clean up the data that is already present.  The administration allows you to check out what DQS Client is doing, change rules, and generally oversee the entire process.  The whole process is user-friendly and a pleasure to use.  I highly recommend implementing Data Quality Services in your database. Here are few of my blog posts which are related to Data Quality Services and I encourage you to try this out. SQL SERVER – Installing Data Quality Services (DQS) on SQL Server 2012 SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS SQL SERVER – DQS Error – Cannot connect to server – A .NET Framework error occurred during execution of user-defined routine or aggregate “SetDataQualitySessions” – SetDataQualitySessionPhaseTwo SQL SERVER – Configuring Interactive Cleansing Suggestion Min Score for Suggestions in Data Quality Services (DQS) – Sensitivity of Suggestion SQL SERVER – Unable to DELETE Project in Data Quality Projects (DQS) 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: Data Quality Services, DQS

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  • Welcome Oracle Data Integration 12c: Simplified, Future-Ready Solutions with Extreme Performance

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 The big day for the Oracle Data Integration team has finally arrived! It is my honor to introduce you to Oracle Data Integration 12c. Today we announced 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. With the 12c release Oracle becomes the new leader in the data integration and replication technologies as no other vendor offers such a complete set of data integration capabilities for pervasive, continuous access to trusted data across Oracle platforms as well as third-party systems and applications. Oracle Data Integration 12c release addresses data-driven organizations’ critical and evolving data integration requirements under 3 key themes: Future-Ready Solutions Extreme Performance Fast Time-to-Value       There are many new features that support these key differentiators for Oracle Data Integrator 12c and for Oracle GoldenGate 12c. In this first 12c blog post, I will highlight only a few:·Future-Ready Solutions to Support Current and Emerging Initiatives: Oracle Data Integration offer robust and reliable solutions for key technology trends including cloud computing, big data analytics, real-time business intelligence and continuous data availability. Via the tight integration with Oracle’s database, middleware, and application offerings Oracle Data Integration will continue to support the new features and capabilities right away as these products evolve and provide advance features. E    Extreme Performance: Both GoldenGate and Data Integrator are known for their high performance. The new release widens the gap even further against competition. Oracle GoldenGate 12c’s Integrated Delivery feature enables higher throughput via a special application programming interface into Oracle Database. As mentioned in the press release, customers already report up to 5X higher performance compared to earlier versions of GoldenGate. Oracle Data Integrator 12c introduces parallelism that significantly increases its performance as well. Fast Time-to-Value via Higher IT Productivity and Simplified Solutions:  Oracle Data Integrator 12c’s new flow-based declarative UI brings superior developer productivity, ease of use, and ultimately fast time to market for end users.  It also gives the ability to seamlessly reuse mapping logic speeds development.Oracle GoldenGate 12c ‘s Integrated Delivery feature automatically optimally tunes the process, saving time while improving performance. This is just a quick glimpse into Oracle Data Integrator 12c and Oracle GoldenGate 12c. On November 12th we will reveal much more about the new release in our video webcast "Introducing 12c for Oracle Data Integration". Our customer and partner speakers, including SolarWorld, BT, Rittman Mead will join us in launching the new release. Please join us at this free event to learn more from our executives about the 12c release, hear our customers’ perspectives on the new features, and ask your questions to our experts in the live Q&A. Also, please continue to follow our blogs, tweets, and Facebook updates as we unveil more about the new features of the latest release. /* 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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; 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-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • CoreData Model Objects for API

    - by theiOSguy
    I am using CoreData in my application. I want to abstract out all the CoreData related stuff as an API so that the consume can use the API instead of directly using CoreData and its generated model objects. CoreData generates the managed objects model as following @interface Person : NSManagedObject @end I want to define my API for example MyAPI and it has a function called as createPerson:(Person*)p; So the consumer of this createPerson API needs to create a Person data object (like POJO in java world) and invoke this API. But I cannot create Person object using Person *p = [Person alloc] init] because the designated initializer for this Person model created by CoreData does not allow this type of creation. So should I define corresponding user facing data object may be PersonDO and this API should take that instead to carry the data into the API implementation? Is my approach right? Any expert advise if design the API this way is a good design pattern?

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  • How and when is something considered industry standard?

    - by Sonny Boy
    Hey all, I'm currently working on a proposal for my organization which includes a shift from waterfall development methodology over to a Scrum framework. As I work for a university, citations for all of my work is extremely important. As I was looking to add a citation for my statement of Agile being the industry standard, I kind of hit a wall. Who is it that decides when something become industry standard and how is that decision made?

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  • Big Data – Final Wrap and What Next – Day 21 of 21

    - by Pinal Dave
    In yesterday’s blog post we explored various resources related to learning Big Data and in this blog post we will wrap up this 21 day series on Big Data. I have been exploring various terms and technology related to Big Data this entire month. It was indeed fun to write about Big Data in 21 days but the subject of Big Data is much bigger and larger than someone can cover it in 21 days. My first goal was to write about the basics and I think we have got that one covered pretty well. During this 21 days I have received many questions and answers related to Big Data. I have covered a few of the questions in this series and a few more I will be covering in the next coming months. Now after understanding Big Data basics. I am personally going to do a list of the things next. I thought I will share the same with you as this will give you a good idea how to continue the journey of the Big Data. Build a schedule to read various Apache documentations Watch all Pluralsight Courses Explore HortonWorks Sandbox Start building presentation about Big Data – this is a great way to learn something new Present in User Groups Meetings on Big Data Topics Write more blog posts about Big Data I am going to continue learning about Big Data – I want you to continue learning Big Data. Please leave a comment how you are going to continue learning about Big Data. I will publish all the informative comments on this blog with due credit. I want to end this series with the infographic by UMUC. 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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  • SQL Developer Data Modeler v3.3 Early Adopter: Link Model Objects Across Designs

    - by thatjeffsmith
    The third post in our “What’s New in SQL Developer Data Modeler v3.3” series, SQL Developer Data Modeler now allows you to link objects across models. If you need to catch up on the earlier posts, here are the first two: New and Improved Search Collaborative Design via Excel Today’s post is a very simple and straightforward discussion on how to share objects across models and designs. In previous releases you could easily copy and paste objects between models and designs. Simply select your object, right-click and select ‘Copy’ Once copied, paste it into your other designs and then make changes as required. Once you paste the object, it is no longer associated with the source it was copied from. You are free to make any changes you want in the new location without affecting the source material. And it works the other way as well – make any changes to the source material and the new object is also unaffected. However. What if you want to LINK a model object instead of COPYING it? In version 3.3, you can now do this. Simply drag and drop the object instead of copy and pasting it. Select the object, in this case a relational model table, and drag it to your other model. It’s as simple as it sounds, here’s a little animated GIF to show you what I’m talking about. Drag and drop between models/designs to LINK an object Notes The ‘linked’ object cannot be modified from the destination space Updating the source object will propagate the changes forward to wherever it’s been linked You can drag a linked object to another design, so dragging from A - B and then from B - C will work Linked objects are annotated in the model with a ‘Chain’ bitmap, see below This object has been linked from another design/model and cannot be modified. A very simple feature, but I like the flexibility here. Copy and paste = new independent object. Drag and drop = linked object.

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  • Partner Webcast - Focus on Oracle Data Profiling and Data Quality 11g

    - by lukasz.romaszewski(at)oracle.com
    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:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; 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-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:RO;} Partner Webcast Focus on Oracle Data Profiling and Data Quality 11g February 24th, 12am  CET   Oracle offers an integrated suite Data Quality software architected to discover and correct today's data quality problems and establish a platform prepared for tomorrow's yet unknown data challenges. Oracle Data Profiling provides data investigation, discovery, and profiling in support of quality, migration, integration, stewardship, and governance initiatives. It includes a broad range of features that expand upon basic profiling, including automated monitoring, business-rule validation, and trend analysis. Oracle Data Quality for Data Integrator provides cleansing, standardization, matching, address validation, location enrichment, and linking functions for global customer data and operational business data. It ensures that data adheres to established standards that are adaptable to fit each organization's specific needs.  Both single - and double - byte data are processed in local languages to provide a unique and centralized view of customers, products and services.   During this in-person briefing, Data Integration Solution Specialists will be providing a technical overview and a walkthrough.   Agenda ·         Oracle Data Integration Strategy overview ·         A focus on Oracle Data Profiling and Oracle Data Quality for Data Integrator: o   Oracle Data Profiling o   Oracle Data Quality for Data Integrator o   Live demoo   Q&A Delivery Format  This FREE online LIVE eSeminar will be delivered over the Web and Conference Call. Registrations   received less than 24hours  prior to start time may not receive confirmation to attend. To register , click here. For any questions please contact [email protected]

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  • Erfolgreich durchstarten als Partner mit dem Open Market Model

    - by A&C Redaktion
    Wer als Oracle Partner bei dem erfolgreichen Programm OMM (Open Market Model) mitmacht, profitiert vierfach: Projektschutz oder Tipp-Provision, auf der Basis der OMM-Policy "Guter Name" durch kontinuierliche Projektregistrierungen Jedes erfolgreiche OMM-Projekt zählt einen Transaktionspunkt Direkter Ansprechpartner, der OMM Manager als Vermittler zum Oracle Sales Gönnen Sie sich diese 3 Minuten und Sie wissen dann, warum OMM auch für Sie interessant sein kann!

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  • Best practice to handle Parent Form Child Form relation using Presentation Model

    - by Rajarshi
    According to Presentation Model notes by Martin Fowler and also on MSDN documentation about Presentation Model, it is explained that the Presentation Model Class should be unaware of the UI class and similarly Business Model Class should be unaware of the Presentation Model class. The UI should databind extensively to the Presentation Model, the Presentation Model in turn will co-ordinate with one or more Domain/Business Model objects to get the job done. The Presentation Model basically presents the Domain Model data in a way to facilitate maximum data binding in UI, allowing the UI take as less decisions as possible and thus increase testability of Presentation behaviours. This also makes the presentation model class generic, i.e. agnostic of any particular UI technology. Now, consider there is a List form (say CustomerList) and there is another Root form (say Customer) and there is a Use Case of allowing to Edit a Customer from the CustomerList form on a button click. For simplicity of discussion, consider that some actions took place when Customer List is opened from menu (i.e. Customer menu clicked) and the Customer List has been shown from the Menu click event. Now as per the above Use Case, I need to open the Customer Root UI (single Customer) from the Customer List. How do I do that? Build necessary objects (BusinessModel, PresentationModel, UI) in click event of Edit button and call CustomerEdit UI from there? Build the CustomerEdit UI from Presentation Model Class and show UI from presentation model? this can be done in any of the two ways below - a. Create objects in the following sequence DomainModel-PresentationModel-UIForm b. Use Unity.Resolve(); Either ways, Presentation Model is violated as the P model now has to the refer the concrete UI assembly, where CustomerEdit is located. Also the P Model has to refer and use a WinForm directly making it less UI technology agnostic. Even though the violations are in theory and can be ignored, I would still seek the community's opinion about whether I am going wrong direction. Please suggest if there's a better way to call the Child Form from the List (Parent) Form. Rajarshi

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  • I keep on getting "save operation failure" after any change on my XCode Data Model

    - by Philip Schoch
    I started using Core Data for iPhone development. I started out by creating a very simple entity (called Evaluation) with just one string property (called evaluationTopic). I had following code for inserting a fresh string: - (void)insertNewObject { // Create a new instance of the entity managed by the fetched results controller. NSManagedObjectContext *context = [fetchedResultsController managedObjectContext]; NSEntityDescription *entity = [[fetchedResultsController fetchRequest] entity]; NSManagedObject *newManagedObject = [NSEntityDescription insertNewObjectForEntityForName:[entity name] inManagedObjectContext:context]; // If appropriate, configure the new managed object. [newManagedObject setValue:@"My Repeating String" forKey:@"evaluationTopic"]; // Save the context. NSError *error; if (![context save:&error]) { // Handle the error... } [self.tableView reloadData]; } This worked perfectly fine and by pushing the +button a new "My Repeating String" would be added to the table view and be in persistent store. I then pressed "Design - Add Model Version" in XCode. I added three entities to the existing entity and also added new properties to the existing "Evaluation" entity. Then, I created new files off the entities by pressing "File - New File - Managed Object Classes" and created a new .h and .m file for my four entities, including the "Evaluation" entity with Evaluation.h and Evaluation.m. Now I changed the model version by setting "Design - Data Model - Set Current Version". After having done all this, I changed my insertMethod: - (void)insertNewObject { // Create a new instance of the entity managed by the fetched results controller. NSManagedObjectContext *context = [fetchedResultsController managedObjectContext]; NSEntityDescription *entity = [[fetchedResultsController fetchRequest] entity]; Evaluation *evaluation = (Evaluation *) [NSEntityDescription insertNewObjectForEntityForName:[entity name] inManagedObjectContext:context]; // If appropriate, configure the new managed object. [evaluation setValue:@"My even new string" forKey:@"evaluationSpeechTopic"]; // Save the context. NSError *error; if (![context save:&error]) { // Handle the error... } [self.tableView reloadData]; } This does not work though! Every time I want to add a row the simulator crashes and I get the following: "NSInternalInconsistencyException', reason: 'This NSPersistentStoreCoordinator has no persistent stores. It cannot perform a save operation.'" I had this error before I knew about creating new version after changing anything on the datamodel, but why is this still coming up? Do I need to do any mapping (even though I just added entities and properties that did not exist before?). In the Apple Dev tutorial it sounds very easy but I have been struggling with this for long time, never worked after changing model version.

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  • Dynamic Typed Table/Model in J2EE?

    - by Viele
    Hi, Usually with J2EE when we create Model, we define the fields and types of fields through XML or annotation before compilation time. Is there a way to change those in runtime? or better, is it possible to create a new Model based on the user's input during the runtime? such that the number of columns and types of fields are dynamic (determined at runtime)? Help is much appreciated. Thank you.

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  • Big Data – Buzz Words: What is HDFS – Day 8 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is MapReduce. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – HDFS. What is HDFS ? HDFS stands for Hadoop Distributed File System and it is a primary storage system used by Hadoop. It provides high performance access to data across Hadoop clusters. It is usually deployed on low-cost commodity hardware. In commodity hardware deployment server failures are very common. Due to the same reason HDFS is built to have high fault tolerance. The data transfer rate between compute nodes in HDFS is very high, which leads to reduced risk of failure. HDFS creates smaller pieces of the big data and distributes it on different nodes. It also copies each smaller piece to multiple times on different nodes. Hence when any node with the data crashes the system is automatically able to use the data from a different node and continue the process. This is the key feature of the HDFS system. Architecture of HDFS The architecture of the HDFS is master/slave architecture. An HDFS cluster always consists of single NameNode. This single NameNode is a master server and it manages the file system as well regulates access to various files. In additional to NameNode there are multiple DataNodes. There is always one DataNode for each data server. In HDFS a big file is split into one or more blocks and those blocks are stored in a set of DataNodes. The primary task of the NameNode is to open, close or rename files and directory and regulate access to the file system, whereas the primary task of the DataNode is read and write to the file systems. DataNode is also responsible for the creation, deletion or replication of the data based on the instruction from NameNode. In reality, NameNode and DataNode are software designed to run on commodity machine build in Java language. Visual Representation of HDFS Architecture Let us understand how HDFS works with the help of the diagram. Client APP or HDFS Client connects to NameSpace as well as DataNode. Client App access to the DataNode is regulated by NameSpace Node. NameSpace Node allows Client App to connect to the DataNode based by allowing the connection to the DataNode directly. A big data file is divided into multiple data blocks (let us assume that those data chunks are A,B,C and D. Client App will later on write data blocks directly to the DataNode. Client App does not have to directly write to all the node. It just has to write to any one of the node and NameNode will decide on which other DataNode it will have to replicate the data. In our example Client App directly writes to DataNode 1 and detained 3. However, data chunks are automatically replicated to other nodes. All the information like in which DataNode which data block is placed is written back to NameNode. High Availability During Disaster Now as multiple DataNode have same data blocks in the case of any DataNode which faces the disaster, the entire process will continue as other DataNode will assume the role to serve the specific data block which was on the failed node. This system provides very high tolerance to disaster and provides high availability. If you notice there is only single NameNode in our architecture. If that node fails our entire Hadoop Application will stop performing as it is a single node where we store all the metadata. As this node is very critical, it is usually replicated on another clustered as well as on another data rack. Though, that replicated node is not operational in architecture, it has all the necessary data to perform the task of the NameNode in the case of the NameNode fails. The entire Hadoop architecture is built to function smoothly even there are node failures or hardware malfunction. It is built on the simple concept that data is so big it is impossible to have come up with a single piece of the hardware which can manage it properly. We need lots of commodity (cheap) hardware to manage our big data and hardware failure is part of the commodity servers. To reduce the impact of hardware failure Hadoop architecture is built to overcome the limitation of the non-functioning hardware. Tomorrow In tomorrow’s blog post we will discuss the importance of the relational database in 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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  • Why can't the IT industry deliver large, faultless projects quickly as in other industries?

    - by MainMa
    After watching National Geographic's MegaStructures series, I was surprised how fast large projects are completed. Once the preliminary work (design, specifications, etc.) is done on paper, the realization itself of huge projects take just a few years or sometimes a few months. For example, Airbus A380 "formally launched on Dec. 19, 2000", and "in the Early March, 2005", the aircraft was already tested. The same goes for huge oil tankers, skyscrapers, etc. Comparing this to the delays in software industry, I can't help wondering why most IT projects are so slow, or more precisely, why they cannot be as fast and faultless, at the same scale, given enough people? Projects such as the Airbus A380 present both: Major unforeseen risks: while this is not the first aircraft built, it still pushes the limits if the technology and things which worked well for smaller airliners may not work for the larger one due to physical constraints; in the same way, new technologies are used which were not used yet, because for example they were not available in 1969 when Boeing 747 was done. Risks related to human resources and management in general: people quitting in the middle of the project, inability to reach a person because she's on vacation, ordinary human errors, etc. With those risks, people still achieve projects like those large airliners in a very short period of time, and despite the delivery delays, those projects are still hugely successful and of a high quality. When it comes to software development, the projects are hardly as large and complicated as an airliner (both technically and in terms of management), and have slightly less unforeseen risks from the real world. Still, most IT projects are slow and late, and adding more developers to the project is not a solution (going from a team of ten developer to two thousand will sometimes allow to deliver the project faster, sometimes not, and sometimes will only harm the project and increase the risk of not finishing it at all). Those which are still delivered may often contain a lot of bugs, requiring consecutive service packs and regular updates (imagine "installing updates" on every Airbus A380 twice per week to patch the bugs in the original product and prevent the aircraft from crashing). How can such differences be explained? Is it due exclusively to the fact that software development industry is too young to be able to manage thousands of people on a single project in order to deliver large scale, nearly faultless products very fast?

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  • New Big Data Appliance Security Features

    - by mgubar
    The Oracle Big Data Appliance (BDA) is an engineered system for big data processing.  It greatly simplifies the deployment of an optimized Hadoop Cluster – whether that cluster is used for batch or real-time processing.  The vast majority of BDA customers are integrating the appliance with their Oracle Databases and they have certain expectations – especially around security.  Oracle Database customers have benefited from a rich set of security features:  encryption, redaction, data masking, database firewall, label based access control – and much, much more.  They want similar capabilities with their Hadoop cluster.    Unfortunately, Hadoop wasn’t developed with security in mind.  By default, a Hadoop cluster is insecure – the antithesis of an Oracle Database.  Some critical security features have been implemented – but even those capabilities are arduous to setup and configure.  Oracle believes that a key element of an optimized appliance is that its data should be secure.  Therefore, by default the BDA delivers the “AAA of security”: authentication, authorization and auditing. Security Starts at Authentication A successful security strategy is predicated on strong authentication – for both users and software services.  Consider the default configuration for a newly installed Oracle Database; it’s been a long time since you had a legitimate chance at accessing the database using the credentials “system/manager” or “scott/tiger”.  The default Oracle Database policy is to lock accounts thereby restricting access; administrators must consciously grant access to users. Default Authentication in Hadoop By default, a Hadoop cluster fails the authentication test. For example, it is easy for a malicious user to masquerade as any other user on the system.  Consider the following scenario that illustrates how a user can access any data on a Hadoop cluster by masquerading as a more privileged user.  In our scenario, the Hadoop cluster contains sensitive salary information in the file /user/hrdata/salaries.txt.  When logged in as the hr user, you can see the following files.  Notice, we’re using the Hadoop command line utilities for accessing the data: $ hadoop fs -ls /user/hrdataFound 1 items-rw-r--r--   1 oracle supergroup         70 2013-10-31 10:38 /user/hrdata/salaries.txt$ hadoop fs -cat /user/hrdata/salaries.txtTom Brady,11000000Tom Hanks,5000000Bob Smith,250000Oprah,300000000 User DrEvil has access to the cluster – and can see that there is an interesting folder called “hrdata”.  $ hadoop fs -ls /user Found 1 items drwx------   - hr supergroup          0 2013-10-31 10:38 /user/hrdata However, DrEvil cannot view the contents of the folder due to lack of access privileges: $ hadoop fs -ls /user/hrdata ls: Permission denied: user=drevil, access=READ_EXECUTE, inode="/user/hrdata":oracle:supergroup:drwx------ Accessing this data will not be a problem for DrEvil. He knows that the hr user owns the data by looking at the folder’s ACLs. To overcome this challenge, he will simply masquerade as the hr user. On his local machine, he adds the hr user, assigns that user a password, and then accesses the data on the Hadoop cluster: $ sudo useradd hr $ sudo passwd $ su hr $ hadoop fs -cat /user/hrdata/salaries.txt Tom Brady,11000000 Tom Hanks,5000000 Bob Smith,250000 Oprah,300000000 Hadoop has not authenticated the user; it trusts that the identity that has been presented is indeed the hr user. Therefore, sensitive data has been easily compromised. Clearly, the default security policy is inappropriate and dangerous to many organizations storing critical data in HDFS. Big Data Appliance Provides Secure Authentication The BDA provides secure authentication to the Hadoop cluster by default – preventing the type of masquerading described above. It accomplishes this thru Kerberos integration. Figure 1: Kerberos Integration The Key Distribution Center (KDC) is a server that has two components: an authentication server and a ticket granting service. The authentication server validates the identity of the user and service. Once authenticated, a client must request a ticket from the ticket granting service – allowing it to access the BDA’s NameNode, JobTracker, etc. At installation, you simply point the BDA to an external KDC or automatically install a highly available KDC on the BDA itself. Kerberos will then provide strong authentication for not just the end user – but also for important Hadoop services running on the appliance. You can now guarantee that users are who they claim to be – and rogue services (like fake data nodes) are not added to the system. It is common for organizations to want to leverage existing LDAP servers for common user and group management. Kerberos integrates with LDAP servers – allowing the principals and encryption keys to be stored in the common repository. This simplifies the deployment and administration of the secure environment. Authorize Access to Sensitive Data Kerberos-based authentication ensures secure access to the system and the establishment of a trusted identity – a prerequisite for any authorization scheme. Once this identity is established, you need to authorize access to the data. HDFS will authorize access to files using ACLs with the authorization specification applied using classic Linux-style commands like chmod and chown (e.g. hadoop fs -chown oracle:oracle /user/hrdata changes the ownership of the /user/hrdata folder to oracle). Authorization is applied at the user or group level – utilizing group membership found in the Linux environment (i.e. /etc/group) or in the LDAP server. For SQL-based data stores – like Hive and Impala – finer grained access control is required. Access to databases, tables, columns, etc. must be controlled. And, you want to leverage roles to facilitate administration. Apache Sentry is a new project that delivers fine grained access control; both Cloudera and Oracle are the project’s founding members. Sentry satisfies the following three authorization requirements: Secure Authorization:  the ability to control access to data and/or privileges on data for authenticated users. Fine-Grained Authorization:  the ability to give users access to a subset of the data (e.g. column) in a database Role-Based Authorization:  the ability to create/apply template-based privileges based on functional roles. With Sentry, “all”, “select” or “insert” privileges are granted to an object. The descendants of that object automatically inherit that privilege. A collection of privileges across many objects may be aggregated into a role – and users/groups are then assigned that role. This leads to simplified administration of security across the system. Figure 2: Object Hierarchy – granting a privilege on the database object will be inherited by its tables and views. Sentry is currently used by both Hive and Impala – but it is a framework that other data sources can leverage when offering fine-grained authorization. For example, one can expect Sentry to deliver authorization capabilities to Cloudera Search in the near future. Audit Hadoop Cluster Activity Auditing is a critical component to a secure system and is oftentimes required for SOX, PCI and other regulations. The BDA integrates with Oracle Audit Vault and Database Firewall – tracking different types of activity taking place on the cluster: Figure 3: Monitored Hadoop services. At the lowest level, every operation that accesses data in HDFS is captured. The HDFS audit log identifies the user who accessed the file, the time that file was accessed, the type of access (read, write, delete, list, etc.) and whether or not that file access was successful. The other auditing features include: MapReduce:  correlate the MapReduce job that accessed the file Oozie:  describes who ran what as part of a workflow Hive:  captures changes were made to the Hive metadata The audit data is captured in the Audit Vault Server – which integrates audit activity from a variety of sources, adding databases (Oracle, DB2, SQL Server) and operating systems to activity from the BDA. Figure 4: Consolidated audit data across the enterprise.  Once the data is in the Audit Vault server, you can leverage a rich set of prebuilt and custom reports to monitor all the activity in the enterprise. In addition, alerts may be defined to trigger violations of audit policies. Conclusion Security cannot be considered an afterthought in big data deployments. Across most organizations, Hadoop is managing sensitive data that must be protected; it is not simply crunching publicly available information used for search applications. The BDA provides a strong security foundation – ensuring users are only allowed to view authorized data and that data access is audited in a consolidated framework.

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  • How to present a stable data model in a public API that allows internal data structures to be changed without breaking the public view of the data?

    - by Max Palmer
    I am in the process of developing an application that allows users to write C# scripts. These scripts allow users to call selected methods and to access and manipulate data in a document. This works well, however, in the development version, scripts access the document's (internal) data structures directly. This means that if we were to change the internal data model/structure, there is a good chance that someone's script will no longer compile. We obviously want to prevent this breaking change from happening, but still want to allow the user to write sensible C# code (whilst not restricting how we develop our internal data model as a result). We therefore need to decouple our scripting API and its data structures from our internal methods and data structures. We've a few ideas as to how we might allow the user to access a what is effectively a stable public version of the document's internal data*, but I wanted to throw the question out there to someone who might have some real experience of this problem. NB our internal document's data structure is quite complex and it could be quite difficult to wrap. We know we want to expose as little as possible in our public API, especially as once it's out there, it's out there for good. Can anyone help? How do scripting languages / APIs decouple their public API and data structures from their internal data structures? Is there no real alternative to having to write a complex interaction layer? If we need to do this, what's a good approach or pattern for wrapping complex data structures that include nested objects, including collections? I've looked at the API facade pattern, which looks like it's trying to address these kinds of issues, but are there alternatives? *One idea is to build a data facade that is kept stable across versions of our application. The facade exposes a set of facade data objects that are used in the script code. These maintain backwards compatibility and wrap access to our internal document's data model.

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  • Metalanguage like BNF or XML-Schema to validate a tree-instance against a tree-model

    - by Stefan
    Hi! I'm implementing a new machine learning algorithm in Java that extracts a prototype datastructure from a set of structured datasets (tree-structure). As im developing a generic library for that purpose, i kept my design independent from concrete data-representations like XML. My problem now is that I need a way to define a data model, which is basically a ruleset describing valid trees, against which a set of trees is being matched. I thought of using BNF or a similar dialect. Basically I need a way to iterate through the space of all valid TreeNodes defined by the ModelTree (Like a search through the search space for algorithms like A*) so that i can compare my set of concrete trees with the model. I know that I'll have to deal with infinite spaces there but first things first. I know, it's rather tricky (and my sentences are pretty bumpy) but I would appreciate any clues. Thanks in advance, Stefan

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  • Mapping between 4+1 architectural view model & UML

    - by Sadeq Dousti
    I'm a bit confused about how the 4+1 architectural view model maps to UML. Wikipedia gives the following mapping: Logical view: Class diagram, Communication diagram, Sequence diagram. Development view: Component diagram, Package diagram Process view: Activity diagram Physical view: Deployment diagram Scenarios: Use-case diagram The paper Role of UML Sequence Diagram Constructs in Object Lifecycle Concept gives the following mapping: Logical view (class diagram (CD), object diagram (OD), sequence diagram (SD), collaboration diagram (COD), state chart diagram (SCD), activity diagram (AD)) Development view (package diagram, component diagram), Process view (use case diagram, CD, OD, SD, COD, SCD, AD), Physical view (deployment diagram), and Use case view (use case diagram, OD, SD, COD, SCD, AD) which combines the four mentioned above. The web page UML 4+1 View Materials presents the following mapping: Finally, the white paper Applying 4+1 View Architecture with UML 2 gives yet another mapping: Logical view class diagrams, object diagrams, state charts, and composite structures Process view sequence diagrams, communication diagrams, activity diagrams, timing diagrams, interaction overview diagrams Development view component diagrams Physical view deployment diagram Use case view use case diagram, activity diagrams I'm sure further search will reveal other mappings as well. While various people usually have different perspectives, I don't see why this is the case here. Specially, each UML diagram describes the system from a particular aspect. So, for instance, why the "sequence diagram" is considered as describing the "logical view" of the system by one author, while another author considers it as describing the "process view"? Could you please help me clarify the confusion?

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  • Why Cornell University Chose Oracle Data Masking

    - by Troy Kitch
    One of the eight Ivy League schools, Cornell University found itself in the unfortunate position of having to inform over 45,000 University community members that their personal information had been breached when a laptop was stolen. To ensure this wouldn’t happen again, Cornell took steps to ensure that data used for non-production purposes is de-identified with Oracle Data Masking. A recent podcast highlights why organizations like Cornell are choosing Oracle Data Masking to irreversibly de-identify production data for use in non-production environments. Organizations often copy production data, that contains sensitive information, into non-production environments so they can test applications and systems using “real world” information. Data in non-production has increasingly become a target of cyber criminals and can be lost or stolen due to weak security controls and unmonitored access. Similar to production environments, data breaches in non-production environments can cost millions of dollars to remediate and cause irreparable harm to reputation and brand. Cornell’s applications and databases help carry out the administrative and academic mission of the university. They are running Oracle PeopleSoft Campus Solutions that include highly sensitive faculty, student, alumni, and prospective student data. This data is supported and accessed by a diverse set of developers and functional staff distributed across the university. Several years ago, Cornell experienced a data breach when an employee’s laptop was stolen.  Centrally stored backup information indicated there was sensitive data on the laptop. With no way of knowing what the criminal intended, the university had to spend significant resources reviewing data, setting up service centers to handle constituent concerns, and provide free credit checks and identity theft protection services—all of which cost money and took time away from other projects. To avoid this issue in the future Cornell came up with several options; one of which was to sanitize the testing and training environments. “The project management team was brought in and they developed a project plan and implementation schedule; part of which was to evaluate competing products in the market-space and figure out which one would work best for us.  In the end we chose Oracle’s solution based on its architecture and its functionality.” – Tony Damiani, Database Administration and Business Intelligence, Cornell University The key goals of the project were to mask the elements that were identifiable as sensitive in a consistent and efficient manner, but still support all the previous activities in the non-production environments. Tony concludes,  “What we saw was a very minimal impact on performance. The masking process added an additional three hours to our refresh window, but it was well worth that time to secure the environment and remove the sensitive data. I think some other key points you can keep in mind here is that there was zero impact on the production environment. Oracle Data Masking works in non-production environments only. Additionally, the risk of exposure has been significantly reduced and the impact to business was minimal.” With Oracle Data Masking organizations like Cornell can: Make application data securely available in non-production environments Prevent application developers and testers from seeing production data Use an extensible template library and policies for data masking automation Gain the benefits of referential integrity so that applications continue to work Listen to the podcast to hear the complete interview.  Learn more about Oracle Data Masking by registering to watch this SANS Institute Webcast and view this short demo.

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  • Removing Barriers to Create Effective Data Models

    After years of creating and maintaining data models, I have started to notice common barriers that decrease the accuracy and usefulness of models. In my opinion, the main causes of these barriers are the lack of knowledge and communication from within a company. The lack of knowledge in regards to data models or data modeling can take many forms. Company Culture Knowledge Whether documented or undocumented, existing business rules of a company can affect how data is modeled. For example, if a company only allows 1 assigned person per customer to be able to manipulate a customer’s record then then a data model that includes an associated table that joins customers and employee’s would be unneeded because that would allow for the possibility of multiple employees to handle a customer because of the potential for a many to many relationship between Customers and Employees. Technical Knowledge Depending on the data modeler’s proficiency in modeling data they can inadvertently cause issues and/or complications with a design without even noticing. It is important that companies share data modeling responsibilities so that the models are developed from multiple perspectives of a system, company and the original problem.  In addition, the tools that a company selects to create data models can also affect the accuracy of the model if designer are not familiar with the tools or the tools are too complex to use for the designer. Existing System Knowledge In order for a data modeler to model data for an existing system so that new changes can be applied to a system then they need to at least know the basic concepts of a system so that they can work within it. This will promote reusability of data and prevent the chance of duplicating data. Project Knowledge This should be pretty obvious, but it is very hard to create an accurate data model without knowing what data needs to be modeled. I have always found it strange that I have been asked to start modeling data prior to a client formalizing any requirements. Usually when this happens I have to make several iterations to a model, and the client still does not know exactly what they want.  In addition additional issues can arise when certain stakeholders of a project are not consulted prior to the design or after the project is over because it can cause miss understandings and confusion by the end user as well as possibly not solving the original problem for which a project is intended to solve. One common thread between each type of knowledge is that they can all be avoided through the use of good communication. For example, if a modeler is new to a company then they should ask older employees about any business specific rules that may be documented or undocumented that must be applied to projects in general. Furthermore, if a modeler is not really familiar with a specific data modeling software then they need to speak up and ask for help form other employees or their manager. This will not only help the modeler in the project, but also help them in future projects that they do for the company. Additionally, if a project is not clearly defined prior to a data modeler being assigned the modeling project then it is their responsibility to communicate with the other stakeholders to clarify any part of a project that is unclear so that the data model that is created is accurately aligned with a project.

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  • LLBLGen Pro feature highlights: model views

    - by FransBouma
    (This post is part of a series of posts about features of the LLBLGen Pro system) To be able to work with large(r) models, it's key you can view subsets of these models so you can have a better, more focused look at them. For example because you want to display how a subset of entities relate to one another in a different way than the list of entities. LLBLGen Pro offers this in the form of Model Views. Model Views are views on parts of the entity model of a project, and the subsets are displayed in a graphical way. Additionally, one can add documentation to a Model View. As Model Views are displaying parts of the model in a graphical way, they're easier to explain to people who aren't familiar with entity models, e.g. the stakeholders you're interviewing for your project. The documentation can then be used to communicate specifics of the elements on the model view to the developers who have to write the actual code. Below I've included an example. It's a model view on a subset of the entities of AdventureWorks. It displays several entities, their relationships (both relational and inheritance relationships) and also some specifics gathered from the interview with the stakeholder. As the information is inside the actual project the developer will work with, the information doesn't have to be converted back/from e.g .word documents or other intermediate formats, it's the same project. This makes sure there are less errors / misunderstandings. (of course you can hide the docked documentation pane or dock it to another corner). The Model View can contain entities which are placed in different groups. This makes it ideal to group entities together for close examination even though they're stored in different groups. The Model View is a first-class citizen of the code-generator. This means you can write templates which consume Model Views and generate code accordingly. E.g. you can write a template which generates a service per Model View and exposes the entities in the Model View as a single entity graph, fetched through a method. (This template isn't included in the LLBLGen Pro package, but it's easy to write it up yourself with the built-in template editor). Viewing an entity model in different ways is key to fully understand the entity model and Model Views help with that.

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  • How often do you use data structures (ie Binary Trees, Linked Lists) in your jobs/side projects?

    - by Chris2021
    It seems to me that, for everyday use, more primitive data structures like arrays get the job done just as well as a binary tree would. My question is how common is to use these structures when writing code for projects at work or projects that you pursue in your free time? I understand the better insertion time/deletion time/sorting time for certain structures but would that really matter that much if you were working with a relatively small amount of data?

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