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• Data Quality and Master Data Management Resources

  - by Dejan Sarka

  Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-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. Start improving the quality of your data now!

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• Deploying BAM Data Control Application to WLS server

  - by [email protected]

  var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-15829414-1"); pageTracker._trackPageview(); } catch(err) {} Typically we would test our ADF pages that use BAM Data control using integrated wls server (ADRS). If we have to deploy this same application to a standalone WLS we have to make sure we have the BAM server connection created in WLS.unless we do that we may face runtime errors.In Development mode of WLS(Reference) For development-mode WebLogic Server, you can set the mode to OVERWRITE to test user names and passwords. You can set the mode by running setDomainEnv.cmd or setDomainEnv.sh with the following option added to the command. Add the following to the JAVA_PROPERTIES entry in the <FMW_HOME>/user_projects/domains/<yourdomain>/bin/setDomainEnv.sh file: -Djps.app.credential.overwrite.allowed=true In Production mode of WLS Enable MDS Create and/or Register your MDS repository. For more details refer this Edit adf-config.xml from your application and add the following tag <adf-mds-config xmlns="http://xmlns.oracle.com/adf/mds/config">     <mds-config version="11.1.1.000">     <persistence-config>   <metadata-store-usages>     <metadata-store-usage default-cust-store="true" deploy-target="true" id="myRepos">     </metadata-store-usage>   </metadata-store-usages>   </persistence-config>           </mds-config>  </adf-mds-config>Deploy the application to WLS server after picking the appropriate repository during deployment from the MDS Repository dialog that pops up Enterprise Manager (Use these steps if using a version prior to 11gR1 PS1 release of JDeveloper) Go to EM (http://<host>:<port>/EMIn the left pane, deployments select Application1(your application)In the right pane, top dropdown select "System Mbean Browser->oracle.adf.share.connections->Server: AdminServer->Server: AdminServer->Application:<Appname>->ADFConnections"Right pane click "Operations->CreateConnection"Enter Connection Type as "BAMConnection"Enter the connection name same as the one defined in JdevClick "Invoke"Click "Return"Click on Operation->SaveNow in the ADFConnections in the navigator, select the connection just created and enter all the configuration details.Save and run the page. Enterprise Manager (Use these steps or the steps above if using 11gR1 PS1 or newer) Go to EM (http://<host>:<port>/EMIn the left pane, deployments select Application1(your application)In the right pane, click on "Application Deployment" to invoke to dropdown. In that select "ADF -> Configure ADF Connections"Select Connection Type as "BAM" from the drop downEnter Connection Type as to be the same as the one defined in JDevClick on "Create Connection". This should add a new row below under "BAM Connections"Select the new connection and click on the "Edit" icon. This should bring up a dialogSpecific appropriate values for all connection parameters - Username, password, BAM Server Host, BAM Server Port, Webtier Server Host, Webtier Server Port and BAM Webtier Protocol - and then click on OK to dismiss the dialogClick on "Apply"Run the page page.

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• Simple ADF page using BAM Data Control

  - by [email protected]

  var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-15829414-1"); pageTracker._trackPageview(); } catch(err) {} Purpose : In this blog I will walk you through very simple steps to create an ADF page using BAM data control connection.Details : Create the projectOpen JDeveloper (make sure you have installed the SOA extension for JDev)Create new Application using "Generic Application" template.Click on "Next"Shuttle  "ADF Faces" to right pane for the project technology.Click "Finish"Create a BAM connectionIn the resource palette click on "Folder->New Connection -> BAM"Enter the connection name and click "Next"Enter Connection details Click on "Test connection" and "Finish"Create the BAM Data Control Open the IDE connection created in above step.Drag and drop "Employees" to "Data controls" palette.Select "Flat Query" and Click "Finish".Create the View Create a new JSF page.From Data control Panel drag and drop "Employees->Query->ADF Read Only table"Right click and Run the page.

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• Using the BAM Interceptor with Continuation

  - by Charles Young

  Originally posted on: http://geekswithblogs.net/cyoung/archive/2014/06/02/using-the-bam-interceptor-with-continuation.aspxI’ve recently been resurrecting some code written several years ago that makes extensive use of the BAM Interceptor provided as part of BizTalk Server’s BAM event observation library.  In doing this, I noticed an issue with continuations.  Essentially, whenever I tried to configure one or more continuations for an activity, the BAM Interceptor failed to complete the activity correctly.   Careful inspection of my code confirmed that I was initializing and invoking the BAM interceptor correctly, so I was mystified.  However, I eventually found the problem.  It is a logical error in the BAM Interceptor code itself. The BAM Interceptor provides a useful mechanism for implementing dynamic tracking.  It supports configurable ‘track points’.  These are grouped into named ‘locations’.  BAM uses the term ‘step’ as a synonym for ‘location’.   Each track point defines a BAM action such as starting an activity, extracting a data item, enabling a continuation, etc.  Each step defines a collection of track points. Understanding Steps The BAM Interceptor provides an abstract model for handling configuration of steps.  It doesn’t, however, define any specific configuration mechanism (e.g., config files, SSO, etc.)  It is up to the developer to decide how to store, manage and retrieve configuration data.  At run time, this configuration is used to register track points which then drive the BAM Interceptor. The full semantics of a step are not immediately clear from Microsoft’s documentation.  They represent a point in a business activity where BAM tracking occurs.  They are named locations in the code.  What is less obvious is that they always represent either the full tracking work for a given activity or a discrete fragment of that work which commences with the start of a new activity or the continuation of an existing activity.  The BAM Interceptor enforces this by throwing an error if no ‘start new’ or ‘continue’ track point is registered for a named location. This constraint implies that each step must marked with an ‘end activity’ track point.  One of the peculiarities of BAM semantics is that when an activity is continued under a correlated ID, you must first mark the current activity as ‘ended’ in order to ensure the right housekeeping is done in the database.  If you re-start an ended activity under the same ID, you will leave the BAM import tables in an inconsistent state.  A step, therefore, always represents an entire unit of work for a given activity or continuation ID.  For activities with continuation, each unit of work is termed a ‘fragment’. Instance and Fragment State Internally, the BAM Interceptor maintains state data at two levels.  First, it represents the overall state of the activity using a ‘trace instance’ token.  This token contains the name and ID of the activity together with a couple of state flags.  The second level of state represents a ‘trace fragment’.   As we have seen, a fragment of an activity corresponds directly to the notion of a ‘step’.  It is the unit of work done at a named location, and it must be bounded by start and end, or continue and end, actions. When handling continuations, the BAM Interceptor differentiates between ‘root’ fragments and other fragments.  Very simply, a root fragment represents the start of an activity.  Other fragments represent continuations.  This is where the logic breaks down.  The BAM Interceptor loses state integrity for root fragments when continuations are defined. Initialization Microsoft’s BAM Interceptor code supports the initialization of BAM Interceptors from track point configuration data.  The process starts by populating an Activity Interceptor Configuration object with an array of track points.  These can belong to different steps (aka ‘locations’) and can be registered in any order.  Once it is populated with track points, the Activity Interceptor Configuration is used to initialise the BAM Interceptor.  The BAM Interceptor sets up a hash table of array lists.  Each step is represented by an array list, and each array list contains an ordered set of track points.  The BAM Interceptor represents track points as ‘executable’ components.  When the OnStep method of the BAM Interceptor is called for a given step, the corresponding list of track points is retrieved and each track point is executed in turn.  Each track point retrieves any required data using a call back mechanism and then serializes a BAM trace fragment object representing a specific action (e.g., start, update, enable continuation, stop, etc.).  The serialised trace fragment is then handed off to a BAM event stream (buffered or direct) which takes the appropriate action. The Root of the Problem The logic breaks down in the Activity Interceptor Configuration.  Each Activity Interceptor Configuration is initialised with an instance of a ‘trace instance’ token.  This provides the basic metadata for the activity as a whole.  It contains the activity name and ID together with state flags indicating if the activity ID is a root (i.e., not a continuation fragment) and if it is completed.  This single token is then shared by all trace actions for all steps registered with the Activity Interceptor Configuration. Each trace instance token is automatically initialised to represent a root fragment.  However, if you subsequently register a ‘continuation’ step with the Activity Interceptor Configuration, the ‘root’ flag is set to false at the point the ‘continue’ track point is registered for that step.   If you use a ‘reflector’ tool to inspect the code for the ActivityInterceptorConfiguration class, you can see the flag being set in one of the overloads of the RegisterContinue method.    This makes no sense.  The trace instance token is shared across all the track points registered with the Activity Interceptor Configuration.  The Activity Interceptor Configuration is designed to hold track points for multiple steps.  The ‘root’ flag is clearly meant to be initialised to ‘true’ for the preliminary root fragment and then subsequently set to false at the point that a continuation step is processed.  Instead, if the Activity Interceptor Configuration contains a continuation step, it is changed to ‘false’ before the root fragment is processed.  This is clearly an error in logic. The problem causes havoc when the BAM Interceptor is used with continuation.  Effectively the root step is no longer processed correctly, and the ultimate effect is that the continued activity never completes!   This has nothing to do with the root and the continuation being in the same process.  It is due to a fundamental mistake of setting the ‘root’ flag to false for a continuation before the root fragment is processed. The Workaround Fortunately, it is easy to work around the bug.  The trick is to ensure that you create a new Activity Interceptor Configuration object for each individual step.  This may mean filtering your configuration data to extract the track points for a single step or grouping the configured track points into individual steps and the creating a separate Activity Interceptor Configuration for each group.  In my case, the first approach was required.  Here is what the amended code looks like: // Because of a logic error in Microsoft's code, a separate ActivityInterceptorConfiguration must be used // for each location. The following code extracts only those track points for a given step name (location). var trackPointGroup = from ResolutionService.TrackPoint tp in bamActivity.TrackPoints                       where (string)tp.Location == bamStepName                       select tp; var bamActivityInterceptorConfig =     new Microsoft.BizTalk.Bam.EventObservation.ActivityInterceptorConfiguration(activityName); foreach (var trackPoint in trackPointGroup) {     switch (trackPoint.Type)     {         case TrackPointType.Start:             bamActivityInterceptorConfig.RegisterStartNew(trackPoint.Location, trackPoint.ExtractionInfo);             break; etc… I’m using LINQ to filter a list of track points for those entries that correspond to a given step and then registering only those track points on a new instance of the ActivityInterceptorConfiguration class.   As soon as I re-wrote the code to do this, activities with continuations started to complete correctly.

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• BAM Converter - dedicated page on my website

  - by panjkov

  Exactly 18 months after development of BAM Converter, small currency converter with offline support for WP7 – I finally made a overview page on my website dedicated to BAM Converter. On that page you can read basic information about BAM Converter, see application screenshots and find links to application page on Codeplex and Windows Phone Marketplace. If you have questions, or you need more details about the BAM Converter, you can contact me by adding comment to this post commenting via my accounts...(read more)

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• Granular Clipboard Control in Oracle IRM

  - by martin.abrahams

  One of the main leak prevention controls that customers are looking for is clipboard control. After all, there is little point in controlling access to a document if authorised users can simply make unprotected copies by use of the cut and paste mechanism. Oddly, for such a fundamental requirement, many solutions only offer very simplistic clipboard control - and require the customer to make an awkward choice between usability and security. In many cases, clipboard control is simply an ON-OFF option. By turning the clipboard OFF, you disable one of the most valuable edit functions known to man. Try working for any length of time without copying and pasting, and you'll soon appreciate how valuable that function is. Worse, some solutions disable the clipboard completely - not just for the protected document but for all of the various applications you have open at the time. Normal service is only resumed when you close the protected document. In this way, policy enforcement bleeds out of the particular assets you need to protect and interferes with the entire user experience. On the other hand, turning the clipboard ON satisfies a fundamental usability requirement - but also makes it really easy for users to create unprotected copies of sensitive information, maliciously or otherwise. All they need to do is paste into another document. If creating unprotected copies is this simple, you have to question how much you are really gaining by applying protection at all. You may not be allowed to edit, forward, or print the protected asset, but all you need to do is create a copy and work with that instead. And that activity would not be tracked in any way. So, a simple ON-OFF control creates a real tension between usability and security. If you are only using IRM on a small scale, perhaps security can outweigh usability - the business can put up with the restriction if it only applies to a handful of important documents. But try extending protection to large numbers of documents and large user communities, and the restriction rapidly becomes really unwelcome. I am aware of one solution that takes a different tack. Rather than disable the clipboard, pasting is always permitted, but protection is automatically applied to any document that you paste into. At first glance, this sounds great - protection travels with the content. However, at any scale this model may not be so appealing once you've had to deal with support calls from users who have accidentally applied protection to documents that really don't need it - which would be all too easily done. This may help control leakage, but it also pollutes the system with documents that have policies applied with no obvious rhyme or reason, and it can seriously inconvenience the business by making non-sensitive documents difficult to access. And what policy applies if you paste some protected content into an already protected document? Which policy applies? There are no prizes for guessing that Oracle IRM takes a rather different approach. The Oracle IRM Approach Oracle IRM offers a spectrum of clipboard controls between the extremes of ON and OFF, and it leverages the classification-based rights model to give granular control that satisfies both security and usability needs. Firstly, we take it for granted that if you have EDIT rights, of course you can use the clipboard within a given document. Why would we force you to retype a piece of content that you want to move from HERE... to HERE...? If the pasted content remains in the same document, it is equally well protected whether it be at the beginning, middle, or end - or all three. So, the first point is that Oracle IRM always enables the clipboard if you have the right to edit the file. Secondly, whether we enable or disable the clipboard, we only affect the protected document. That is, you can continue to use the clipboard in the usual way for unprotected documents and applications regardless of whether the clipboard is enabled or disabled for the protected document(s). And if you have multiple protected documents open, each may have the clipboard enabled or disabled independently, according to whether you have Edit rights for each. So, even for the simplest cases - the ON-OFF cases - Oracle IRM adds value by containing the effect to the protected documents rather than to the whole desktop environment. Now to the granular options between ON and OFF. Thanks to our classification model, we can define rights that enable pasting between documents in the same classification - ie. between documents that are protected by the same policy. So, if you are working on this month's financial report and you want to pull some data from last month's report, you can simply cut and paste between the two documents. The two documents are classified the same way, subject to the same policy, so the content is equally safe in both documents. However, if you try to paste the same data into an unprotected document or a document in a different classification, you can be prevented. Thus, the control balances legitimate user requirements to allow pasting with legitimate information security concerns to keep data protected. We can take this further. You may have the right to paste between related classifications of document. So, the CFO might want to copy some financial data into a board document, where the two documents are sealed to different classifications. The CFO's rights may well allow this, as it is a reasonable thing for a CFO to want to do. But policy might prevent the CFO from copying the same data into a classification that is accessible to external parties. The above option, to copy between classifications, may be for specific classifications or open-ended. That is, your rights might enable you to go from A to B but not to C, or you might be allowed to paste to any classification subject to your EDIT rights. As for so many features of Oracle IRM, our classification-based rights model makes this type of granular control really easy to manage - you simply define that pasting is permitted between classifications A and B, but omit C. Or you might define that pasting is permitted between all classifications, but not to unprotected locations. The classification model enables millions of documents to be controlled by a few such rules. Finally, you MIGHT have the option to paste anywhere - such that unprotected copies may be created. This is rare, but a legitimate configuration for some users, some use cases, and some classifications - but not something that you have to permit simply because the alternative is too restrictive. As always, these rights are defined in user roles - so different users are subject to different clipboard controls as required in different classifications. So, where most solutions offer just two clipboard options - ON-OFF or ON-but-encrypt-everything-you-touch - Oracle IRM offers real granularity that leverages our classification model. Indeed, I believe it is the lack of a classification model that makes such granularity impractical for other IRM solutions, because the matrix of rules for controlling pasting would be impossible to manage - there are so many documents to consider, and more are being created all the time.

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• Big Data – Learning Basics of Big Data in 21 Days – Bookmark

  - by Pinal Dave

  Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 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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• BAM Data Control in multiple ADF Faces Components

  - by [email protected]

  As we know Oracle BAM data control instance sharing is not supported.When two or more ADF Faces components must display the same data, and are bound to the same Oracle BAM data control definition, we have to make sure that we wrap each ADF Faces component in an ADF task flow, and set the Data Control Scope to isolated. This blog will show a small sample to demonstrate this. In this sample we will create a Pie and Bar using same BAM DC, such that both components use same Data control but have isolated scope.This sample can be downloaded  fromSample1.zip Set-up: Create a BAM data control using employees DO (sample) Steps: Right click on View Controller project and select "New->ADF Task Flow" Check "Create Bounded Task Flow" and give some meaningful name (ex:EmpPieTF.xml ) to the TaskFlow(TF) and click on "OK"CreateTF.bmpFrom the "Components Palette", drag and drop "View" into the task flow diagram. Give a meaningful name to the view. Double Click and Click "Ok" for  "Create New JSF Page Fragment" From "Data Controls" drag and drop "Employees->Query"  into this jsff page as "Graph->Pie" (Pie: Sales_Number and Slices: Salesperson) Repeat step 1 through 4 for another Task Flow (ex: EmpBarTF). From "Data Controls" drag and drop "Employees->Query"  into this jsff page as "Graph->Bar" (Bars :Sales_Number and X-axis : Salesperson). Open the Taskflow created in step 2. In the Structure Pane, right click on "Task Flow Definition -EmpPieTF" Click "Insert inside Task Flow Definition - EmpPieTF -> ADF Task Flow -> Data Control Scope". Click "OK"TFDCScope.bmpFor the "Data Control Scope", In the Property Inspector ->General section, change data control scope from Shared to Isolated. Repeat step 8 through 11 for the 2nd Task flow created. Now create a new jspx page example: Main.jspxDrag and drop both the Task flows (ex: "EmpPieTF" and "EmpBarTF") as regions. Surround with panel components as needed.Run the page Main.jspxMainPage.bmpNow when the page runs although both components are created using same Data control the bindings are not shared and each component will have a separate instance of the data control.

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• Getting a Database into Source Control

  - by Grant Fritchey

  For any number of reasons, from simple auditing, to change tracking, to automated deployment, to integration with application development processes, you’re going to want to place your database into source control. Using Red Gate SQL Source Control this process is extremely simple. SQL Source Control works within your SQL Server Management Studio (SSMS) interface.  This means you can work with your databases in any way that you’re used to working with them. If you prefer scripts to using the GUI, not a problem. If you prefer using the GUI to having to learn T-SQL, again, that’s fine. After installing SQL Source Control, this is what you’ll see when you open SSMS:   SQL Source Control is now a direct piece of the SSMS environment. The key point initially is that I currently don’t have a database selected. You can even see that in the SQL Source Control window where it shows, in red, “No database selected – select a database in Object Explorer.” If I expand my Databases list in the Object Explorer, you’ll be able to immediately see which databases have been integrated with source control and which have not. There are visible differences between the databases as you can see here:   To add a database to source control, I first have to select it. For this example, I’m going to add the AdventureWorks2012 database to an instance of the SVN source control software (I’m using uberSVN). When I click on the AdventureWorks2012 database, the SQL Source Control screen changes:   I’m going to need to click on the “Link database to source control” text which will open up a window for connecting this database to the source control system of my choice.  You can pick from the default source control systems on the left, or define one of your own. I also have to provide the connection string for the location within the source control system where I’ll be storing my database code. I set these up in advance. You’ll need two. One for the main set of scripts and one for special scripts called Migrations that deal with different kinds of changes between versions of the code. Migrations help you solve problems like having to create or modify data in columns as part of a structural change. I’ll talk more about them another day. Finally, I have to determine if this is an isolated environment that I’m going to be the only one use, a dedicated database. Or, if I’m sharing the database in a shared environment with other developers, a shared database.  The main difference is, under a dedicated database, I will need to regularly get any changes that other developers have made from source control and integrate it into my database. While, under a shared database, all changes for all developers are made at the same time, which means you could commit other peoples work without proper testing. It all depends on the type of environment you work within. But, when it’s all set, it will look like this: SQL Source Control will compare the results between the empty folders in source control and the database, AdventureWorks2012. You’ll get a report showing exactly the list of differences and you can choose which ones will get checked into source control. Each of the database objects is scripted individually. You’ll be able to modify them later in the same way. Here’s the list of differences for my new database:   You can select/deselect all the objects or each object individually. You also get a report showing the differences between what’s in the database and what’s in source control. If there was already a database in source control, you’d only see changes to database objects rather than every single object. You can see that the database objects can be sorted by name, by type, or other choices. I’m going to add a comment such as “Initial creation of database in source control.” And then click on the Commit button which will put all the objects in my database into the source control system. That’s all it takes to get the objects into source control initially. Now is when things can get fun with breaking changes to code, automated deployments, unit testing and all the rest.

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• Passing Date Parameter to BAM

  - by mona.rakibe(at)oracle.com

  In the past I wrote a blog on passing ADF parameter to a BAM page. This sample can be further extended for parameter of any data type. Here is the  similar sample for Date type, the steps  to create application remain same.Sample : PassDate.zipSteps to RunCreate this Data Object on BAM server    ID(integer)    Date(datetime)     1             01/12/2010 4:22:34 AM     2             10/12/2009 5:22:20 PM     3             10/10/1999 5:22:10 PM     4             11/11/1980 4:23:10 PM Open adfc-config.xml and run InputValueProvide some date value and click on "Filter Records"Verify that the data is filtered in next page.   

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• Data Quality Through Data Governance

  Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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• SQLAuthority News – Links to Book On Line – Data Mining Algorithms (Analysis Services – Data Mining)

  - by pinaldave

  I have quite often received request for the Data Mining Algorithms details. Book Online has wonderful resources for the same. I suggest to read them here. Data Mining Algorithms (Analysis Services – Data Mining) The data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set [...]

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• SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

  - by pinaldave

  There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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• Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

  - by Pinal Dave

  This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  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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• Big Data – Basics of Big Data Architecture – Day 4 of 21

  - by Pinal Dave

  In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. 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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• Big Data – Evolution of Big Data – Day 3 of 21

  - by Pinal Dave

  In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. 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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• Working with Temporal Data in SQL Server

  - by Dejan Sarka

  My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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• New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

  - by charlie.berger

  Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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• Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. 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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• Oracle BPM enable BAM by Peter Paul

  - by JuergenKress

  BPMN processes created in the BPM Suite can be monitored by standardized dashboard in the BPM workspace. Besides that there a default views to export Oracle BPM metrics to a data warehouse. And there is another option: BAM – Business Activity Monitoring. BAM takes the monitoring of BPMN processes one step further. BAM allows you to create more advanced dashboards and even real-time alerts. BAM enables you to make decisions based on real-time information gathered from your running processes. With BPMN processes you can use the standard Business Indicators that the BPM Suite offers you and use them to with BAM without much extra effort. However you have to enable BAM in BPM processes. Read the full article here. SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: BPM,BAM,BPM and BAM,Peter Paul,proces monitoring,SOA Community,Oracle SOA,Oracle BPM,Community,OPN,Jürgen Kress

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• BizTalk - Removing BAM Activities and Views using bm.exe

  - by Stuart Brierley

  Originally posted on: http://geekswithblogs.net/StuartBrierley/archive/2013/10/16/biztalk---removing-bam-activities-and-views-using-bm.exe.aspxOn the project I am currently working on, we are making quite extensive use of BAM within our growing number of BizTalk applications, all of which are being deployed and undeployed using the excellent Deployment Framework for BizTalk 5.0.Recently I had an issue where problems on the build server had left the target development servers in a state where the BAM activities and views for a particular application were not being removed by the undeploy process and unfortunately the definition in the solution had changed meaning that I could not easily recreate the file from source control.  To get around this I used the bm.exe application from the command line to manually remove the problem BAM artifacts - bm.exe can be found at the following path:C:\Program Files (x86)\Microsoft BizTalk Server 2010\TrackingC:\Program Files (x86)\Microsoft BizTalk Server 2010\TrackingStep1 :Get the BAM Definition FileRun the following command to get the BAm definition file, containing the details of all the activities, views and alerts:bm.exe get-defxml -FileName:{Path and File Name Here}.xmlStep 2: Remove the BAM ArtifactsAt this stage I chose to manually remove each of my problem BAM activities and views using seperate command line calls.  By looking in the definition file I could see the names of the activities and views that I wanted to remove and then use the following commands to remove first the views and then the activities:bm.exe remove-view -name:{viewname}bm.exe remove-activity -name:{activityname}

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• Updated Business Activity Monitoring (BAM) Class

  - by Gary Barg

  We have just completed an extensive upgrade to the Business Activity Monitoring course, bringing it up to PS5 level and doing some major rework of content and topic flow. This should be a GREAT course for anyone needing to learn to use BAM effectively to analyze their SOA data. Details of the Course This course explains how to use Oracle BAM to monitor enterprise business activities across an enterprise in real time. You can measure your key performance indicators (KPIs), determine whether you are meeting service-level agreements (SLAs), and take corrective action in real time. Learn To: Create dashboards and alerts using a business-friendly, wizard-based design environment Monitor BPM and BPEL processes Configure drilling, driving, and time-based filtering Create alerts Build applications with a dynamic user interface Manage BAM users and roles In addition to learning Oracle BAM architecture, you learn how to perform administrative tasks related to Oracle BAM. You create and work with the different types of message sources that send data into Oracle BAM. You build interactive, real-time, actionable dashboards, and you configure alerts on abnormal conditions. You learn how to monitor both BPEL and BPM composite applications with Oracle BAM. Lastly, you create and use Oracle BAM data control to build applications with a dynamic user interface that changes based on real-time business events. Registration The Oracle University course page with more course details and registration information, is here. The next scheduled class: Date: 5-Dec-2012 Duration: 3 days Hours: 9:00 AM – 5:00 PM CT Location: Chicago, IL Class ID: 3325708

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• Unstructured Data - The future of Data Administration

  Some have claimed that there is a problem with the way data is currently managed using the relational paradigm do to the rise of unstructured data in modern business. PCMag.com defines unstructured data as data that does not reside in a fixed location. They further explain that unstructured data refers to data in a free text form that is not bound to any specific structure. With the rise of unstructured data in the form of emails, spread sheets, images and documents the critics have a right to argue that the relational paradigm is not as effective as the object oriented data paradigm in managing this type of data. The relational paradigm relies heavily on structure and relationships in and between items of data. This type of paradigm works best in a relation database management system like Microsoft SQL, MySQL, and Oracle because data is forced to conform to a structure in the form of tables and relations can be derived from the existence of one or more tables. These critics also claim that database administrators have not kept up with reality because their primary focus in regards to data administration deals with structured data and the relational paradigm. The relational paradigm was developed in the 1970’s as a way to improve data management when compared to standard flat files. Little has changed since then, and modern database administrators need to know more than just how to handle structured data. That is why critics claim that today’s data professionals do not have the proper skills in order to store and maintain data for modern systems when compared to the skills of system designers, programmers , software engineers, and data designers  due to the industry trend of object oriented design and development. I think that they are wrong. I do not disagree that the industry is moving toward an object oriented approach to development with the potential to use more of an object oriented approach to data.   However, I think that it is business itself that is limiting database administrators from changing how data is stored because of the potential costs, and impact that might occur by altering any part of stored data. Furthermore, database administrators like all technology workers constantly are trying to improve their technical skills in order to excel in their job, so I think that accusing data professional is not just when the root cause of the lack of innovation is controlled by business, and it is business that will suffer for their inability to keep up with technology. One way for database professionals to better prepare for the future of database management is start working with data in the form of objects and so that they can extract data from the objects so that the stored information within objects can be used in relation to the data stored in a using the relational paradigm. Furthermore, I think the use of pattern matching will increase with the increased use of unstructured data because object can be selected, filtered and altered based on the existence of a pattern found within an object.

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• Databases in Source Control

  - by Grant Fritchey

  I’ve been working as a database professional for quite a long time. But originally, I was a developer. And I loved being a developer. There was this constant feedback loop of a job well done, your code compiled and it ran. Every time this happened successfully, you’d check it into source control. These days you have to add another step; the code passed all the tests, unit, line, regression, qa, whatever, then into source control it goes. As a matter of fact, when I first made the jump from developer to DBA/database developer/database professional, source control was the one thing I couldn’t believe was missing from the DBA toolbox. Come to find out, source control was only the beginning of what was missing from your standard DBAs set of skills. Don’t get me wrong. I’m not disrespecting the DBA. They’re focused where they should be, on your production data. But there has to be a method for developing applications that include databases and the database side of that development and deployment process has long been lacking. This lack of development and deployment methodologies is a part of what has given rise to some of the wackier implementations of Object Relational Mapping tools, the NoSQL movement, and some of the other foul cursing that is directed towards databases, DBAs, and database development by application developers. Some of that is well earned. A lot isn’t. But it is a fact that database professionals, in general, do not have as sophisticated a model for managing development and deployment as application developers do. We could charge out and start trying to come up with our own standards and methods. I’m sure people have done exactly that. However, I’m lazy, and not terribly bright. Rather than try to invent a whole new process, I’m going to look to my developer roots and choose instead to emulate the developers. They’re sitting over there across the hall from me working with SCRUM/Agile/Waterfall/Object Driven/Feature Driven/Test Driven development processes that they’ve been polishing for years. What if I just started working on database development the same way they work on code development? Win! Ah, but now I have to have a mechanism for treating my database like application code. First, I need a method for getting it into source control. That’s where Red Gate’s SQL Source Control comes into the picture. SQL Source Control works within SQL Server Management Studio to connect your database objects up to the source control system of your choice. Right out of the box SQL Source Control can link to TFS, SVN or Vault. With a little work you can connect it to Git or just about any other source control system. With the ability to get my database into source control, a lot of possibilities for more direct integration with the application development teams open up.

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• Developing an analytics's system processing large amounts of data - where to start

  - by Ryan

  Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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