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

  - by pinaldave

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

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• Consolidate Data in Private Clouds, But Consider Security and Regulatory Issues

  - by Troy Kitch

  The January 13 webcast Security and Compliance for Private Cloud Consolidation will provide attendees with an overview of private cloud computing based on Oracle's Maximum Availability Architecture and how security and regulatory compliance affects implementations. Many organizations are taking advantage of Oracle's Maximum Availability Architecture to drive down the cost of IT by deploying private cloud computing environments that can support downtime and utilization spikes without idle redundancy. With two-thirds of sensitive and regulated data in organizations' databases private cloud database consolidation means organizations must be more concerned than ever about protecting their information and addressing new regulatory challenges. Join us for this webcast to learn about greater risks and increased threats to private cloud data and how Oracle Database Security Solutions can assist in securely consolidating data and meet compliance requirements. Register Now.

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

  - by bipinjoshi

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

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• NLog Exception Details Renderer

  - by jtimperley

  Originally posted on: http://geekswithblogs.net/jtimperley/archive/2013/07/28/nlog-exception-details-renderer.aspxI recently switch from Microsoft's Enterprise Library Logging block to NLog.  In my opinion, NLog offers a simpler and much cleaner configuration section with better use of placeholders, complemented by custom variables. Despite this, I found one deficiency in my migration; I had lost the ability to simply render all details of an exception into our logs and notification emails. This is easily remedied by implementing a custom layout renderer. Start by extending 'NLog.LayoutRenderers.LayoutRenderer' and overriding the 'Append' method. using System.Text; using NLog; using NLog.Config; using NLog.LayoutRenderers;   [ThreadAgnostic] [LayoutRenderer(Name)] public class ExceptionDetailsRenderer : LayoutRenderer { public const string Name = "exceptiondetails";   protected override void Append(StringBuilder builder, LogEventInfo logEvent) { // Todo: Append details to StringBuilder } }   Now that we have a base layout renderer, we simply need to add the formatting logic to add exception details as well as inner exception details. This is done using reflection with some simple filtering for the properties that are already being rendered. I have added an additional 'Register' method, allowing the definition to be registered in code, rather than in configuration files. This complements by 'LogWrapper' class which standardizes writing log entries throughout my applications. using System; using System.Collections.Generic; using System.Linq; using System.Reflection; using System.Text; using NLog; using NLog.Config; using NLog.LayoutRenderers;   [ThreadAgnostic] [LayoutRenderer(Name)] public sealed class ExceptionDetailsRenderer : LayoutRenderer { public const string Name = "exceptiondetails"; private const string _Spacer = "======================================"; private List<string> _FilteredProperties;   private List<string> FilteredProperties { get { if (_FilteredProperties == null) { _FilteredProperties = new List<string> { "StackTrace", "HResult", "InnerException", "Data" }; }   return _FilteredProperties; } }   public bool LogNulls { get; set; }   protected override void Append(StringBuilder builder, LogEventInfo logEvent) { Append(builder, logEvent.Exception, false); }   private void Append(StringBuilder builder, Exception exception, bool isInnerException) { if (exception == null) { return; }   builder.AppendLine();   var type = exception.GetType(); if (isInnerException) { builder.Append("Inner "); }   builder.AppendLine("Exception Details:") .AppendLine(_Spacer) .Append("Exception Type: ") .AppendLine(type.ToString());   var bindingFlags = BindingFlags.Instance | BindingFlags.Public; var properties = type.GetProperties(bindingFlags); foreach (var property in properties) { var propertyName = property.Name; var isFiltered = FilteredProperties.Any(filter => String.Equals(propertyName, filter, StringComparison.InvariantCultureIgnoreCase)); if (isFiltered) { continue; }   var propertyValue = property.GetValue(exception, bindingFlags, null, null, null); if (propertyValue == null && !LogNulls) { continue; }   var valueText = propertyValue != null ? propertyValue.ToString() : "NULL"; builder.Append(propertyName) .Append(": ") .AppendLine(valueText); }   AppendStackTrace(builder, exception.StackTrace, isInnerException); Append(builder, exception.InnerException, true); }   private void AppendStackTrace(StringBuilder builder, string stackTrace, bool isInnerException) { if (String.IsNullOrEmpty(stackTrace)) { return; }   builder.AppendLine();   if (isInnerException) { builder.Append("Inner "); }   builder.AppendLine("Exception StackTrace:") .AppendLine(_Spacer) .AppendLine(stackTrace); }   public static void Register() { Type definitionType; var layoutRenderers = ConfigurationItemFactory.Default.LayoutRenderers; if (layoutRenderers.TryGetDefinition(Name, out definitionType)) { return; }   layoutRenderers.RegisterDefinition(Name, typeof(ExceptionDetailsRenderer)); LogManager.ReconfigExistingLoggers(); } } For brevity I have removed the Trace, Debug, Warn, and Fatal methods. They are modelled after the Info methods. As mentioned above, note how the log wrapper automatically registers our custom layout renderer reducing the amount of application configuration required. using System; using NLog;   public static class LogWrapper { static LogWrapper() { ExceptionDetailsRenderer.Register(); }   #region Log Methods   public static void Info(object toLog) { Log(toLog, LogLevel.Info); }   public static void Info(string messageFormat, params object[] parameters) { Log(messageFormat, parameters, LogLevel.Info); }   public static void Error(object toLog) { Log(toLog, LogLevel.Error); }   public static void Error(string message, Exception exception) { Log(message, exception, LogLevel.Error); }   private static void Log(string messageFormat, object[] parameters, LogLevel logLevel) { string message = parameters.Length == 0 ? messageFormat : string.Format(messageFormat, parameters); Log(message, (Exception)null, logLevel); }   private static void Log(object toLog, LogLevel logLevel, LogType logType = LogType.General) { if (toLog == null) { throw new ArgumentNullException("toLog"); }   if (toLog is Exception) { var exception = toLog as Exception; Log(exception.Message, exception, logLevel, logType); } else { var message = toLog.ToString(); Log(message, null, logLevel, logType); } }   private static void Log(string message, Exception exception, LogLevel logLevel, LogType logType = LogType.General) { if (exception == null && String.IsNullOrEmpty(message)) { return; }   var logger = GetLogger(logType); // Note: Using the default constructor doesn't set the current date/time var logInfo = new LogEventInfo(logLevel, logger.Name, message); logInfo.Exception = exception; logger.Log(logInfo); }   private static Logger GetLogger(LogType logType) { var loggerName = logType.ToString(); return LogManager.GetLogger(loggerName); }   #endregion   #region LogType private enum LogType { General } #endregion } The following configuration is similar to what is provided for each of my applications. The 'application' variable is all that differentiates the various applications in all of my environments, the rest has been standardized. Depending on your needs to tweak this configuration while developing and debugging, this section could easily be pushed back into code similar to the registering of our custom layout renderer.   <?xml version="1.0"?>   <configuration> <configSections> <section name="nlog" type="NLog.Config.ConfigSectionHandler, NLog"/> </configSections> <nlog xmlns="http://www.nlog-project.org/schemas/NLog.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <variable name="application" value="Example"/> <targets> <target type="EventLog" name="EventLog" source="${application}" log="${application}" layout="${message}${onexception: ${newline}${exceptiondetails}}"/> <target type="Mail" name="Email" smtpServer="smtp.example.local" from="[email protected]" to="[email protected]" subject="(${machinename}) ${application}: ${level}" body="Machine: ${machinename}${newline}Timestamp: ${longdate}${newline}Level: ${level}${newline}Message: ${message}${onexception: ${newline}${exceptiondetails}}"/> </targets> <rules> <logger name="*" minlevel="Debug" writeTo="EventLog" /> <logger name="*" minlevel="Error" writeTo="Email" /> </rules> </nlog> </configuration>   Now go forward, create your custom exceptions without concern for including their custom properties in your exception logs and notifications.

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• More Value From Data Using Data Mining Presentation

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

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• Move data from others user accounts in my user account

  - by user118136

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

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• How to implement SEH (Structured Ecxeption Handling) in VB6?

  - by Fernando

  Could someone provide some example on implementing SEH in VB6? Everything I've seen so far is in C++

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• How to implement exception chaining in PHP

  - by Josef Sábl

  Constructor for PHP's exception has third parameter, documentation says: $previous: The previous exception used for the exception chaining. But I can't make it work. My code looks like this: try { throw new Exception('Exception 1', 1001); } catch (Exception $ex) { throw new Exception('Exception 2', 1002, $ex); } I expect Exception 2 to be thrown and I expect that it will have Exception 1 attached. But all I get is: Fatal error: Wrong parameters for Exception([string $exception [, long $code ]]) in ... What am I doing wrong?

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

  - by pinaldave

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

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• DAL Exception handling in a MVP application

  - by Chathuranga

  In a MVP win forms application I'm handling exceptions as follows in DAL. Since the user messaging is not a responsibility of DAL, I want to move it in to my Presentation class. Could you show me a standard way to do that? public bool InsertAccount(IBankAccount ba) { string selectStatement = @"IF NOT EXISTS (SELECT ac_no FROM BankAccount WHERE ac_no=@ac_no) BEGIN INSERT INTO BankAccount ..."; using (SqlConnection sqlConnection = new SqlConnection(db.ConnectionString)) { using (SqlCommand sqlCommand = new SqlCommand(selectStatement, sqlConnection)) { try { sqlConnection.Open(); sqlCommand.Parameters.Add("@ac_no", SqlDbType.Char).Value = ba.AccountNumber; // // sqlCommand.ExecuteNonQuery(); return true; } catch (Exception e) { MessageBox.Show(("Error: " + e.Message)); } if (sqlConnection.State == System.Data.ConnectionState.Open) sqlConnection.Close(); return false; } } }

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• Data Mining Resources

  - by Dejan Sarka

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

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• Master Data Services Employees Sample Model

  - by Davide Mauri

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

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• Best way to throw exception and avoid code duplication

  - by JF Dion

  I am currently writing code and want to make sure all the params that get passed to a function/method are valid. Since I am writing in PHP I don't have access to all the facilities of other languages like C, C++ or Java to check for parameters values and types public function inscriptionExists($sectionId, $userId) // PHP vs. public boolean inscriptionExists(int sectionId, int userId) // Java So I have to rely on exceptions if I want to make sure that my params are both integers. Since I have a lot of places where I need to check for param validity, what would be the best way to create a validation/exception machine and avoid code duplication? I was thinking on a static factory (since I don't want to pass it to all of my classes) with a signature like: public static function factory ($value, $valueType, $exceptionType = 'InvalidArgumentException'); Which would then call the right sub process to validate based on the type. Am I on the right way, or am I going completely off the road and overthinking my problem?

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

  - by dain.hansen

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

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

  - by Tanu Sood

  Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Author: Dain Hansen, Director, Oracle It was an exciting OpenWorld 2013 for us in the Data Integration track. Our theme this year was all about ‘being future ready’ - previewing one of our biggest releases this year: Oracle Data Integration 12c. Just this week we followed up with this preview by announcing the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Mark Hurd's keynote on day one set the tone for the Data Integration sessions. Mark focused on big data analytics and the changing consumer expectations. Especially real-time insight is a key theme for Oracle overall and data integration products. In Mark Hurd's keynote we heard from key customers, such as Airbus and Thomson Reuters, how real-time analysis of operational data including machine data creates value, in some cases even saves lives. Thomas Kurian gave a deeper look into Oracle's big data and fast data solutions. In the initial lead Data Integration track session - Brad Adelberg, VP of Development, presented Oracle’s Data Integration 12c product strategy based on key trends from the initial OpenWorld keynotes. Brad talked about how Oracle's data integration products address the new data integration requirements that evolved with cloud computing, big data, and changing consumer expectations and how they set the key themes in our products’ road map. Brad explained why and how fast-time to value, high-performance and future-ready solutions is the top focus areas for product development. If you were not able to attend OpenWorld or this session I recommend reading the white paper: Five New Data Integration Requirements and How to Meet them with Oracle Data Integration, which provides an in-depth look into how Oracle addresses the new trends in the DI market. Following Brad’s session, Nick Wagner provided in depth review of Oracle GoldenGate’s latest features and roadmap. Nick discussed how Oracle GoldenGate’s tight integration with Oracle Database sets the product apart from the competition. We also heard that heterogeneity of the product is still a major focus for GoldenGate’s development and there will be more news on that front when there is a major release. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} After GoldenGate’s product strategy session, Denis Gray from the PM team presented Oracle Data Integrator’s product strategy session, talking about the latest and greatest on ODI. Another good session was delivered by long-time GoldenGate users, Comcast.  Jason Hurd and Amit Patel of Comcast talked about the various use cases they deploy Oracle GoldenGate throughout their enterprise, from database upgrades, feeding reporting systems, to active-active database synchronization.  The Comcast team shared many good tips on how to use GoldenGate for both zero downtime upgrades and active-active replication with conflict management requirement. One of our other important goals we had this year for the Data Integration track at OpenWorld was hearing from our customers. We ended day 1 on just that, with a wonderful award ceremony for Oracle Excellence Awards for Oracle Fusion Middleware Innovation. The ceremony was held in the Yerba Buena Center for the Arts. Congratulations to Royal Bank of Scotland and Yalumba Wine Company, the winners in the Data Integration category. You can find more information on the award and the winners in our previous blog post: 2013 Oracle Excellence Awards for Fusion Middleware Innovation… Selected for their innovation use of Oracle’s Data Integration products; the winners for the Data Integration Category are Royal Bank of Scotland and The Yalumba Wine Company. Congratulations!!! Royal Bank of Scotland’s Market and International Banking division provides clients across the globe with seamless trading and competitive pricing, underpinned by a deep knowledge of risk management across the full spectrum of financial products. They handle millions of transactions daily to keep the lifeblood of their clients’ businesses flowing – whether through payment management solutions or through bespoke trade finance solutions. Royal Bank of Scotland is leveraging Oracle GoldenGate and Oracle Data Integrator along with Oracle Business Intelligence Enterprise Edition and the Oracle Database for a variety of solutions. Mainly, Oracle GoldenGate and Oracle Data Integrator are used to feed their data warehouse – providing a real-time data integration solution that feeds transactional data to their analytics system in minutes to enable improved decision making with timely, accurate data for their business users. Oracle Data Integrator’s in-database transformation capabilities and its ability to integrate with Oracle GoldenGate for real-time data capture is the foundation of this implementation. This solution makes it such that changes happening in the analytics systems are available the same day they are deployed on the operational system with 100% data quality guaranteed. Additionally, the solution has helped to reduce their operational database size from 150GB to 10GB. Impressive! Now what if I told you this solution was built in 3 months and had a less than 6 month return on investment? That’s outstanding! The Yalumba Wine Company is situated in the Barossa Valley of Australia. It is the oldest family owned winery in Australia with a unique way of aging their wines in specially crafted 100 liter barrels. Did you know that “Yalumba” is Aboriginal for “all the land around”? The Yalumba Wine Company is growing rapidly, and was in need of introducing a more modern standard to the existing manufacturing processes to meet globalization demands, overall time-to-market, and better operational efficiency objectives of product development. The Yalumba Wine Company worked with a partner, Bristlecone to develop a unique solution whereby Oracle Data Integrator is leveraged to pull data from Salesforce.com and JD Edwards, in addition to their other pre-existing source systems, for consumption into their data warehouse. They have emphasized the overall ease of developing integration workflows with Oracle Data Integrator. The solution has brought better visibility for the business users, shorter data loading and transformation performance to their data warehouse with rapid incorporation of new data sources, and a solid future-proof foundation for their organization. Moving forward, they plan on leveraging more from Oracle’s Data Integration portfolio. Terrific! In addition to these two customers on Tuesday we featured many other important Oracle Data Integrator and Oracle GoldenGate customers. On Tuesday the GoldenGate panel included: Land O’Lakes, Smuckers, and Veolia Water. Besides giving us yummy nutrition and healthy water, these companies have another aspect in common. They all use GoldenGate to boost their ERP application. Please read the recap by Irem Radzik. On Wednesday, the ODI Panel included: Barry Ralston and Ryan Weber of Infinity Insurance, Paul Stracke of Paychex Inc., and Ian Wall of Vertex Pharmaceuticals for a session filled with interesting projects, use cases and approaches to leveraging Oracle Data Integrator. Please read the recap by Sandrine Riley for more. Thanks to everyone who joined with us and we hope to stay connected! To hear more about our Data Integration12c products join us in an upcoming webcast to learn more. Follow us www.twitter.com/ORCLGoldenGate or goto our website at www.oracle.com/goto/dataintegration

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• Best exception handling practices or recommendations?

  - by user828584

  I think the two main problems with my programs are my code structure/organization and my error handling. I'm reading Code Complete 2, but I need something to read for working with potential problems. For example, on a website, if something can only happen if the user tampers with data via javascript, do you write for that? Also, when do you not catch errors? When you write a class that expects a string and an int as input, and they aren't a string and int, do you check for that, or do you let it bubble up to the calling method that passed incorrect parameters? I know this is a broad topic that can't be answered in a single answer here, so what I'm looking for is a book or resource that's commonly accepted as teaching proper exception handling practice.

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• Fast Data - Big Data's achilles heel

  - by thegreeneman

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

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

  - by jgelhaus

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

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

  - by ced

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

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• How to simulate inner exception in C++

  - by Siva Chandran

  Basically I want to simulate .NET Exception.InnerException in C++. I want to catch exception from bottom layer and wrap it with another exception and throw again to upper layer. The problem here is I don't know how to wrap the catched exception inside another exception. struct base_exception : public std::exception { std::exception& InnerException; base_exception() : InnerException(???) { } // <---- what to initialize with base_exception(std::exception& innerException) : InnerException(innerException) { } }; struct func1_exception : public base_exception { const char* what() const throw() { return "func1 exception"; } }; struct func2_exception : public base_exception { const char* what() const throw() { return "func2 exception"; } }; void func2() { throw func2_exception(); } void func1() { try { func2(); } catch(std::exception& e) { throw func2_exception(e); // <--- is this correct? will the temporary object will be alive? } } int main(void) { try { func1(); } catch(base_exception& e) { std::cout << "Got exception" << std::endl; std::cout << e.what(); std::cout << "InnerException" << std::endl; std::cout << e.InnerException.what(); // <---- how to make sure it has inner exception ? } } In the above code listing I am not sure how to initialize the "InnerException" member when there is no inner exception. Also I am not sure whether the temporary object that is thrown from func1 will survive even after func2 throw?

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• SQL SERVER – Introduction to Big Data – Guest Post

  - by pinaldave

  BIG Data – such a big word – everybody talks about this now a days. It is the word in the database world. In one of the conversation I asked my friend Jasjeet Sigh the same question – what is Big Data? He instantly came up with a very effective write-up.  Jasjeet is working as a Technical Manager with Koenig Solutions. He leads the SQL domain, and holds rich IT industry experience. Talking about Koenig, it is a 19 year old IT training company that offers several certification choices. Some of its courses include SharePoint Training, Project Management certifications, Microsoft Trainings, Business Intelligence programs, Web Design and Development courses etc. Big Data, as the name suggests, is about data that is BIG in nature. The data is BIG in terms of size, and it is difficult to manage such enormous data with relational database management systems that are quite popular these days. Big Data is not just about being large in size, it is also about the variety of the data that differs in form or type. Some examples of Big Data are given below : Scientific data related to weather and atmosphere, Genetics etc Data collected by various medical procedures, such as Radiology, CT scan, MRI etc Data related to Global Positioning System Pictures and Videos Radio Frequency Data Data that may vary very rapidly like stock exchange information Apart from difficulties in managing and storing such data, it is difficult to query, analyze and visualize it. The characteristics of Big Data can be defined by four Vs: Volume: It simply means a large volume of data that may span Petabyte, Exabyte and so on. However it also depends organization to organization that what volume of data they consider as Big Data. Variety: As discussed above, Big Data is not limited to relational information or structured Data. It can also include unstructured data like pictures, videos, text, audio etc. Velocity:  Velocity means the speed by which data changes. The higher is the velocity, the more efficient should be the system to capture and analyze the data. Missing any important point may lead to wrong analysis or may even result in loss. Veracity: It has been recently added as the fourth V, and generally means truthfulness or adherence to the truth. In terms of Big Data, it is more of a challenge than a characteristic. It is difficult to ascertain the truth out of the enormous amount of data and the one that has high velocity. There are always chances of having un-precise and uncertain data. It is a challenging task to clean such data before it is analyzed. Big Data can be considered as the next big thing in the IT sector in terms of innovation and development. If appropriate technologies are developed to analyze and use the information, it can be the driving force for almost all industrial segments. These include Retail, Manufacturing, Service, Finance, Healthcare etc. This will help them to automate business decisions, increase productivity, and innovate and develop new products. Thanks Jasjeet Singh for an excellent write up.  Jasjeet Sign is working as a Technical Manager with Koenig Solutions. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Database, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Big Data

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• Subterranean IL: Compiling C# exception handlers

  - by Simon Cooper

  An exception handler in C# combines the IL catch and finally exception handling clauses into a single try statement: try { Console.WriteLine("Try block") // ... } catch (IOException) { Console.WriteLine("IOException catch") // ... } catch (Exception e) { Console.WriteLine("Exception catch") // ... } finally { Console.WriteLine("Finally block") // ... } How does this get compiled into IL? Initial implementation If you remember from my earlier post, finally clauses must be specified with their own .try clause. So, for the initial implementation, we take the try/catch/finally, and simply split it up into two .try clauses (I have to use label syntax for this): StartTry: ldstr "Try block" call void [mscorlib]System.Console::WriteLine(string) // ... leave.s End EndTry: StartIOECatch: ldstr "IOException catch" call void [mscorlib]System.Console::WriteLine(string) // ... leave.s End EndIOECatch: StartECatch: ldstr "Exception catch" call void [mscorlib]System.Console::WriteLine(string) // ... leave.s End EndECatch: StartFinally: ldstr "Finally block" call void [mscorlib]System.Console::WriteLine(string) // ... endfinally EndFinally: End: // ... .try StartTry to EndTry catch [mscorlib]System.IO.IOException handler StartIOECatch to EndIOECatch catch [mscorlib]System.Exception handler StartECatch to EndECatch .try StartTry to EndTry finally handler StartFinally to EndFinally However, the resulting program isn't verifiable, and doesn't run: [IL]: Error: Shared try has finally or fault handler. Nested try blocks What's with the verification error? Well, it's a condition of IL verification that all exception handling regions (try, catch, filter, finally, fault) of a single .try clause have to be completely contained within any outer exception region, and they can't overlap with any other exception handling clause. In other words, IL exception handling clauses must to be representable in the scoped syntax, and in this example, we're overlapping catch and finally clauses. Not only is this example not verifiable, it isn't semantically correct. The finally handler is specified round the .try. What happens if you were able to run this code, and an exception was thrown? Program execution enters top of try block, and exception is thrown within it CLR searches for an exception handler, finds catch Because control flow is leaving .try, finally block is run The catch block is run leave.s End inside the catch handler branches to End label. We're actually running the finally before the catch! What we do about it What we actually need to do is put the catch clauses inside the finally clause, as this will ensure the finally gets executed at the correct time (this time using scoped syntax): .try { .try { ldstr "Try block" call void [mscorlib]System.Console::WriteLine(string) // ... leave.s End } catch [mscorlib]System.IO.IOException { ldstr "IOException catch" call void [mscorlib]System.Console::WriteLine(string) // ... leave.s End } catch [mscorlib]System.Exception { ldstr "Exception catch" call void [mscorlib]System.Console::WriteLine(string) // ... leave.s End } } finally { ldstr "Finally block" call void [mscorlib]System.Console::WriteLine(string) // ... endfinally } End: ret Returning from methods There is a further semantic mismatch that the C# compiler has to deal with; in C#, you are allowed to return from within an exception handling block: public int HandleMethod() { try { // ... return 0; } catch (Exception) { // ... return -1; } } However, you can't ret inside an exception handling block in IL. So the C# compiler does a leave.s to a ret outside the exception handling area, loading/storing any return value to a local variable along the way (as leave.s clears the stack): .method public instance int32 HandleMethod() { .locals init ( int32 retVal ) .try { // ... ldc.i4.0 stloc.0 leave.s End } catch [mscorlib]System.Exception { // ... ldc.i4.m1 stloc.0 leave.s End } End: ldloc.0 ret } Conclusion As you can see, the C# compiler has quite a few hoops to jump through to translate C# code into semantically-correct IL, and hides the numerous conditions on IL exception handling blocks from the C# programmer. Next up: catch-all blocks, and how the runtime deals with non-Exception exceptions.

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• Creating a Corporate Data Hub

  - by BuckWoody

  The Windows Azure Marketplace has a rich assortment of data and software offerings for you to use – a type of Software as a Service (SaaS) for IT workers, not necessarily for end-users. Among those offerings is the “Data Hub” – a  codename for a project that ironically actually does what the codename says. In many of our organizations, we have multiple data quality issues. Finding data is one problem, but finding it just once is often a bigger problem. Lots of departments and even individuals have stored the same data more than once, and in some cases, made changes to one of the copies. It’s difficult to know which location or version of the data is authoritative. Then there’s the problem of accessing the data. It’s fairly straightforward to publish a database, share or other location internally to store the data. But then you have to figure out who owns it, how it is controlled, and pass out the various connection strings to those who want to use it. And then you need to figure out how to let folks access the internal data externally – bringing up all kinds of security issues. Finally, in many cases our user community wants us to combine data from the internally sources with external data, bringing up the security, strings, and exploration features up all over again. Enter the Data Hub. This is an online offering, where you assign an administrator and data stewards. You import the data into the service, and it’s available to you - and only you and your organization if you wish. The basic steps for this service are to set up the portal for your company, assign administrators and permissions, and then you assign data areas and import data into them. From there you make them discoverable, and then you have multiple options that you or your users can access that data. You’re then able, if you wish, to combine that data with other data in one location. So how does all that work? What about security? Is it really that easy? And can you really move the data definition off to the Subject Matter Experts (SME’s) that know the particular data stack better than the IT team does? Well, nothing good is easy – but using the Data Hub is actually pretty simple. I’ll give you a link in a moment where you can sign up and try this yourself. Once you sign up, you assign an administrator. From there you’ll create data areas, and then use a simple interface to bring the data in. All of this is done in a portal interface – nothing to install, configure, update or manage. After the data is entered in, and you’ve assigned meta-data to describe it, your users have multiple options to access it. They can simply use the portal – which actually has powerful visualizations you can use on any platform, even mobile phones or tablets.     Your users can also hit the data with Excel – which gives them ultimate flexibility for display, all while using an authoritative, single reference for the data. Since the service is online, they can do this wherever they are – given the proper authentication and permissions. You can also hit the service with simple API calls, like this one from C#: http://msdn.microsoft.com/en-us/library/hh921924  You can make HTTP calls instead of code, and the data can even be exposed as an OData Feed. As you can see, there are a lot of options. You can check out the offering here: http://www.microsoft.com/en-us/sqlazurelabs/labs/data-hub.aspx and you can read the documentation here: http://msdn.microsoft.com/en-us/library/hh921938

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• Creating a Corporate Data Hub

  - by BuckWoody

  The Windows Azure Marketplace has a rich assortment of data and software offerings for you to use – a type of Software as a Service (SaaS) for IT workers, not necessarily for end-users. Among those offerings is the “Data Hub” – a  codename for a project that ironically actually does what the codename says. In many of our organizations, we have multiple data quality issues. Finding data is one problem, but finding it just once is often a bigger problem. Lots of departments and even individuals have stored the same data more than once, and in some cases, made changes to one of the copies. It’s difficult to know which location or version of the data is authoritative. Then there’s the problem of accessing the data. It’s fairly straightforward to publish a database, share or other location internally to store the data. But then you have to figure out who owns it, how it is controlled, and pass out the various connection strings to those who want to use it. And then you need to figure out how to let folks access the internal data externally – bringing up all kinds of security issues. Finally, in many cases our user community wants us to combine data from the internally sources with external data, bringing up the security, strings, and exploration features up all over again. Enter the Data Hub. This is an online offering, where you assign an administrator and data stewards. You import the data into the service, and it’s available to you - and only you and your organization if you wish. The basic steps for this service are to set up the portal for your company, assign administrators and permissions, and then you assign data areas and import data into them. From there you make them discoverable, and then you have multiple options that you or your users can access that data. You’re then able, if you wish, to combine that data with other data in one location. So how does all that work? What about security? Is it really that easy? And can you really move the data definition off to the Subject Matter Experts (SME’s) that know the particular data stack better than the IT team does? Well, nothing good is easy – but using the Data Hub is actually pretty simple. I’ll give you a link in a moment where you can sign up and try this yourself. Once you sign up, you assign an administrator. From there you’ll create data areas, and then use a simple interface to bring the data in. All of this is done in a portal interface – nothing to install, configure, update or manage. After the data is entered in, and you’ve assigned meta-data to describe it, your users have multiple options to access it. They can simply use the portal – which actually has powerful visualizations you can use on any platform, even mobile phones or tablets.     Your users can also hit the data with Excel – which gives them ultimate flexibility for display, all while using an authoritative, single reference for the data. Since the service is online, they can do this wherever they are – given the proper authentication and permissions. You can also hit the service with simple API calls, like this one from C#: http://msdn.microsoft.com/en-us/library/hh921924  You can make HTTP calls instead of code, and the data can even be exposed as an OData Feed. As you can see, there are a lot of options. You can check out the offering here: http://www.microsoft.com/en-us/sqlazurelabs/labs/data-hub.aspx and you can read the documentation here: http://msdn.microsoft.com/en-us/library/hh921938

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• Ideal data structure/techniques for storing generic scheduler data in C#

  - by GraemeMiller

  I am trying to implement a generic scheduler object in C# 4 which will output a table in HTML. Basic aim is to show some object along with various attributes, and whether it was doing something in a given time period. The scheduler will output a table displaying the headers: Detail Field 1 ....N| Date1.........N I want to initialise the table with a start date and an end date to create the date range (ideally could also do other time periods e.g. hours but that isn't vital). I then want to provide a generic object which will have associated events. Where an object has events within the period I want a table cell to be marked E.g. Name Height Weight 1/1/2011 2/1/2011 3/1/20011...... 31/1/2011 Ben 5.11 75 X X X Bill 5.7 83 X X So I created scheduler with Start Date=1/1/2011 and end date 31/1/2011 I'd like to give it my person object (already sorted) and tell it which fields I want displayed (Name, Height, Weight) Each person has events which have a start date and end date. Some events will start and end outwith but they should still be shown on the relevant date etc. Ideally I'd like to have been able to provide it with say a class booking object as well. So I'm trying to keep it generic. I have seen Javasript implementations etc of similar. What would a good data structure be for this? Any thoughts on techniques I could use to make it generic. I am not great with generics so any tips appreciated.

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