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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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  • Stackify featured in the KC Business Journal

    - by Matt Watson
    Very excited to be in the KC Business Journal today. Stackify is focused on giving limited production access to developers to help them do application troubleshooting. We about ready to launch our product and we are looking for beta testers!Ex-VinSolutions exec pours sale proceeds into Stackify, other tech startupsRead the entire article on their website:http://www.bizjournals.com/kansascity/print-edition/2012/06/01/ex-vinsolutions-exec-pours-sale.html

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  • Novell Revenues, Linux Business Slide

    <b>Datamation:</b> "It's been a tough quarter quarter for Novell (NASDAQ: NOVL) as questions about its future ownership remain on the table. Novell is also facing pricing pressure on its Linux business as renewals come up on Microsoft's SUSE Linux Enterprise subscriptions."

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  • AIIM, Oracle and Keste - Talking Social Business in LA

    - by Brian Dirking
    We had a great event today in Los Angeles - AIIM, Oracle and Keste presented on how organizations are making social business work. Atle Skjekkeland of AIIM presented How Social Business Is Driving Innovation. Atle talked about a number of fascinating points, such as how answers to questions come from unexpected sources. Atle cited the fact that 38% of organizations get half or more of answers from unexpected sources, which speaks to the wisdom of the crowds and how people are benefiting from open communications tools to get answers to their questions. He also had a number of hilarious examples of companies that don't get it. If Comcast were to go to YouTube and search Comcast, they would see the number one hit after their paid ad is a video of one of their technicians asleep on a customer's couch. Seems when he called the office for support he was put on hold so long he fell asleep. Dan O'Leary and Atle Skjekkeland After Atle's presentation I presented on Solving the Innovation Challenge with Oracle WebCenter. Atle had talked about McKinsey's research titled The Rise Of The Networked Enterprise: Web 2.0 Finds Its Payday. I brought in some new McKinsey research that built on that article. The new article is How Social Technologies Are Extending The Organization. A survey of 4,200 Global Executives brought three conclusions for the future: Boundaries among employees, vendors and customers will blur Employee teams will self-organize Data-driven decisions will rise These three items were themes that repeated through the day as we went through examples of what customers are doing today.  Next up was Vince Casarez of Keste. Vince was scheduled to profile one customer, but in an incredible 3 for 1 deal, Vince profiled Alcatel-Lucent, Qualcomm, and NetApp. Each of these implementations had content consolidation elements, as well as user engagement requirements that Keste was able to address with Oracle WebCenter. Vince Casarez of Keste And we had a couple of good tweets worth reprinting here. danieloleary Daniel O'Leary Learning about user engagement and social platforms from @bdirking #AIIM LA and @oracle event pic.twitter.com/1aNcLEUs danieloleary Daniel O'Leary Users want to be able to share data and activity streams, work at organizations that embrace social via @bdirking skjekkeland Atle Skjekkeland RT @danieloleary: Learning about user engagement and social platforms from @bdirking #AIIM LA and @oracle event pic.twitter.com/EWRYpvJa danieloleary Daniel O'Leary Thanks again to @bdirking for an amazing event in LA today, really impressed with the completeness of web center JimLundy Jim Lundy @ @danieloleary @bdirking yes, it is looking good - Web Center shadrachwhite Shadrach White @ @bdirking @heybenito I heard the #AIIM event in LA was a hit We had some great conversations through they day, many thanks to everyone who joined in. We look forward to continuing the conversation - thanks again to everyone who attended!

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  • Discover How to Deliver Measurable Business Value from your HCM Strategy

    - by Jay Richey, HCM Product Marketing
    Join our live Webcast on Wednesday, July 13 to learn how to fine tune your HCM strategy and better utlize your Oracle HCM investment.  In this session you'll learn how to access, analyze and act on information from multiple sources to ensure that all workforce decisions are focused on meeting overall business objectives. Date:Wednesday, July 13, 2011Time:10:00 a.m. PT / 1:00 p.m. ET Register now!

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  • Portal 11g (11.1.1.2) Certified with E-Business Suite

    - by Steven Chan
    Oracle Portal 11g allows you to build, deploy, and manage enterprise portals running on Oracle WebLogic Server.  Oracle Portal 11g includes integration with Oracle WebCenter Services 11g and BPEL, support for open portlet standards JSR 168, WSRP 2.0, and JSR 301.Portal 11g (11.1.1.2) is now certified with Oracle E-Business Suite Release 11i and 12.If you're running a previous version of Portal, there are a number of certified and supported upgrade paths to Portal 11g (11.1.1.2):

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  • SQL SERVER – Using expressor Composite Types to Enforce Business Rules

    - by pinaldave
    One of the features that distinguish the expressor Data Integration Platform from other products in the data integration space is its concept of composite types, which provide an effective and easily reusable way to clearly define the structure and characteristics of data within your application.  An important feature of the composite type approach is that it allows you to easily adjust the content of a record to its ultimate purpose.  For example, a record used to update a row in a database table is easily defined to include only the minimum set of columns, that is, a value for the key column and values for only those columns that need to be updated. Much like a class in higher level programming languages, you can also use the composite type as a way to enforce business rules onto your data by encapsulating a datum’s name, data type, and constraints (for example, maximum, minimum, or acceptable values) as a single entity, which ensures that your data can not assume an invalid value.  To what extent you use this functionality is a decision you make when designing your application; the expressor design paradigm does not force this approach on you. Let’s take a look at how these features are used.  Suppose you want to create a group of applications that maintain the employee table in your human resources database. Your table might have a structure similar to the HumanResources.Employee table in the AdventureWorks database.  This table includes two columns, EmployeID and rowguid, that are maintained by the relational database management system; you cannot provide values for these columns when inserting new rows into the table. Additionally, there are columns such as VacationHours and SickLeaveHours that you might choose to update for all employees on a monthly basis, which justifies creation of a dedicated application. By creating distinct composite types for the read, insert and update operations against this table, you can more easily manage this table’s content. When developing this application within expressor Studio, your first task is to create a schema artifact for the database table.  This process is completely driven by a wizard, only requiring that you select the desired database schema and table.  The resulting schema artifact defines the mapping of result set records to a record within the expressor data integration application.  The structure of the record within the expressor application is a composite type that is given the default name CompositeType1.  As you can see in the following figure, all columns from the table are included in the result set and mapped to an identically named attribute in the default composite type. If you are developing an application that needs to read this table, perhaps to prepare a year-end report of employees by department, you would probably not be interested in the data in the rowguid and ModifiedDate columns.  A typical approach would be to drop this unwanted data in a downstream operator.  But using an alternative composite type provides a better approach in which the unwanted data never enters your application. While working in expressor  Studio’s schema editor, simply create a second composite type within the same schema artifact, which you could name ReadTable, and remove the attributes corresponding to the unwanted columns. The value of an alternative composite type is even more apparent when you want to insert into or update the table.  In the composite type used to insert rows, remove the attributes corresponding to the EmployeeID primary key and rowguid uniqueidentifier columns since these values are provided by the relational database management system. And to update just the VacationHours and SickLeaveHours columns, use a composite type that includes only the attributes corresponding to the EmployeeID, VacationHours, SickLeaveHours and ModifiedDate columns. By specifying this schema artifact and composite type in a Write Table operator, your upstream application need only deal with the four required attributes and there is no risk of unintentionally overwriting a value in a column that does not need to be updated. Now, what about the option to use the composite type to enforce business rules?  If you review the composition of the default composite type CompositeType1, you will note that the constraints defined for many of the attributes mirror the table column specifications.  For example, the maximum number of characters in the NationaIDNumber, LoginID and Title attributes is equivalent to the maximum width of the target column, and the size of the MaritalStatus and Gender attributes is limited to a single character as required by the table column definition.  If your application code leads to a violation of these constraints, an error will be raised.  The expressor design paradigm then allows you to handle the error in a way suitable for your application.  For example, a string value could be truncated or a numeric value could be rounded. Moreover, you have the option of specifying additional constraints that support business rules unrelated to the table definition. Let’s assume that the only acceptable values for marital status are S, M, and D.  Within the schema editor, double-click on the MaritalStatus attribute to open the Edit Attribute window.  Then click the Allowed Values checkbox and enter the acceptable values into the Constraint Value text box. The schema editor is updated accordingly. There is one more option that the expressor semantic type paradigm supports.  Since the MaritalStatus attribute now clearly specifies how this type of information should be represented (a single character limited to S, M or D), you can convert this attribute definition into a shared type, which will allow you to quickly incorporate this definition into another composite type or into the description of an output record from a transform operator. Again, double-click on the MaritalStatus attribute and in the Edit Attribute window, click Convert, which opens the Share Local Semantic Type window that you use to name this shared type.  There’s no requirement that you give the shared type the same name as the attribute from which it was derived.  You should supply a name that makes it obvious what the shared type represents. In this posting, I’ve overviewed the expressor semantic type paradigm and shown how it can be used to make your application development process more productive.  The beauty of this feature is that you choose when and to what extent you utilize the functionality, but I’m certain that if you opt to follow this approach your efforts will become more efficient and your work will progress more quickly.  As always, I encourage you to download and evaluate expressor Studio for your current and future data integration needs. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, Pinal Dave, PostADay, SQL, SQL Authority, SQL Documentation, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • Warning: E-Business Suite Issues with Sun JRE 1.6.0_19

    - by Steven Chan
    Sadly, the issues reported in the following article also apply to JRE 1.6.0_19:Warning: E-Business Suite Issues with Sun JRE 1.6.0_18Once again, if you haven't already upgraded your end-users to JRE 1.6.0_18 or 1.6.0_19, we recommend that you to keep them on a prior JRE release such as 1.6.0_17 (6u17).We're working closely with the Sun JRE team to get this issue resolved as quickly as possible.  Please monitor this blog for updates.

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