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• Denormalization database

  - by Pedro Magalhaes

  I was taking a look at SSB (Star Schema Benchmark -http://www.percona.com/docs/wiki/_media/benchmark:ssb:starschemab.pdf) and then i was thinking if is possible to denormalize all tables from the SSB? So database size will increase a lot but potencially the performance will grow up. Is that right? Is It possible? Thanks and sorry for my poor english

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• How do I define a Calculated Measure in MDX based on a Dimension Attribute?

  - by ShaneD

  I would like to create a calculated measure that sums up only a specific subset of records in my fact table based on a dimension attribute. Given: Dimension Date LedgerLineItem {Charge, Payment, Write-Off, Copay, Credit} Measures LedgerAmount Relationships * LedgerLineItem is a degenerate dimension of FactLedger If I break down LedgerAmount by LedgerLineItem.Type I can easily see how much is charged, paid, credit, etc, but when I do not break it down by LedgerLineItem.Type I cannot easily add the credit, paid, credit, etc into a pivot table. I would like to create separate calculated measures that sum only specific type (or multiple types) of ledger facts. An example of the desired output would be: | Year | Charged | Total Paid | Amount - Ledger | | 2008 | $1000 | $600 | -$400 | | 2009 | $2000 | $1500 | -$500 | | Total | $3000 | $2100 | -$900 | I have tried to create the calculated measure a couple of ways and each one works in some circumstances but not in others. Now before anyone says do this in ETL, I have already done it in ETL and it works just fine. What I am trying to do as part of learning to understand MDX better is to figure out how to duplicate what I have done in the ETL in MDX as so far I am unable to do that. Here are two attempts I have made and the problems with them. This works only when ledger type is in the pivot table. It returns the correct amount of the ledger entries (although in this case it is identical to [amount - ledger] but when I try to remove type and just get the sum of all ledger entries it returns unknown. CASE WHEN ([Ledger].[Type].currentMember = [Ledger].[Type].&[Credit]) OR ([Ledger].[Type].currentMember = [Ledger].[Type].&[Paid]) OR ([Ledger].[Type].currentMember = [Ledger].[Type].&[Held Money: Copay]) THEN [Measures].[Amount - ledger] ELSE 0 END This works only when ledger type is not in the pivot table. It always returns the total payment amount, which is incorrect when I am slicing by type as I would only expect to see the credit portion under credit, the paid portion, under paid, $0 under charge, etc. sum({([Ledger].[Type].&[Credit]), ([Ledger].[Type].&[Paid]), ([Ledger].[Type].&[Held Money: Copay])}, [Measures].[Amount - ledger]) Is there any way to make this return the correct numbers regardless of whether Ledger.Type is included in my pivot table or not?

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• Analysis Services with excel as front end - is it possible to get the nicer UI that powerpivot provi

  - by AJM

  I have been looking into PowerPivot and concluded that for "self service BI" and ahoc buidling of cubes it has its uses. In particular I like the enhanced UI that you get from using PowerPivot rather than just using a PivotTable hooked up to an analysis services datasource. However it seems that hooking up PowerPivot to an existing analysis services cube is not a solution for "organisational BI". It is not always desireable to suck millions of rows into excel at once and the interface between PowerPivot and analysis services is very poor in my book. Hence the question is can an existing analysis services solution get the enhanced ui features that power pivot brings, withoout using powerpivot as the design tool? If powerpivot is aimed ad self service/personal BI then it seems bizare that the UI for this is better than for bigger/more costly analysis services solutions.

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• Star schema [fact 1:n dimension]...how?

  - by Mike Gates

  I am a newcomer to data warehouses and have what I hope is an easy question about building a star schema: If I have a fact table where a fact record naturally has a one-to-many relationship with a single dimension, how can a star schema be modeled to support this? For example: Fact Table: Point of Sale entry (the measurement is DollarAmount) Dimension Table: Promotions (these are sales promotions in effect when a sale was made) The situation is that I want a single Point Of Sale entry to be associated with multiple different Promotions. These Promotions cannot be their own dimensions as there are many many many promotions. How do I do this?

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• Visual Studio hangs when deploying a cube

  - by Richie

  Hello All, I'm having an issue with an Analysis Services project in Visual Studio 2005. My project always builds but only occasionally deploys. No errors are reported and VS just hangs. This is my first Analysis Services project so I am hoping that there is something obvious that I am just missing. Here is the situation I have a cube that I have successfully deployed. I then make some change, e.g., adding a hierarchy to a dimension. When I try to deploy again VS hangs. I have to restart Analysis Services to regain control of VS so I can shut it down. I restart everything sometimes once, sometimes twice or more before the project will eventually deploy. This happens with any change I make there seems to be no pattern to this behaviour. Sometimes I have to delete the cube from Analysis Services before restarting everything to get a successful deploy. Also I have successfully deployed the cube, and then subsequently successfully reprocessed a dimension then when I open a query window in SQL Server Management Studio it says that it can find any cubes. As a test I have deployed a cube successfully. I have then deleted it in Analysis Services and attempted to redeploy it, without making any changes to the cube, only to have the same behaviour mentioned above. VS just hangs with no reason so I have no idea where to start hunting down the problem. It is taking 15-20 minutes to make a change as simple as setting the NameColumn of a dimension attribute. As you can imagine this is taking hours of my time so I would greatly appreciate any assistance anyone can give me.

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• Hbase schema design -- to make sorting easy?

  - by chen

  I have 1M words in my dictionary. Whenever a user issue a query on my website, I will see if the query contains the words in my dictionary and increment the counter corresponding to them individually. Here is the example, say if a user type in "Obama is a president" and "Obama" and "president" are in my dictionary, then I should increment the counter by 1 for "Obama" and "president". And from time to time, I want to see the top 100 words (most queried words). If I use Hbase to store the counter, what schema should I use? -- I have not come up an efficient one yet. If I use word in my dictionary as row key, and "counter" as column key, then updating counter(increment) is very efficient. But it's very hard to sort and return the top 100. Anyone can give a good advice? Thanks.

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• Database for managing large volumes of (system) metrics

  - by symcbean

  Hi, I'm looking at building a system for managing and reporting stats on web page performance. I'll be collecting a lot more stats than are available in the standard log formats (approx 20 metrics) but compared to most types of database applications, the base data structure will be very simple. My problem is that I'll be accumulating a lot of data - in the region of 100,000 records (i.e. sets of metrics) per hour. Of course, resources are very limited! So that its possible to sensibly interact with the data, I'd need to consolidate each metric into one minute bins, broken down by URL, then for anything more than 1 day old, consolidated into 10 minute bins, then at 1 week, hourly bins. At the front end, I want to provide a view (prefereably as plots) of the last hour of data, with the facility for users to drill up/down through defined hierarchies of URLs (which do not always map directly to the hierarchy expressed in the path of the URL) and to view different time frames. Rather than coding all this myself and using a relational database, I was wondering if there were tools available which would facilitate both the management of the data and the reporting. I had a look at Mondrian however I can't see from the documentation I've looked at whether it's possible to drop the more granular information while maintaining the consolidated views of the data. RRDTool looks promising in terms of managing the data consolidation, but seems to be rather limited in terms of querying the dataset as a multi-dimensional/relational database. What else whould I be looking at?

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• MDX filter members in a sum-function

  - by Radagast2005

  I have an SSAS cube containing customers and their purchased memberships (let's say magazines). I want to calculate the retention. I.e. how many customers remain a customer. To do this I specify a set (males, 30yrs, 1 january 2011). I want to see if this set - identified by a customer number - is still present in the following months. I named my set MySet2. What I try to do is: sum(measures.amount, [membership].[name].&[X], MySet2, [date].[date].currentmember) However, the result is incorrect. The number is far to low. I suspect it still tries to account for all the males of 30 years old, but after a year they're not 30 anymore. What am I missing? I looked at scope and filter, but I'm not sure how to apply it.

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• Chaining Many-To-Many Dimensional Relationships in SSAS

  - by Ray Saltrelli

  I'm developing a cube in SSAS and attempting to model the following relationships: Many Facts to 1 Customer Many Customers to Many Sales Reps Many Sales Reps (Subordinates) to Sales Reps (Managers) Each M2M relationship is facilitated by a bridge table which also acts as a fact table in the cube I have most of this working. I can slice Facts by Customer and by Sales Rep (Subordinate), but when I add Sales Rep (Manager) to the query it appears to return every subordinate/manager combination regardless of whether or not that relationship exists in the bridge table. Any ideas as to what I might be doing wrong?

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• Getting a count of users each day in Mondrian MDX

  - by user1874144

  I'm trying to write a query to give me the total number of users for each customer per day. Here is what I have so far, which for each customer/day combination is giving the total number of user dimension entries without splitting them up by customer/day. WITH MEMBER [Measures].[MyUserCount] AS COUNT(Descendants([User].CurrentMember, [User].[User Name]), INCLUDEEMPTY) SELECT NON EMPTY CrossJoin([Date].[Date].Members, [Customer].[Customer Name].Members) ON ROWS, {[Measures].[MyUserCount]} on COLUMNS FROM [Users]

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• Resources to learn about engineering aspects of data analytics (OLAP, warehousing, ETL, etc.)

  - by JT

  I'm a math/stats guy, interested in learning more about the engineering aspects of "data analytics" (this may be an overly broad term, this is a case of "I don't know what I don't know", so I'm not sure how to be more specific). I'm fine with manipulating and analyzing the data once it's already stored somewhere and I can access it, and I'm fine with writing scripts and SQL queries (and have a general knowledge of things like normalization). What I don't know is the whole engineering process of capturing and storing the data. For example, terms I've heard thrown about that I only vaguely understand the meaning of include: - OLAP, OLTP - Data warehousing - ETL - ??? What's a good book (or any other resource) to learn about these kinds of things? What are things I should know about database design (normalization seems kinda "obvious" to me, something I would have done even before I knew the term -- is there anything else?)? In other words, for jobs falling under the umbrella term of "analytics engineer", what kinds of things should I know?

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• How to resolve: 'cmd' is not recognized as an internal or external command?

  - by qwer1234

  I have searched other forums to solve this error where it would either end with: 1.) re-install OS 2.) Setting path variable C:/Windows/System32 The latter did not work, and as you can probably imagine, I do not want to have to re-install my OS... I am running the command "mvn jetty:run" and the following is my stack trace, finishing with the message: "'cmd' is not recognized as an internal or external command, operable problem or batch file" as stated in the title of this question. [INFO] Scanning for projects... [INFO] ------------------------------------------------------------------------ [INFO] Building Test Tool [INFO] task-segment: [jetty:run] [INFO] ------------------------------------------------------------------------ [INFO] Preparing jetty:run [WARNING] Removing: run from forked lifecycle, to prevent recursive invocation. [INFO] [resources:resources] [WARNING] Using platform encoding (Cp1252 actually) to copy filtered resources, i.e. build is platform dependent! [INFO] Copying 32 resources [INFO] Copying 192 resources [INFO] [compiler:compile] [INFO] Compiling 1854 source files to C:\Development\global_stock_record\test\java\Turtle\target\classes [INFO] ------------------------------------------------------------------------ [ERROR] BUILD FAILURE [INFO] ------------------------------------------------------------------------ [INFO] Compilation failure C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\compilers\JavaScriptClassCompiler.java:[45,29] cannot find symbol symbol : class CompilerEnvirons location: package org.mozilla.javascript C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\compilers\JavaScriptClassCompiler.java:[47,29] cannot find symbol symbol : class ContextFactory location: package org.mozilla.javascript C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\compilers\JavaScriptClassCompiler.java:[49,39] cannot find symbol symbol : class ClassCompiler location: package org.mozilla.javascript.optimizer C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\compilers\JavaScriptClassCompiler.java:[181,55] cannot find symbol symbol : class CompilerEnvirons location: class net.sf.jasperreports.compilers.JavaScriptClassCompiler C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\export\JRXmlExporter.java:[99,26] package org.w3c.tools.codec does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\JRBaseFactory.java:[26,34] package org.apache.commons.digester does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\JRBaseFactory.java:[27,34] package org.apache.commons.digester does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\JRBaseFactory.java:[34,47] cannot find symbol symbol: class ObjectCreationFactory public abstract class JRBaseFactory implements ObjectCreationFactory C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\JRBaseFactory.java:[41,21] cannot find symbol symbol : class Digester location: class net.sf.jasperreports.engine.xml.JRBaseFactory C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\JRBaseFactory.java:[47,8] cannot find symbol symbol : class Digester location: class net.sf.jasperreports.engine.xml.JRBaseFactory C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\JRBaseFactory.java:[56,25] cannot find symbol symbol : class Digester location: class net.sf.jasperreports.engine.xml.JRBaseFactory C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\Code39Component.java:[28,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\BarcodeComponent.java:[41,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\Code39Component.java:[66,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.Code39Component C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\BarcodeComponent.java:[179,29] cannot find symbol symbol : class HumanReadablePlacement location: class net.sf.jasperreports.components.barcode4j.BarcodeComponent C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\EAN128Component.java:[26,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\DataMatrixComponent.java:[26,45] package org.krysalis.barcode4j.impl.datamatrix does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\FourStateBarcodeComponent.java:[26,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\UPCAComponent.java:[28,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\UPCEComponent.java:[28,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\EAN13Component.java:[28,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\EAN8Component.java:[28,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\Interleaved2Of5Component.java:[28,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\EAN128Component.java:[57,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.EAN128Component C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\DataMatrixComponent.java:[62,22] cannot find symbol symbol : class SymbolShapeHint location: class net.sf.jasperreports.components.barcode4j.DataMatrixComponent C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\FourStateBarcodeComponent.java:[76,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.FourStateBarcodeComponent C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\UPCAComponent.java:[56,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.UPCAComponent C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\UPCEComponent.java:[56,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.UPCEComponent C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\EAN13Component.java:[56,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.EAN13Component C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\EAN8Component.java:[56,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.EAN8Component C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\Interleaved2Of5Component.java:[60,29] cannot find symbol symbol : class ChecksumMode location: class net.sf.jasperreports.components.barcode4j.Interleaved2Of5Component C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRHibernateAbstractDataSource.java:[36,25] package org.hibernate.type does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[49,20] package org.hibernate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[50,20] package org.hibernate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[51,20] package org.hibernate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[52,20] package org.hibernate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[53,20] package org.hibernate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[54,25] package org.hibernate.type does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRHibernateAbstractDataSource.java:[173,38] cannot find symbol symbol : class Type location: class net.sf.jasperreports.engine.data.JRHibernateAbstractDataSource C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[66,35] cannot find symbol symbol : class Type location: class net.sf.jasperreports.engine.query.JRHibernateQueryExecuter C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[89,9] cannot find symbol symbol : class Session location: class net.sf.jasperreports.engine.query.JRHibernateQueryExecuter C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[90,9] cannot find symbol symbol : class Query location: class net.sf.jasperreports.engine.query.JRHibernateQueryExecuter C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[92,9] cannot find symbol symbol : class ScrollableResults location: class net.sf.jasperreports.engine.query.JRHibernateQueryExecuter C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[359,8] cannot find symbol symbol : class Type location: class net.sf.jasperreports.engine.query.JRHibernateQueryExecuter C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\query\JRHibernateQueryExecuter.java:[474,8] cannot find symbol symbol : class ScrollableResults location: class net.sf.jasperreports.engine.query.JRHibernateQueryExecuter C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barbecue\BarbecueFillComponent.java:[40,31] package net.sourceforge.barbecue does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[38,27] package org.apache.tools.ant does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[39,27] package org.apache.tools.ant does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[40,27] package org.apache.tools.ant does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[41,33] package org.apache.tools.ant.types does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[42,33] package org.apache.tools.ant.types does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[43,43] package org.apache.tools.ant.types.resources does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[44,32] package org.apache.tools.ant.util does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[45,32] package org.apache.tools.ant.util does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRBaseAntTask.java:[34,36] package org.apache.tools.ant.taskdefs does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRBaseAntTask.java:[41,35] cannot find symbol symbol: class MatchingTask public class JRBaseAntTask extends MatchingTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[74,9] cannot find symbol symbol : class Path location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[76,9] cannot find symbol symbol : class Path location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[86,23] cannot find symbol symbol : class Path location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[104,8] cannot find symbol symbol : class Path location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[131,8] cannot find symbol symbol : class Path location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[145,30] cannot find symbol symbol : class BuildException location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[183,41] cannot find symbol symbol : class BuildException location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[211,33] cannot find symbol symbol : class BuildException location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\ant\JRAntXmlExportTask.java:[276,32] cannot find symbol symbol : class BuildException location: class net.sf.jasperreports.ant.JRAntXmlExportTask C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\TransformedPropertyRule.java:[27,34] package org.apache.commons.digester does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\xml\TransformedPropertyRule.java:[37,54] cannot find symbol symbol: class Rule public abstract class TransformedPropertyRule extends Rule C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\data\mondrian\MondrianDataAdapterService.java:[29,20] package mondrian.olap does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\data\mondrian\MondrianDataAdapterService.java:[30,20] package mondrian.olap does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\data\mondrian\MondrianDataAdapterService.java:[31,20] package mondrian.olap does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\data\mondrian\MondrianDataAdapterService.java:[45,9] cannot find symbol symbol : class Connection location: class net.sf.jasperreports.data.mondrian.MondrianDataAdapterService C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRXlsDataSource.java:[40,10] package jxl does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRXlsDataSource.java:[41,10] package jxl does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRXlsDataSource.java:[42,10] package jxl does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRXlsDataSource.java:[43,20] package jxl.read.biff does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRXlsDataSource.java:[66,9] cannot find symbol symbol : class Workbook location: class net.sf.jasperreports.engine.data.JRXlsDataSource C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\engine\data\JRXlsDataSource.java:[83,24] cannot find symbol symbol : class Workbook location: class net.sf.jasperreports.engine.data.JRXlsDataSource C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\olap\xmla\JRXmlaMember.java:[26,20] package mondrian.olap does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\olap\result\JROlapMember.java:[26,20] package mondrian.olap does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\olap\xmla\JRXmlaMember.java:[89,8] cannot find symbol symbol : class Member location: class net.sf.jasperreports.olap.xmla.JRXmlaMember C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\olap\result\JROlapMember.java:[46,1] cannot find symbol symbol : class Member location: interface net.sf.jasperreports.olap.result.JROlapMember C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\web\actions\AbstractAction.java:[43,36] package org.codehaus.jackson.annotate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\web\actions\AbstractAction.java:[49,1] cannot find symbol symbol: class JsonTypeInfo @JsonTypeInfo(use=JsonTypeInfo.Id.NAME, include=JsonTypeInfo.As.PROPERTY, property="actionName") C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[32,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[33,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[34,29] package org.krysalis.barcode4j does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[35,34] package org.krysalis.barcode4j.impl does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[36,42] package org.krysalis.barcode4j.impl.codabar does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[37,42] package org.krysalis.barcode4j.impl.code128 does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[38,42] package org.krysalis.barcode4j.impl.code128 does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[39,41] package org.krysalis.barcode4j.impl.code39 does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[40,45] package org.krysalis.barcode4j.impl.datamatrix does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[41,45] package org.krysalis.barcode4j.impl.datamatrix does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[42,44] package org.krysalis.barcode4j.impl.fourstate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[43,44] package org.krysalis.barcode4j.impl.fourstate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[44,44] package org.krysalis.barcode4j.impl.fourstate does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[45,42] package org.krysalis.barcode4j.impl.int2of5 does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[46,41] package org.krysalis.barcode4j.impl.pdf417 does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[47,42] package org.krysalis.barcode4j.impl.postnet does not exist C:\Development\global_stock_record\test\java\Turtle\src\main\java\net\sf\jasperreports\components\barcode4j\AbstractBarcodeEvaluator.java:[48,41] package org.krysalis.barcode4j.impl.upcean does not exist [INFO] ------------------------------------------------------------------------ [INFO] For more information, run Maven with the -e switch [INFO] ------------------------------------------------------------------------ [INFO] Total time: 17 seconds [INFO] Finished at: Fri Dec 07 11:46:28 EST 2012 [INFO] Final Memory: 27M/63M [INFO] ------------------------------------------------------------------------

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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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• Into Orbit (OBIEE 11g Launch)

  - by Darryn.Hinett

  After much anticipation, it appears that OBIEE 11g is about to hit the streets. Join Charles Phillips, President, and Thomas Kurian, Executive Vice President, Product Development, for the launch of the latest release of Oracle's business intelligence software. Be the first to hear about Oracle Business Intelligence Enterprise Edition 11g, the new, industry-leading technology platform for business intelligence, which offers: A powerful end-user experience with rich visualisation, search, and actionable collaboration Advancements in analytics, OLAP, and enterprise reporting, with unmatched performance and scalability Simplified system configuration, life-cycle management, and performance optimisation As well as the keynote and technical general session, break out sessions will cover the following topics: Business Intelligence: From Insight to Action In this session, you will learn about an exciting, industry-first innovation that connects business intelligence directly to your business processes. You can spot an opportunity or issue, and immediately initiate appropriate action directly from your dashboard. Oracle Business Intelligence Enterprise Edition 11g Systems Management and Deployment Learn how you can streamline the process of configuring your system, provisioning users, and monitoring and optimising query performance. Attend this session to hear how new integration with Oracle Enterprise Manager provides unique systems management, superior scalability, and high availability and security benefits, while making upgrades effortless. Extending Business Intelligence Analytics with Online Analytical Processing (OLAP) Learn how you can enhance the analytical power and business value of your BI solution with a unified environment for navigating and querying both OLAP and relational data sources. This session will focus on how Oracle Business Intelligence Enterprise Edition 11g, used with Oracle Essbase, can deliver insight at the speed of thought. Integrated Performance Management If your organisation is using or considering performance management applications such as Oracle's Hyperion Planning and Hyperion Financial Management, you will not want to miss this session. See how you can leverage Oracle's BI solution for accessing performance management applications and performing extended financial reporting and analysis. Visualisation and End-user Experience The latest release of Oracle Business Intelligence provides an unrivaled end user experience, including rich interactive dashboards, a vast range of animated charting options, integrated search, and more. This session will also include a close look at how you can leverage location data to visualise geo-spatial information.

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• links for 2010-04-19

  - by Bob Rhubart

  @lucasjellema: Book review -- Getting Started With Oracle SOA Suite 11g R1: A Hands-On Tutorial "I have to confess that I may be biased – or at least that I have a personal stake in books about the SOA Suite. I am currently in the final stages of writing the Oracle SOA Suite 11g Handbook, published by Oracle Press (see http://www.mhprofessional.com/product.php?isbn=0071608974 and http://wiki.oracle.com/page/Oracle+11g+SOA+Suite+Handbook for some supporting material and early screenshots) which you could consider a competitor to the book I am discussing here. I would suggest however that the two are quite complementary: after reading the Getting Started With Oracle SOA Suite 11g R1: A Hands-On Tutorial and concluding that you want to learn more and delve deeper into the SOA Suite and the concepts around it, it would make perfect sense to read my book, Oracle SOA Suite 11g Handbook, as that takes you to the next level." -- Oracle ACE Director Lucas Jellema of Amis Technology (tags: oracle otn oracleace soa bookreview soasuite) Terri Noyes: The Scoop: Oracle E-Business Suite Support on 64-bit Linux Terri Noyes addresses frequently asked questions about Oracle E-Business Suite (EBS) 64-bit Linux support. (tags: otn oracle ebs linux) Sunil S. Ranka: My session at Collaborate 10 – Las Vegas, Nevada, USA Sunil S. Ranka checking in from the Luxor with the details of his Collaborate 2010 presentation on Business Intelligence. (tags: oracle otn businessintelligence obiee collaborate2010) @bex: Bezzotech and IRA Merge Into One! Oracle ACE Director Bex Huff with details on his new partnership with Jason Clarkin from Impement R Advantage and their joint presentations at Collaborate 2010. (tags: oracle otn oracleace enterprise2.0 ucm collaborate2010) Mike Donohue: Collaborate 2010 Sunday Update - Oracle Business Intelligence Publisher Hands On Lab Updates on the session schedule an room numbers for the Oracle Business Intelligence Publisher Hands On Lab, 3:45 pm - 4:45 pm in Palm B. (tags: oracle otn collaborate2010 businessintelligence) @ORACLENERD: COLLABORATE: OAUG 20th Anniversary Chet "oraclenerd" Justice shares the details of his first day at Collaborate 2010. Venkatakrishnan J: Oracle EPM 11.1.1.3 & Oracle OLAP 11g – Reporting on Oracle OLAP using Essbase Excel Add-in/Smartview – XOLAP Some of the stuff Venkatakrishnan J was going to present at Collaborate 2010 until an Icelandic volcano got in the way. (tags: oracle olap businessintelligence database collaborate2010)

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• Fraud Detection with the SQL Server Suite Part 2

  - by Dejan Sarka

  This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t know whether there is already an existing infrastructure, like a data warehouse, in place, or whether we would need to build one from scratch. Therefore, I always suggest to start with a proof-of-concept (POC) project. A POC takes something between 5 and 10 working days, and involves personnel from the customer’s site – either employees or outsourced consultants. The team should include a subject matter expert (SME) and at least one information technology (IT) expert. The SME must be familiar with both the domain in question as well as the meaning of data at hand, while the IT expert should be familiar with the structure of data, how to access it, and have some programming (preferably Transact-SQL) knowledge. With more than one IT expert the most time consuming work, namely data preparation and overview, can be completed sooner. I assume that the relevant data is already extracted and available at the very beginning of the POC project. If a customer wants to have their people involved in the project directly and requests the transfer of knowledge, the project begins with training. I strongly advise this approach as it offers the establishment of a common background for all people involved, the understanding of how the algorithms work and the understanding of how the results should be interpreted, a way of becoming familiar with the SQL Server suite, and more. Once the data has been extracted, the customer’s SME (i.e. the analyst), and the IT expert assigned to the project will learn how to prepare the data in an efficient manner. Together with me, knowledge and expertise allow us to focus immediately on the most interesting attributes and identify any additional, calculated, ones soon after. By employing our programming knowledge, we can, for example, prepare tens of derived variables, detect outliers, identify the relationships between pairs of input variables, and more, in only two or three days, depending on the quantity and the quality of input data. I favor the customer’s decision of assigning additional personnel to the project. For example, I actually prefer to work with two teams simultaneously. I demonstrate and explain the subject matter by applying techniques directly on the data managed by each team, and then both teams continue to work on the data overview and data preparation under our supervision. I explain to the teams what kind of results we expect, the reasons why they are needed, and how to achieve them. Afterwards we review and explain the results, and continue with new instructions, until we resolve all known problems. Simultaneously with the data preparation the data overview is performed. The logic behind this task is the same – again I show to the teams involved the expected results, how to achieve them and what they mean. This is also done in multiple cycles as is the case with data preparation, because, quite frankly, both tasks are completely interleaved. A specific objective of the data overview is of principal importance – it is represented by a simple star schema and a simple OLAP cube that will first of all simplify data discovery and interpretation of the results, and will also prove useful in the following tasks. The presence of the customer’s SME is the key to resolving possible issues with the actual meaning of the data. We can always replace the IT part of the team with another database developer; however, we cannot conduct this kind of a project without the customer’s SME. After the data preparation and when the data overview is available, we begin the scientific part of the project. I assist the team in developing a variety of models, and in interpreting the results. The results are presented graphically, in an intuitive way. While it is possible to interpret the results on the fly, a much more appropriate alternative is possible if the initial training was also performed, because it allows the customer’s personnel to interpret the results by themselves, with only some guidance from me. The models are evaluated immediately by using several different techniques. One of the techniques includes evaluation over time, where we use an OLAP cube. After evaluating the models, we select the most appropriate model to be deployed for a production test; this allows the team to understand the deployment process. There are many possibilities of deploying data mining models into production; at the POC stage, we select the one that can be completed quickly. Typically, this means that we add the mining model as an additional dimension to an existing DW or OLAP cube, or to the OLAP cube developed during the data overview phase. Finally, we spend some time presenting the results of the POC project to the stakeholders and managers. Even from a POC, the customer will receive lots of benefits, all at the sole risk of spending money and time for a single 5 to 10 day project: The customer learns the basic patterns of frauds and fraud detection The customer learns how to do the entire cycle with their own people, only relying on me for the most complex problems The customer’s analysts learn how to perform much more in-depth analyses than they ever thought possible The customer’s IT experts learn how to perform data extraction and preparation much more efficiently than they did before All of the attendees of this training learn how to use their own creativity to implement further improvements of the process and procedures, even after the solution has been deployed to production The POC output for a smaller company or for a subsidiary of a larger company can actually be considered a finished, production-ready solution It is possible to utilize the results of the POC project at subsidiary level, as a finished POC project for the entire enterprise Typically, the project results in several important “side effects” Improved data quality Improved employee job satisfaction, as they are able to proactively contribute to the central knowledge about fraud patterns in the organization Because eventually more minds get to be involved in the enterprise, the company should expect more and better fraud detection patterns After the POC project is completed as described above, the actual project would not need months of engagement from my side. This is possible due to our preference to transfer the knowledge onto the customer’s employees: typically, the customer will use the results of the POC project for some time, and only engage me again to complete the project, or to ask for additional expertise if the complexity of the problem increases significantly. I usually expect to perform the following tasks: Establish the final infrastructure to measure the efficiency of the deployed models Deploy the models in additional scenarios Through reports By including Data Mining Extensions (DMX) queries in OLTP applications to support real-time early warnings Include data mining models as dimensions in OLAP cubes, if this was not done already during the POC project Create smart ETL applications that divert suspicious data for immediate or later inspection I would also offer to investigate how the outcome could be transferred automatically to the central system; for instance, if the POC project was performed in a subsidiary whereas a central system is available as well Of course, for the actual project, I would repeat the data and model preparation as needed It is virtually impossible to tell in advance how much time the deployment would take, before we decide together with customer what exactly the deployment process should cover. Without considering the deployment part, and with the POC project conducted as suggested above (including the transfer of knowledge), the actual project should still only take additional 5 to 10 days. The approximate timeline for the POC project is, as follows: 1-2 days of training 2-3 days for data preparation and data overview 2 days for creating and evaluating the models 1 day for initial preparation of the continuous learning infrastructure 1 day for presentation of the results and discussion of further actions Quite frequently I receive the following question: are we going to find the best possible model during the POC project, or during the actual project? My answer is always quite simple: I do not know. Maybe, if we would spend just one hour more for data preparation, or create just one more model, we could get better patterns and predictions. However, we simply must stop somewhere, and the best possible way to do this, according to my experience, is to restrict the time spent on the project in advance, after an agreement with the customer. You must also never forget that, because we build the complete learning infrastructure and transfer the knowledge, the customer will be capable of doing further investigations independently and improve the models and predictions over time without the need for a constant engagement with me.

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• "Accelerating Data Warehouses", Oracle Magazine, May/June 2008, Technology

  Oracle OLAP 11g brings high-performance data warehouse features to Oracle Database 11g.

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• Why creating a new MDX language instead of extending SQL?

  - by DReispt

  I have a long experience with SQL, but recently began working with datawarehouse and OLAP technologies: building fact and dimension tables, that then are queried using MDX (MultiDimensional eXpressions). The problem is that MDX works with a completely different logic compared to SQL, and it's a whole new learning curve even for someone with a strong SQL background. Yes, MDX allows you to do things that would be hard or almost impossible with plain SQL. But sometimes it's frustrating to be hours around an MDX to do something you know you could achieve in minutes using SQL (ok, you can tell me to RTFM ...). But why go on to the trouble of creating a new completely different language when you could build on SQL, extend it to add the features needed by OLAP applications?

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• ADOMD & Excel Integration

  - by koumides

  All, We have an Excel spreadsheet that uses ADOMD to query OLAP cubes and present the data in Excel. We are using version 2.8 at the moment of the ADOMD API. As far as I know there is an ADOMD.NET API for querying OLAP cubes. Can this new .NET version used from inside Excel ? Many Thanks, MK

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• Component for processing cube

  - by Sreejesh Kumar

  how do we process a cube or access OLAP database through ASP.Net with C# code? what is the component to be used, in C#.Net for connecting OLAP database or process actions in anaysis Services ?

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• SSAS: Utility to export SQL code from your cube's Data Source View (DSV)

  - by DrJohn

  When you are working on a cube, particularly in a multi-person team, it is sometimes necessary to review what changes that have been done to the SQL queries in the cube's data source view (DSV). This can be a problem as the SQL editor in the DSV is not the best interface to review code. Now of course you can cut and paste the SQL into SSMS, but you have to do each query one-by-one. What is worse your DBA is unlikely to have BIDS installed, so you will have to manually export all the SQL yourself and send him the files. To make it easy to get hold of the SQL in a Data Source View, I developed a C# utility which connects to an OLAP database and uses Analysis Services Management Objects (AMO) to obtain and export all the SQL to a series of files. The added benefit of this approach is that these SQL files can be placed under source code control which means the DBA can easily compare one version with another. The Trick When I came to implement this utility, I quickly found that the AMO API does not give direct access to anything useful about the tables in the data source view. Iterating through the DSVs and tables is easy, but getting to the SQL proved to be much harder. My Google searches returned little of value, so I took a look at the idea of using the XmlDom to open the DSV’s XML and obtaining the SQL from that. This is when the breakthrough happened. Inspecting the DSV’s XML I saw the things I was interested in were called TableType DbTableName FriendlyName QueryDefinition Searching Google for FriendlyName returned this page: Programming AMO Fundamental Objects which hinted at the fact that I could use something called ExtendedProperties to obtain these XML attributes. This simplified my code tremendously to make the implementation almost trivial. So here is my code with appropriate comments. The full solution can be downloaded from here: ExportCubeDsvSQL.zip   using System;using System.Data;using System.IO;using Microsoft.AnalysisServices; ... class code removed for clarity// connect to the OLAP server Server olapServer = new Server();olapServer.Connect(config.olapServerName);if (olapServer != null){ // connected to server ok, so obtain reference to the OLAP databaseDatabase olapDatabase = olapServer.Databases.FindByName(config.olapDatabaseName);if (olapDatabase != null){ Console.WriteLine(string.Format("Succesfully connected to '{0}' on '{1}'",   config.olapDatabaseName,   config.olapServerName));// export SQL from each data source view (usually only one, but can be many!)foreach (DataSourceView dsv in olapDatabase.DataSourceViews){ Console.WriteLine(string.Format("Exporting SQL from DSV '{0}'", dsv.Name));// for each table in the DSV, export the SQL in a fileforeach (DataTable dt in dsv.Schema.Tables){ Console.WriteLine(string.Format("Exporting SQL from table '{0}'", dt.TableName)); // get name of the table in the DSV// use the FriendlyName as the user inputs this and therefore has control of itstring queryName = dt.ExtendedProperties["FriendlyName"].ToString().Replace(" ", "_");string sqlFilePath = Path.Combine(targetDir.FullName, queryName + ".sql"); // delete the sql file if it exists... file deletion code removed for clarity// write out the SQL to a fileif (dt.ExtendedProperties["TableType"].ToString() == "View"){ File.WriteAllText(sqlFilePath, dt.ExtendedProperties["QueryDefinition"].ToString());}if (dt.ExtendedProperties["TableType"].ToString() == "Table"){ File.WriteAllText(sqlFilePath, dt.ExtendedProperties["DbTableName"].ToString()); } } } Console.WriteLine(string.Format("Successfully written out SQL scripts to '{0}'", targetDir.FullName)); } }   Of course, if you are following industry best practice, you should be basing your cube on a series of views. This will mean that this utility will be of limited practical value unless of course you are inheriting a project and want to check if someone did the implementation correctly.

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• Fraud Detection with the SQL Server Suite Part 1

  - by Dejan Sarka

  While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of blog posts which will at the end make the whole whitepaper. Abstract With the massive usage of credit cards and web applications for banking and payment processing, the number of fraudulent transactions is growing rapidly and on a global scale. Several fraud detection algorithms are available within a variety of different products. In this paper, we focus on using the Microsoft SQL Server suite for this purpose. In addition, we will explain our original approach to solving the problem by introducing a continuous learning procedure. Our preferred type of service is mentoring; it allows us to perform the work and consulting together with transferring the knowledge onto the customer, thus making it possible for a customer to continue to learn independently. This paper is based on practical experience with different projects covering online banking and credit card usage. Introduction A fraud is a criminal or deceptive activity with the intention of achieving financial or some other gain. Fraud can appear in multiple business areas. You can find a detailed overview of the business domains where fraud can take place in Sahin Y., & Duman E. (2011), Detecting Credit Card Fraud by Decision Trees and Support Vector Machines, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011 Vol 1. Hong Kong: IMECS. Dealing with frauds includes fraud prevention and fraud detection. Fraud prevention is a proactive mechanism, which tries to disable frauds by using previous knowledge. Fraud detection is a reactive mechanism with the goal of detecting suspicious behavior when a fraudster surpasses the fraud prevention mechanism. A fraud detection mechanism checks every transaction and assigns a weight in terms of probability between 0 and 1 that represents a score for evaluating whether a transaction is fraudulent or not. A fraud detection mechanism cannot detect frauds with a probability of 100%; therefore, manual transaction checking must also be available. With fraud detection, this manual part can focus on the most suspicious transactions. This way, an unchanged number of supervisors can detect significantly more frauds than could be achieved with traditional methods of selecting which transactions to check, for example with random sampling. There are two principal data mining techniques available both in general data mining as well as in specific fraud detection techniques: supervised or directed and unsupervised or undirected. Supervised techniques or data mining models use previous knowledge. Typically, existing transactions are marked with a flag denoting whether a particular transaction is fraudulent or not. Customers at some point in time do report frauds, and the transactional system should be capable of accepting such a flag. Supervised data mining algorithms try to explain the value of this flag by using different input variables. When the patterns and rules that lead to frauds are learned through the model training process, they can be used for prediction of the fraud flag on new incoming transactions. Unsupervised techniques analyze data without prior knowledge, without the fraud flag; they try to find transactions which do not resemble other transactions, i.e. outliers. In both cases, there should be more frauds in the data set selected for checking by using the data mining knowledge compared to selecting the data set with simpler methods; this is known as the lift of a model. Typically, we compare the lift with random sampling. The supervised methods typically give a much better lift than the unsupervised ones. However, we must use the unsupervised ones when we do not have any previous knowledge. Furthermore, unsupervised methods are useful for controlling whether the supervised models are still efficient. Accuracy of the predictions drops over time. Patterns of credit card usage, for example, change over time. In addition, fraudsters continuously learn as well. Therefore, it is important to check the efficiency of the predictive models with the undirected ones. When the difference between the lift of the supervised models and the lift of the unsupervised models drops, it is time to refine the supervised models. However, the unsupervised models can become obsolete as well. It is also important to measure the overall efficiency of both, supervised and unsupervised models, over time. We can compare the number of predicted frauds with the total number of frauds that include predicted and reported occurrences. For measuring behavior across time, specific analytical databases called data warehouses (DW) and on-line analytical processing (OLAP) systems can be employed. By controlling the supervised models with unsupervised ones and by using an OLAP system or DW reports to control both, a continuous learning infrastructure can be established. There are many difficulties in developing a fraud detection system. As has already been mentioned, fraudsters continuously learn, and the patterns change. The exchange of experiences and ideas can be very limited due to privacy concerns. In addition, both data sets and results might be censored, as the companies generally do not want to publically expose actual fraudulent behaviors. Therefore it can be quite difficult if not impossible to cross-evaluate the models using data from different companies and different business areas. This fact stresses the importance of continuous learning even more. Finally, the number of frauds in the total number of transactions is small, typically much less than 1% of transactions is fraudulent. Some predictive data mining algorithms do not give good results when the target state is represented with a very low frequency. Data preparation techniques like oversampling and undersampling can help overcome the shortcomings of many algorithms. SQL Server suite includes all of the software required to create, deploy any maintain a fraud detection infrastructure. The Database Engine is the relational database management system (RDBMS), which supports all activity needed for data preparation and for data warehouses. SQL Server Analysis Services (SSAS) supports OLAP and data mining (in version 2012, you need to install SSAS in multidimensional and data mining mode; this was the only mode in previous versions of SSAS, while SSAS 2012 also supports the tabular mode, which does not include data mining). Additional products from the suite can be useful as well. SQL Server Integration Services (SSIS) is a tool for developing extract transform–load (ETL) applications. SSIS is typically used for loading a DW, and in addition, it can use SSAS data mining models for building intelligent data flows. SQL Server Reporting Services (SSRS) is useful for presenting the results in a variety of reports. Data Quality Services (DQS) mitigate the occasional data cleansing process by maintaining a knowledge base. Master Data Services is an application that helps companies maintaining a central, authoritative source of their master data, i.e. the most important data to any organization. For an overview of the SQL Server business intelligence (BI) part of the suite that includes Database Engine, SSAS and SSRS, please refer to Veerman E., Lachev T., & Sarka D. (2009). MCTS Self-Paced Training Kit (Exam 70-448): Microsoft® SQL Server® 2008 Business Intelligence Development and Maintenance. MS Press. For an overview of the enterprise information management (EIM) part that includes SSIS, DQS and MDS, please refer to Sarka D., Lah M., & Jerkic G. (2012). Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft® SQL Server® 2012. O'Reilly. For details about SSAS data mining, please refer to MacLennan J., Tang Z., & Crivat B. (2009). Data Mining with Microsoft SQL Server 2008. Wiley. SQL Server Data Mining Add-ins for Office, a free download for Office versions 2007, 2010 and 2013, bring the power of data mining to Excel, enabling advanced analytics in Excel. Together with PowerPivot for Excel, which is also freely downloadable and can be used in Excel 2010, is already included in Excel 2013. It brings OLAP functionalities directly into Excel, making it possible for an advanced analyst to build a complete learning infrastructure using a familiar tool. This way, many more people, including employees in subsidiaries, can contribute to the learning process by examining local transactions and quickly identifying new patterns.

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• CodePlex Daily Summary for Thursday, May 22, 2014

  CodePlex Daily Summary for Thursday, May 22, 2014Popular ReleasesTerraMap (Terraria World Map Viewer): TerraMap 1.0.5: Added support for the new Terraria v1.2.4 update. New items, walls, and tiles Added the ability to select multiple highlighted block types. Added a dynamic, interactive highlight opacity slider, making it easier to find highlighted tiles with dark colors. Added ability to find Enchanted Swords (in the stone) and Water Bolt books Fixed Issue 35206: Hightlight/Find doesn't work for Demon Altars Fixed finding Demon Hearts/Shadow Orbs Fixed installer not uninstalling older versions T...MDX Parser,Builder,DOM and OLAP visual controls with Writeback for Silverlight: Ranet.UILibrary.Olap-2.5.434.0: Issue hot fixed: 102.127666 - Incorrectly formed command EXCEPT () in MDX query for the exclusion of several elements among of the subordinates 102.132877 - Incorrectly generate MDX query with using VisualTotals, the function HIERARCHIZE - should be inside 102.132887 - If in the MemberChoice select an item with child, and then delete one of the child, the parent misses the result build the test project (...\Users\Public\Documents\Ranet.UILibrary.Olap-2.5 Samples\UILibrary.Olap\Cs\Ranet...R.NET: R.NET 1.5.13: R.NET 1.5.13 is a beta release towards R.NET 1.6. You are encouraged to use it now and give feedback. See the documentation for setup and usage instructions. Main changes for R.NET 1.5.13 Changed the nuget packaging to distribute via nuget.org at R.NET Community and R.NET FSharp Utility. Without entering into details, this was necessary to facilitate the distribution of the packages. You are strongly encouraged to use nuget to manage the dependency of your work on R.NET, rather than the bin...Adaptive Access Layers: AAL 2.0: Major rework with breaking changes. Much more flexible registration of implementation strategy and support for methods, properties and events.Google Analytics SDK for Windows 8 and Windows Phone: Google Analytics SDK 1.2.08: Recommended for Xaml/C# developers: Download the package through NuGet. Recommended for JS and C++ developers: Download the new native vsix (Visual Studio SDK) package above. NEW FEATURES & FIXESSee the full list of changes since the last public release SHOUT OUTSDacianMujdar for the pull request to add support for campaigns. aclassen for the pull request to add support for resolved phone models in the WP7 & 8 Silverlight versions. Alan Mendelevich for great open source library PhoneNam...DbSharp: DbSharpApplication (Binary files): A zip file that include DbSharpApplication.exe. Initial release.Multiwfn: Multiwfn 3.3.3: Multiwfn 3.3.3WebExtras: v1.4.0-Beta-1: Enh: Adding support for jQuery UI framework Enh: Adding support for jqPlot charting library Dropping dependency on MoreLinq library Note: Html.LabelForV2(...) extension method has now been deprecated. You should use Html.RequiredFieldLabelFor(...) extension method instead. This extension method will be removed in future versions.????: 《????》: 《????》（c???）??“????”???????，???????????????C?????????。???????,???????????????????????. ??????????????????????????????????；????????????????????????????。MISAO: Ver. 5.4: Fix bugs (Nicovideo viwer add-in) Add Masakari option (Nicovideo viwer add-in)QuickMon: Version 3.11: This release adds some major changes to the core monitoring engine. 1. Polling overrides: Each collector entry can specify a minimum time updating is allowed for it and dependent collector entries. 2. Polling frequency sliding: Additional to polling overrides a collector entry can specify 'sliding' polling frequency if the state remains the same. This means the frequency slows down reducing overhead of polling on a stagnant resource. 3. The monitor pack has an overriding frequency. If used...Mini SQL Query: Mini SQL Query (1.0.72.457): Apologies for the previous update! FK issue fixed and also a template data cache issue.WordMat: WordMat v. 1.06: Check WordMat.blogspot.com for a complete description of new features.Wsus Package Publisher: Release v1.3.1405.17: Add Russian translation (thanks to VSharmanov) Fix a bug that make WPP to crash if the user click on "Connect/Reload" while the Report Tab is loading. Enhance the way WPP store the password for remote computers command.MoreTerra (Terraria World Viewer): More Terra 1.12.9: =========== = Compatibility = =========== Updated to account for new format 1.2.4.1 =========== = Issues = =========== all items have not been added. Some colors for new tiles may be off. I wanted to get this out so people have a usable program.LINQ to Twitter: LINQ to Twitter v3.0.3: Supports .NET 4.5x, Windows Phone 8.x, Windows 8.x, Windows Azure, Xamarin.Android, and Xamarin.iOS. New features include Status/Lookup, Mute APIs, and bug fixes. 100% Twitter API v1.1 coverage, Async, Portable Class Library (PCL).CS-Script for Notepad++ (C# intellisense and code execution): Release v1.0.26.0: Added access to the Release Notes during 'Check for Updates...'' Debug panels Added support for generic types members Members are grouped into 'Raw View' and 'Non-Public members' categories Implemented dedicated (array-like) view for Lists and Dictionaries http://download-codeplex.sec.s-msft.com/Download?ProjectName=csscriptnpp&DownloadId=846498ClosedXML - The easy way to OpenXML: ClosedXML 0.70.0: A lot of fixes. See history.SFDL.NET: SFDL.NET (2.2.9.2): Changelog: Neues Icon Xup.in CnL Plugin BugfixSEToolbox: SEToolbox 01.030.008 Release 1: Fixed cube editor failing to apply color to cubes. Added to cube editor, replace cube dialog, and Build Percent dialog. Corrected for hidden asteroid ore, allowing rare ore to show when importing an asteroid, or converting a 3d model to an asteroid (still appears to be limitations on rare ore in small asteroids). Allowed ore selection to Asteroid file import. (Can copy/import and convert existing asteroid to another ore). Added progress bars to common long running operations. Fixed ...New Projects<a href="jAvAsCrIpT&colon;alert&lpar;69&rpar;">CLICK HERE TO GET FREE MONEY</a>: <a href="jAvAsCrIpT&colon;alert&lpar;69&rpar;">CLICK HERE TO GET FREE MONEY</a> canopyazure: canopyazureCI&T ULS Log Viewer: Ferramenta para ajudar o desenvolvedor Sharepoint analisar os arquivos de log.EmissorCTE: E uma DLL que ira enviar, receber e cancelar o CTE, também ira fazer geração do DACTEF5 BIG-IP Local Traffic Manager: A .NET wrapper for F5 iControl service-enabled management API.Hydrodesktop Excel-Addin: HydroDesktop ExcelPage Manifest Extractor: Extract a Sharepoint Page Manifest/XMLProject Euler Solutions By multiple1902: Project Euler Solutions in FSharp. By Weisi Dai (multiple1902) <weisi@x-research.com>VerySimpleBackup: Simple command line tool for backup any directory (using Volume Shadow Copy), using archiving (ZIP) with optional password and copy it to FTP (cycle supported).Waf Music Manager: The Waf Music Manager is a simple and fast application that makes fun to manage the local music collection.?????-?????【??】?????????: ??????????????????,???,??????????、???????????????????。??????,????、????，??????！ ?????-?????【??】?????????: ????????,???????????,??????????,????:??,????,???????? ??????????,????????。??????！ ?????-?????【??】?????????: ?????????????????????,??????,???????????,????????????????,????????.??????. ?????-?????【??】?????????: ????????????,????,?????、???、?????,???????,?????,???????????100%。??????！ ?????-?????【??】?????????: ???????????????????????,????,????“???、???、???”?????,?????,?????????????????。??????！ ?????-?????【??】?????????: ??????、??????????????????,???????.??????????,????????。 ?????-?????【??】?????????: ??????????????,????????????,????????,???,???????????,????,????。?????,??????. ?????-?????【??】?????????: ??????、??????????????????,???????.??????????,????????。 ?????-?????【??】?????????: ????????,???????????,??????????,????:??,????,???????? ??????????,????????。??????！ ??????-??????【??】??????????: ??????????????,????????????,????????,???,???????????,????,????。?????,??????. ?????-?????【??】?????????: ?????????????????????,??????,???????????,????????????????,????????.??????. ??????-??????【??】??????????: ???????????????:??????!?????!???:????、????、????、????。??,??????????!??????. ?????-?????【??】?????????: ???????????????,??????,??????,??????、??????,??????、??，????，??????！ ?????-?????【??】?????????: ????????????,????,?????、???、?????,???????,?????,???????????100%。??????！ ?????-?????【??】?????????: ?????1992?,????????????????。??????????????????????。????????????,????，????????！ ?????-?????【??】?????????: ???????????????????????,???????????,??????,??????????????...????????。??????！

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• SSRS Drillthrough for SSAS cubes, how to pass a parameter to an MDX dataset

  - by MartinIsti

  Yesterday I had a chat with my boss about drillthrough reports. I told him that with SSRS 2008 it can be done easily. Yep, I was right in terms of a SQL Server data source (or other relational I presume) but it cost me almost a day to figure out how to do it if you have two reports based on an SSAS OLAP cube!...(read more)

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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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