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  • MDX lekérdezések az Oracle OLAP-hoz

    - by Fekete Zoltán
    Az Oracle OpenWord-ön, 2009. október 12-én jelentette be az Oracle, hogy elkészült a Simba Technologies MDX eszköze az Oracle OLAP eléréséhez: Oracle and Simba Technologies Introduce MDX Provider for Oracle® OLAP. Az MDX Provider for Oracle® OLAP eszközzel közvetlenül az Excel felületrol lehet elérni az Oracle OLAP multidimenziós (multidimenzionális) motor által kezelt adatokat. Az MDX Provider for Oracle OLAP esköz lehetové teszi, hogy az Excel kereszttábla/pivott'bla (PivotTable) és PivotChart funkciókat közvetlenül használjuk az Oracle OLAP-ban tárolt adatvagyon ékszerek eléréséhez. :) - könnyen kihasználhatjuk az Oracle Database OLAP nagy sebességét a lekérdezési és a számítási oldalon is - támogatott táblázatkezelo és adatbázis-kezelo platformok: Microsoft Excel 2007 / 2003 és Oracle Database 11g Release 1 és Release 2. Az Oracle OLAP az Oracle Database EE-ben érheto el, annak opciójaként. Az Oracle a hírös és régebben csinos rekordokat is felmutató Oracle Express Server-bol fejlesztette ki az Oracle OLAP-ot, ami az adatbáziskezelo szerver részeként muködik. Technikai OLAP információ. Mire is jó az Oracle OLAP: - az üzleti szakemberek gondolkodásához közel álló elemzési lehetoséget nyújt - kifinomult analitikus lekérdezések elvégzése - hatalmas lekérdezési sebesség, apró futási idok bármilyen mennyiségu adatra - komoly számítási sebesség óriási adatmennyiségen is - gyors aggregációk - SQL-bol is kezelhetok és lekérdezhetok az OLAP adatok! - a cube-organised materialized views alkalmazásával a relációs részletes adatok mögé transzparens aggregációs szinteket helyezhetünk el könnyen Az MDX Provider for Oracle OLAP eszköz a következo helyen letöltheto és kipróbálható: http://www.simba.com/MDX-Provider-for-Oracle-OLAP.htm.

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  • OWB 11gR2 – OLAP and Simba

    - by David Allan
    Oracle Warehouse Builder was the first ETL product to provide a single integrated and complete environment for managing enterprise data warehouse solutions that also incorporate multi-dimensional schemas. The OWB 11gR2 release provides Oracle OLAP 11g deployment for multi-dimensional models (in addition to support for prior releases of OLAP). This means users can easily utilize Simba's MDX Provider for Oracle OLAP (see here for details and cost) which allows you to use the powerful and popular ad hoc query and analysis capabilities of Microsoft Excel PivotTables® and PivotCharts® with your Oracle OLAP business intelligence data. The extensions to the dimensional modeling capabilities have been built on established relational concepts, with the option to seamlessly move from a relational deployment model to a multi-dimensional model at the click of a button. This now means that ETL designers can logically model a complete data warehouse solution using one single tool and control the physical implementation of a logical model at deployment time. As a result data warehouse projects that need to provide a multi-dimensional model as part of the overall solution can be designed and implemented faster and more efficiently. Wizards for dimensions and cubes let you quickly build dimensional models and realize either relationally or as an Oracle database OLAP implementation, both 10g and 11g formats are supported based on a configuration option. The wizard provides a good first cut definition and the objects can be further refined in the editor. Both wizards let you choose the implementation, to deploy to OLAP in the database select MOLAP: multidimensional storage. You will then be asked what levels and attributes are to be defined, by default the wizard creates a level bases hierarchy, parent child hierarchies can be defined in the editor. Once the dimension or cube has been designed there are special mapping operators that make it easy to load data into the objects, below we load a constant value for the total level and the other levels from a source table.   Again when the cube is defined using the wizard we can edit the cube and define a number of analytic calculations by using the 'generate calculated measures' option on the measures panel. This lets you very easily add a lot of rich analytic measures to your cube. For example one of the measures is the percentage difference from a year ago which we can see in detail below. You can also add your own custom calculations to leverage the capabilities of the Oracle OLAP option, either by selecting existing template types such as moving averages to defining true custom expressions. The 11g OLAP option now supports percentage based summarization (the amount of data to precompute and store), this is available from the option 'cost based aggregation' in the cube's configuration. Ensure all measure-dimensions level based aggregation is switched off (on the cube-dimension panel) - previously level based aggregation was the only option. The 11g generated code now uses the new unified API as you see below, to generate the code, OWB needs a valid connection to a real schema, this was not needed before 11gR2 and is a new requirement since the OLAP API which OWB uses is not an offline one. Once all of the objects are deployed and the maps executed then we get to the fun stuff! How can we analyze the data? One option which is powerful and at many users' fingertips is using Microsoft Excel PivotTables® and PivotCharts®, which can be used with your Oracle OLAP business intelligence data by utilizing Simba's MDX Provider for Oracle OLAP (see Simba site for details of cost). I'll leave the exotic reporting illustrations to the experts (see Bud's demonstration here), but with Simba's MDX Provider for Oracle OLAP its very simple to easily access the analytics stored in the database (all built and loaded via the OWB 11gR2 release) and get the regular features of Excel at your fingertips such as using the conditional formatting features for example. That's a very quick run through of the OWB 11gR2 with respect to Oracle 11g OLAP integration and the reporting using Simba's MDX Provider for Oracle OLAP. Not a deep-dive in any way but a quick overview to illustrate the design capabilities and integrations possible.

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  • Building dynamic OLAP data marts on-the-fly

    - by DrJohn
    At the forthcoming SQLBits conference, I will be presenting a session on how to dynamically build an OLAP data mart on-the-fly. This blog entry is intended to clarify exactly what I mean by an OLAP data mart, why you may need to build them on-the-fly and finally outline the steps needed to build them dynamically. In subsequent blog entries, I will present exactly how to implement some of the techniques involved. What is an OLAP data mart? In data warehousing parlance, a data mart is a subset of the overall corporate data provided to business users to meet specific business needs. Of course, the term does not specify the technology involved, so I coined the term "OLAP data mart" to identify a subset of data which is delivered in the form of an OLAP cube which may be accompanied by the relational database upon which it was built. To clarify, the relational database is specifically create and loaded with the subset of data and then the OLAP cube is built and processed to make the data available to the end-users via standard OLAP client tools. Why build OLAP data marts? Market research companies sell data to their clients to make money. To gain competitive advantage, market research providers like to "add value" to their data by providing systems that enhance analytics, thereby allowing clients to make best use of the data. As such, OLAP cubes have become a standard way of delivering added value to clients. They can be built on-the-fly to hold specific data sets and meet particular needs and then hosted on a secure intranet site for remote access, or shipped to clients' own infrastructure for hosting. Even better, they support a wide range of different tools for analytical purposes, including the ever popular Microsoft Excel. Extension Attributes: The Challenge One of the key challenges in building multiple OLAP data marts based on the same 'template' is handling extension attributes. These are attributes that meet the client's specific reporting needs, but do not form part of the standard template. Now clearly, these extension attributes have to come into the system via additional files and ultimately be added to relational tables so they can end up in the OLAP cube. However, processing these files and filling dynamically altered tables with SSIS is a challenge as SSIS packages tend to break as soon as the database schema changes. There are two approaches to this: (1) dynamically build an SSIS package in memory to match the new database schema using C#, or (2) have the extension attributes provided as name/value pairs so the file's schema does not change and can easily be loaded using SSIS. The problem with the first approach is the complexity of writing an awful lot of complex C# code. The problem of the second approach is that name/value pairs are useless to an OLAP cube; so they have to be pivoted back into a proper relational table somewhere in the data load process WITHOUT breaking SSIS. How this can be done will be part of future blog entry. What is involved in building an OLAP data mart? There are a great many steps involved in building OLAP data marts on-the-fly. The key point is that all the steps must be automated to allow for the production of multiple OLAP data marts per day (i.e. many thousands, each with its own specific data set and attributes). Now most of these steps have a great deal in common with standard data warehouse practices. The key difference is that the databases are all built to order. The only permanent database is the metadata database (shown in orange) which holds all the metadata needed to build everything else (i.e. client orders, configuration information, connection strings, client specific requirements and attributes etc.). The staging database (shown in red) has a short life: it is built, populated and then ripped down as soon as the OLAP Data Mart has been populated. In the diagram below, the OLAP data mart comprises the two blue components: the Data Mart which is a relational database and the OLAP Cube which is an OLAP database implemented using Microsoft Analysis Services (SSAS). The client may receive just the OLAP cube or both components together depending on their reporting requirements.  So, in broad terms the steps required to fulfil a client order are as follows: Step 1: Prepare metadata Create a set of database names unique to the client's order Modify all package connection strings to be used by SSIS to point to new databases and file locations. Step 2: Create relational databases Create the staging and data mart relational databases using dynamic SQL and set the database recovery mode to SIMPLE as we do not need the overhead of logging anything Execute SQL scripts to build all database objects (tables, views, functions and stored procedures) in the two databases Step 3: Load staging database Use SSIS to load all data files into the staging database in a parallel operation Load extension files containing name/value pairs. These will provide client-specific attributes in the OLAP cube. Step 4: Load data mart relational database Load the data from staging into the data mart relational database, again in parallel where possible Allocate surrogate keys and use SSIS to perform surrogate key lookup during the load of fact tables Step 5: Load extension tables & attributes Pivot the extension attributes from their native name/value pairs into proper relational tables Add the extension attributes to the views used by OLAP cube Step 6: Deploy & Process OLAP cube Deploy the OLAP database directly to the server using a C# script task in SSIS Modify the connection string used by the OLAP cube to point to the data mart relational database Modify the cube structure to add the extension attributes to both the data source view and the relevant dimensions Remove any standard attributes that not required Process the OLAP cube Step 7: Backup and drop databases Drop staging database as it is no longer required Backup data mart relational and OLAP database and ship these to the client's infrastructure Drop data mart relational and OLAP database from the build server Mark order complete Start processing the next order, ad infinitum. So my future blog posts and my forthcoming session at the SQLBits conference will all focus on some of the more interesting aspects of building OLAP data marts on-the-fly such as handling the load of extension attributes and how to dynamically alter the structure of an OLAP cube using C#.

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  • An OLAP client!

    - by Davide Mauri
    While surfing CodePlex I’ve come across a very interesting tool for all BI Developers who misses a decent OLAP client where to write, run & test MDX queries http://ranetuilibraryolap.codeplex.com/ I’ve not tested it yet, but I’ll surely do this week and I’ll post my impressions ASAP. The first impression, just looking the CodePlex page, is that tool Rocks!!!!! Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • An OLAP client!

    - by Davide Mauri
    While surfing CodePlex I’ve come across a very interesting tool for all BI Developers who misses a decent OLAP client where to write, run & test MDX queries http://ranetuilibraryolap.codeplex.com/ I’ve not tested it yet, but I’ll surely do this week and I’ll post my impressions ASAP. The first impression, just looking the CodePlex page, is that tool Rocks!!!!! Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • In SQL Server Business Intelligence, why would I create a report model from an OLAP cube?

    - by ngm
    In Business Intelligence Developer Studio, I'm wondering why one would want to create a report model from an OLAP cube. As far as I understand it, OLAP cubes and report models are both business-oriented views of underlying structures (usually relational databases) that may not mean much to a business user. The cube is a multidimensional view in terms of dimensions and measures, and the report model is... well I'm not sure entirely -- is it a more business-oriented, but still essentially relational view? Anyway, in Report Builder I can connect directly to both an OLAP cube or a report model. So I don't see why, if I have an OLAP cube which already provides a business-oriented view of the data suitable for end-users, why I would then convert that to a report model and use that in Report Builder instead. I think I'm obviously missing some fundamental difference between report models and cubes -- any help appreciated!

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  • Writing OLAP SQL query

    - by user1859596
    I have a project I am working on that requires the following : create a normalized sample rdbms (5 tables) using Java I entered 1 million rows of data to each table run two OLTP and two OLAP queries on the normalized tables. Denormalized tables. run the same OLTP and OLAP queries on them and compare time. What does OLAP query mean? I've searched the internet and all that I can find is that I have to make a cube, and apply queries on it. How can I write an OLAP query on a RDBMS? I have a sample : tables normalized(orders,product,customer,branch,sales) sales : order_id,product_id,quantity product : product_id,name,description,price,sales_tax customer : customer_id,f_name,l_name,tel_no,addr,nic,city branch : branch_id,name,tel_no,addr,city orders : order_id,customer_id,order_date,branch_id I want to write an OLAP query on the above tables. I am using Oracle Express with SQL Developer.

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  • SSIS: Deploying OLAP cubes using C# script tasks and AMO

    - by DrJohn
    As part of the continuing series on Building dynamic OLAP data marts on-the-fly, this blog entry will focus on how to automate the deployment of OLAP cubes using SQL Server Integration Services (SSIS) and Analysis Services Management Objects (AMO). OLAP cube deployment is usually done using the Analysis Services Deployment Wizard. However, this option was dismissed for a variety of reasons. Firstly, invoking external processes from SSIS is fraught with problems as (a) it is not always possible to ensure SSIS waits for the external program to terminate; (b) we cannot log the outcome properly and (c) it is not always possible to control the server's configuration to ensure the executable works correctly. Another reason for rejecting the Deployment Wizard is that it requires the 'answers' to be written into four XML files. These XML files record the three things we need to change: the name of the server, the name of the OLAP database and the connection string to the data mart. Although it would be reasonably straight forward to change the content of the XML files programmatically, this adds another set of complication and level of obscurity to the overall process. When I first investigated the possibility of using C# to deploy a cube, I was surprised to find that there are no other blog entries about the topic. I can only assume everyone else is happy with the Deployment Wizard! SSIS "forgets" assembly references If you build your script task from scratch, you will have to remember how to overcome one of the major annoyances of working with SSIS script tasks: the forgetful nature of SSIS when it comes to assembly references. Basically, you can go through the process of adding an assembly reference using the Add Reference dialog, but when you close the script window, SSIS "forgets" the assembly reference so the script will not compile. After repeating the operation several times, you will find that SSIS only remembers the assembly reference when you specifically press the Save All icon in the script window. This problem is not unique to the AMO assembly and has certainly been a "feature" since SQL Server 2005, so I am not amazed it is still present in SQL Server 2008 R2! Sample Package So let's take a look at the sample SSIS package I have provided which can be downloaded from here: DeployOlapCubeExample.zip  Below is a screenshot after a successful run. Connection Managers The package has three connection managers: AsDatabaseDefinitionFile is a file connection manager pointing to the .asdatabase file you wish to deploy. Note that this can be found in the bin directory of you OLAP database project once you have clicked the "Build" button in Visual Studio TargetOlapServerCS is an Analysis Services connection manager which identifies both the deployment server and the target database name. SourceDataMart is an OLEDB connection manager pointing to the data mart which is to act as the source of data for your cube. This will be used to replace the connection string found in your .asdatabase file Once you have configured the connection managers, the sample should run and deploy your OLAP database in a few seconds. Of course, in a production environment, these connection managers would be associated with package configurations or set at runtime. When you run the sample, you should see that the script logs its activity to the output screen (see screenshot above). If you configure logging for the package, then these messages will also appear in your SSIS logging. Sample Code Walkthrough Next let's walk through the code. The first step is to parse the connection string provided by the TargetOlapServerCS connection manager and obtain the name of both the target OLAP server and also the name of the OLAP database. Note that the target database does not have to exist to be referenced in an AS connection manager, so I am using this as a convenient way to define both properties. We now connect to the server and check for the existence of the OLAP database. If it exists, we drop the database so we can re-deploy. svr.Connect(olapServerName); if (svr.Connected) { // Drop the OLAP database if it already exists Database db = svr.Databases.FindByName(olapDatabaseName); if (db != null) { db.Drop(); } // rest of script } Next we start building the XMLA command that will actually perform the deployment. Basically this is a small chuck of XML which we need to wrap around the large .asdatabase file generated by the Visual Studio build process. // Start generating the main part of the XMLA command XmlDocument xmlaCommand = new XmlDocument(); xmlaCommand.LoadXml(string.Format("<Batch Transaction='false' xmlns='http://schemas.microsoft.com/analysisservices/2003/engine'><Alter AllowCreate='true' ObjectExpansion='ExpandFull'><Object><DatabaseID>{0}</DatabaseID></Object><ObjectDefinition/></Alter></Batch>", olapDatabaseName));  Next we need to merge two XML files which we can do by simply using setting the InnerXml property of the ObjectDefinition node as follows: // load OLAP Database definition from .asdatabase file identified by connection manager XmlDocument olapCubeDef = new XmlDocument(); olapCubeDef.Load(Dts.Connections["AsDatabaseDefinitionFile"].ConnectionString); // merge the two XML files by obtain a reference to the ObjectDefinition node oaRootNode.InnerXml = olapCubeDef.InnerXml;   One hurdle I had to overcome was removing detritus from the .asdabase file left by the Visual Studio build. Through an iterative process, I found I needed to remove several nodes as they caused the deployment to fail. The XMLA error message read "Cannot set read-only node: CreatedTimestamp" or similar. In comparing the XMLA generated with by the Deployment Wizard with that generated by my code, these read-only nodes were missing, so clearly I just needed to strip them out. This was easily achieved using XPath to find the relevant XML nodes, of which I show one example below: foreach (XmlNode node in rootNode.SelectNodes("//ns1:CreatedTimestamp", nsManager)) { node.ParentNode.RemoveChild(node); } Now we need to change the database name in both the ID and Name nodes using code such as: XmlNode databaseID = xmlaCommand.SelectSingleNode("//ns1:Database/ns1:ID", nsManager); if (databaseID != null) databaseID.InnerText = olapDatabaseName; Finally we need to change the connection string to point at the relevant data mart. Again this is easily achieved using XPath to search for the relevant nodes and then replace the content of the node with the new name or connection string. XmlNode connectionStringNode = xmlaCommand.SelectSingleNode("//ns1:DataSources/ns1:DataSource/ns1:ConnectionString", nsManager); if (connectionStringNode != null) { connectionStringNode.InnerText = Dts.Connections["SourceDataMart"].ConnectionString; } Finally we need to perform the deployment using the Execute XMLA command and check the returned XmlaResultCollection for errors before setting the Dts.TaskResult. XmlaResultCollection oResults = svr.Execute(xmlaCommand.InnerXml);  // check for errors during deployment foreach (Microsoft.AnalysisServices.XmlaResult oResult in oResults) { foreach (Microsoft.AnalysisServices.XmlaMessage oMessage in oResult.Messages) { if ((oMessage.GetType().Name == "XmlaError")) { FireError(oMessage.Description); HadError = true; } } } If you are not familiar with XML programming, all this may all seem a bit daunting, but perceiver as the sample code is pretty short. If you would like the script to process the OLAP database, simply uncomment the lines in the vicinity of Process method. Of course, you can extend the script to perform your own custom processing and to even synchronize the database to a front-end server. Personally, I like to keep the deployment and processing separate as the code can become overly complex for support staff.If you want to know more, come see my session at the forthcoming SQLBits conference.

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  • SPARC T4-4 Delivers World Record Performance on Oracle OLAP Perf Version 2 Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered world record performance with subsecond response time on the Oracle OLAP Perf Version 2 benchmark using Oracle Database 11g Release 2 running on Oracle Solaris 11. The SPARC T4-4 server achieved throughput of 430,000 cube-queries/hour with an average response time of 0.85 seconds and the median response time of 0.43 seconds. This was achieved by using only 60% of the available CPU resources leaving plenty of headroom for future growth. The SPARC T4-4 server operated on an Oracle OLAP cube with a 4 billion row fact table of sales data containing 4 dimensions. This represents as many as 90 quintillion aggregate rows (90 followed by 18 zeros). Performance Landscape Oracle OLAP Perf Version 2 Benchmark 4 Billion Fact Table Rows System Queries/hour Users* Response Time (sec) Average Median SPARC T4-4 430,000 7,300 0.85 0.43 * Users - the supported number of users with a given think time of 60 seconds Configuration Summary and Results Hardware Configuration: SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 1 TB memory Data Storage 1 x Sun Fire X4275 (using COMSTAR) 2 x Sun Storage F5100 Flash Array (each with 80 FMODs) Redo Storage 1 x Sun Fire X4275 (using COMSTAR with 8 HDD) Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 (11.2.0.3) with Oracle OLAP option Benchmark Description The Oracle OLAP Perf Version 2 benchmark is a workload designed to demonstrate and stress the Oracle OLAP product's core features of fast query, fast update, and rich calculations on a multi-dimensional model to support enhanced Data Warehousing. The bulk of the benchmark entails running a number of concurrent users, each issuing typical multidimensional queries against an Oracle OLAP cube consisting of a number of years of sales data with fully pre-computed aggregations. The cube has four dimensions: time, product, customer, and channel. Each query user issues approximately 150 different queries. One query chain may ask for total sales in a particular region (e.g South America) for a particular time period (e.g. Q4 of 2010) followed by additional queries which drill down into sales for individual countries (e.g. Chile, Peru, etc.) with further queries drilling down into individual stores, etc. Another query chain may ask for yearly comparisons of total sales for some product category (e.g. major household appliances) and then issue further queries drilling down into particular products (e.g. refrigerators, stoves. etc.), particular regions, particular customers, etc. Results from version 2 of the benchmark are not comparable with version 1. The primary difference is the type of queries along with the query mix. Key Points and Best Practices Since typical BI users are often likely to issue similar queries, with different constants in the where clauses, setting the init.ora prameter "cursor_sharing" to "force" will provide for additional query throughput and a larger number of potential users. Except for this setting, together with making full use of available memory, out of the box performance for the OLAP Perf workload should provide results similar to what is reported here. For a given number of query users with zero think time, the main measured metrics are the average query response time, the median query response time, and the query throughput. A derived metric is the maximum number of users the system can support achieving the measured response time assuming some non-zero think time. The calculation of the maximum number of users follows from the well-known response-time law N = (rt + tt) * tp where rt is the average response time, tt is the think time and tp is the measured throughput. Setting tt to 60 seconds, rt to 0.85 seconds and tp to 119.44 queries/sec (430,000 queries/hour), the above formula shows that the T4-4 server will support 7,300 concurrent users with a think time of 60 seconds and an average response time of 0.85 seconds. For more information see chapter 3 from the book "Quantitative System Performance" cited below. -- See Also Quantitative System Performance Computer System Analysis Using Queueing Network Models Edward D. Lazowska, John Zahorjan, G. Scott Graham, Kenneth C. Sevcik external local Oracle Database 11g – Oracle OLAP oracle.com OTN SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 11/2/2012.

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  • Reporting tool for OLAP, *not* OLTP!

    - by Stefan Moser
    I'm looking for a control that I can put on top of an already existing OLAP star schema to allow the user to define their own "queries" and generate reports. Right now I have some predefined reports built on top of the cubes, but I'd like to allow the user to define their own criteria based on the cubes that I've created. I've found lots of products that will allow you to treat a transactional table like an OLAP cube, but nothing specifically for pre-existing cubes. EDIT: Let me be clear, I know there are countless reporting tools out there that claim to report on OLAP cubes. The problem is they all assume they are looking at transactional data and try to create their own cubes. I have tables that contain tens, if not hundreds of millions of records. Most tools crash when handling this much data, the others just run incredible slowly. I don't want a tool that is targeting the business people. I want a tool that understands what a star and snowflake schema is. I want to be able to tell it what the fact tables are and what the dimension tables are, and then creates a UI on top of them. This is an easier problem to solve for the tool vendor because I am spoon feeding them the cubes. I want to rely on the fact that cubes are a standardized pattern and I want a tool that takes advantage of this fact. I want a tool that targets developers and starts with the assumption that I actually know how to manage my data, it just needs to build pretty reports for me and not crumble under the weight of my data.

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  • OLAP Web Visualization and Reporting Recommendations

    - by Gok Demir
    I am preparing an offer for a customer. They proide weekly data to different organizations. There is huge amount data suits OLAP that needed to be visualized with charts and pivot tables on web and custom reports will be built by non-it persons (an easy gui). They will enter a date range, location which data columns to be included and generate report and optionally export the data to Excel. They currently prepare reports with MS Excel with Pivot Tables and but they need a better online tool now to show data to their customers. Tables are huge and need of drill-down functionality. My current knowledge Spring, Flex, MySql, Linux. I have some knowledge of PostgreSQL and MSSQL and Windows. What is the easiest way of doing this project. Do you think that SSRP (haven't tried yet) and ASP.NET better suits for this kind of job. Actually I prefer open source solutions. Flex have OLAP Data Grid control which do aggregation on client side. JasperServer seems promising but it seems I need enterprise version (multiple organizations and ad hoc queries). What about Modrian + Flex + PostgreSQL solution? Any previous experience will be appreciated. Yes I am confused with options.

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  • New release of &quot;OLAP PivotTable Extensions&quot;

    - by Luca Zavarella
    For those who are not familiar with this add-in, the OLAP PivotTable Extensions add features of interest to Excel 2007 or 2010 PivotTables pointing to an OLAP cube in Analysis Services. One of these features I like very much, is to know the MDX query code associated with the pivot used at that time in Excel: You can find all the details here: http://olappivottableextend.codeplex.com/ It was recently released a new version of the add-in (version 0.7.4), which does not introduce any new features, but fixes a significant bug: Release 0.7.4 now properly handles languages but introduces no new features. International users who run a different Windows language than their Excel UI language may be receiving an error message when they double click a cell and perform drillthrough which reads: "XML for Analysis parser: The LocaleIdentifier property is not overwritable and cannot be assigned a new value". This error was caused by OLAP PivotTable Extensions in some situations, but release 0.7.4 fixes this problem. Enjoy!

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  • MS Analysis Services OLAP API for Python

    - by Kaloyan Todorov
    I am looking for a way to connect to a MS Analysis Services OLAP cube, run MDX queries, and pull the results into Python. In other words, exactly what Excel does. Is there a solution in Python that would let me do that? Someone with a similar question going pointed to Django's ORM. As much as I like the framework, this is not what I am looking for. I am also not looking for a way to pull rows and aggregate them -- that's what Analysis Services is for in the first place. Ideas? Thanks.

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  • SSAS OLAP MDX and relationships

    - by Sonic Soul
    I new to OLAP, and still not sure how to create a relationship between 2 or more entities. I am basing my cube on views. For simplicity sake let's call them like this: viewParent (ParentID PK) viewChild (ChildID PK, ParentID FK) these views have more fields, but they're not important for this question. in my data source, i defined a relationship between viewParent and viewChild using ParentID for the link. As for measures, i was forced to create separate measures for Parent and Child. in my MDX query however, the relationship does not seem to be enforced. If i select record count for parent, child, and add some filters for the parent, the child count is not reflecting it.. SELECT { [Measures].[ParentCount],[Measures].[ChildCount] } ON COLUMNS FROM [Cube] WHERE { ( {[Time].[Month].&[2011-06-01T00:00:00]} ,{[SomeDimension].&[Foo]} ) } the selected ParentCount is correct, but ChildCount is not affected by any of the filters (because they are parent filters). However, since i defined a relationship, how can i take advantage of that to filter children by parent filter?

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  • How to create offline OLAP cube in C#?

    - by jimmyjoe
    I have a problem with creating an offline OLAP cube from C# using following code: using (var connection = new OleDbConnection()) { connection.ConnectionString = "Provider=MSOLAP; Initial Catalog=[OCWCube]; Data Source=C:\\temp\\test.cub; CreateCube=CREATE CUBE [OCWCube] ( DIMENSION [NAME], LEVEL [Wszystkie] TYPE ALL, LEVEL [NAME], MEASURE [Liczba DESCRIPTIO] FUNCTION COUNT ); InsertInto=INSERT INTO OCWCube([Liczba DESCRIPTIO], [NAME].[NAME]) OPTIONS ATTEMPT_ANALYSIS SELECT Planners.DESCRIPTIO, Planners.NAME FROM Planners Planners; Source_DSN=\"CollatingSequence=ASCII;DefaultDir=c:\\temp;Deleted=1;Driver={Microsoft dBase Driver (*.dbf)};DriverId=277;FIL=dBase IV;MaxBufferSize=2048;MaxScanRows=8;PageTimeout=600;SafeTransactions=0;Statistics=0;Threads=3;UserCommitSync=Yes;\";Mode=Write;UseExistingFile=True"; try { connection.Open(); } catch (OleDbException e) { Console.WriteLine(e); } } I keep on getting the following exception: "Multiple-step operation generated errors. Check each OLE database status value. No action was taken." I took the connection string literally from OQY file generated by Excel. I had to add "Mode=Write" section, otherwise I was getting another exception ("file may be in use"). What is wrong with the connection string? How to diagnose the error? Somebody please guide me...

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  • Ad-hoc reporting similar to Microstrategy/Pentaho - is OLAP really the only choice (is OLAP even sufficient)?

    - by TheBeefMightBeTough
    So I'm getting ready to develop an API in Java that will provide all dimensions, metrics, hierarchies, etc to a user such that they can pick and choose what they want (say, e.g., dimensions of Location (a store) and Weekly, and the metric Product Sales $), provide their choices to the api, and have it spit out an object that contains the answer to their question (the object would probably be a set of cells). I don't even believe there will be much drill up/down. The data warehouse the APIwill interface with is in a standard form (FACT tables, dimensions, star schema format). My question is, is an OLAP framework such as Mondrian the only way to achieve something akin to ad-hoc reporting? I can envisage a really large Cube (or VirtualCube) that contains most of the dimensions and metrics the user could ever want, which would give the illusion of ad-hoc reporting. The problem is that there is a ton of setup to do (so much XML) to get the framework to work with the data. Further it requires specific knowledge, such as MDX, and even moreso learning the framework peculiars (Mondrian API). Finally, I am not positive it will scale much better than simply making queries against a SQL database. OLAP to me feels like very old technology. Is performance really an issue anymore? The alternative I can think of would be dynamic SQL. If the existing tables in the data warehouse conform to a naming scheme (FACT_, DIM_, etc), or if a very simple config file/ database table containing config information existed that stored which tables are fact tables, which are dimensions, and what metrics are available, then couldn't the api read from that and assembly the appropriate sql query? Would this necessarily be harder than learning MDX, Mondrian (or another OLAP framework), and creating all the cubes? In general, I feel that OLAP is at the same time too powerful (supports drill up/down, complex functions) and outdated and am reluctant to base my architecture on it. However, I am unsure if the alternative(s), such as rolling my own ad-hoc reporting framework using dynamic SQL would remove any complexity while still fulfilling requirements, both functional and non-functional (e.g., scalability; some FACT tables have many millions of rows). I also wonder about other techniques (e.g., hive). Has anyone here tried to do ad-hoc reporting? Any advice? I expect this project to take a pretty long time (3 months min, but probably longer), so I just do not want to commit to an architecture without being absolutely sure of its pros and cons. Thanks so much.

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  • Cube project doesn't work because of permissions

    - by sms
    I'm doing "Multidimensional Project" with MS SQL Server 2012 (Server Data Tools - Visual Studio 2010 Shell). I can't run (debug) it. If the data source's impersonation information is set to "use the service account", this error occures: Error 2 Internal error: The operation terminated unsuccessfully. 0 0 Error 3 OLE DB error: OLE DB or ODBC error: Login failed for user 'NT Service\MSSQLServerOLAPService'.; 28000. 0 0 Error 4 Errors in the high-level relational engine. A connection could not be made to the data source with the DataSourceID of 'Data Warehouse', Name of 'Data Warehouse'. 0 0 Error 5 Errors in the OLAP storage engine: An error occurred while the dimension, with the ID of 'Items', Name of 'Items' was being processed. 0 0 Error 6 Errors in the OLAP storage engine: An error occurred while the 'Id' attribute of the 'Items' dimension from the 'Warehouse_MultidimensionalProject_Cube' database was being processed. 0 0 Error 7 Server: The current operation was cancelled because another operation in the transaction failed. 0 0 I guessed that this account has no premissions but (1) I coudn't even add this account (it seems that it doesn't exist) and (2) how is that even possible for it to not have built-it poremissions? When I'm setting impersonation to "use the credentials of current user" (which is the owner of the data source, btw.), another error occures: Error 2 Internal error: The operation terminated unsuccessfully. 0 0 Error 3 The datasource, 'Data Warehouse', contains an ImpersonationMode that is not supported for processing operations. 0 0 Error 4 Errors in the high-level relational engine. A connection could not be made to the data source with the DataSourceID of 'Data Warehouse', Name of 'Data Warehouse'. 0 0 Error 5 Errors in the OLAP storage engine: An error occurred while the dimension, with the ID of 'Items', Name of 'Items' was being processed. 0 0 Error 6 Errors in the OLAP storage engine: An error occurred while the 'Id' attribute of the 'Items' dimension from the 'Warehouse_MultidimensionalProject_Cube' database was being processed. 0 0 Error 7 Server: The current operation was cancelled because another operation in the transaction failed. 0 0 Any help?

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  • SSAS 2008 backup/restore fails with a GetOverlappedResult 'Insufficient system resources exist to co

    - by Anant Aneja
    Hi, On my SSAS 2008 instance if a backup/restore of any database is made to/from a UNC path I get an error : The following system error occurred from a call to GetOverlappedResult for Physical file: '\server\share\OLAPDB.abf', Logical file: '' : Insufficient system resources exist to complete the requested service. . Server: The operation has been cancelled. (Microsoft.AnalysisServices) Creating/copying/moving a file on the share of any size on the share using explorer or the command prompt works. The most useful link I could find is : http://www.tech-archive.net/Archive/Development/microsoft.public.win32.programmer.kernel/2004-07/0475.html Can anyone shed more light on what could be causing this error ? (I've posted the same question on the SSAS forums - just a heads up)

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  • Collaborate10 &ndash; THEconference

    - by jean-pierre.dijcks
    After spending a few days in Mandalay Bay's THEHotel, I guess I now call everything THE... Seriously, they even tag their toilet paper with THEtp... I guess the brand builders in Vegas thought that once you are on to something you keep on doing it, and granted it is a nice hotel with nice rooms. THEanalytics Most of my collab10 experience was in a room called Reef C, where the BIWA bootcamp was held. Two solid days of BI, Warehousing and Analytics organized by the BIWA SIG at IOUG. Didn't get to see all sessions, but what struck me was the high interest in Analytics. Marty Gubar's OLAP session was full and he did some very nice things with the OLAP option. The cool bit was that he actually gets all the advanced calculations in OLAP to show up in OBI EE without any effort. It was nice to see that the idea from OWB where you generate an RPD is now also in AWM. I think it makes life so much simpler to generate these RPD's from your data model. Even if the end RPD needs some tweaking, it is all a lot less effort to get something going. You can see this stuff for yourself in this demo (click here). OBI EE uses just SQL to get to the calculations, and so, if you prefer APEX, you can build you application there and get the same nice calculations in an APEX application. Marty also showed the Simba MDX driver used with Excel. I guess we should call that THEcoolone... and it is very slick and wonderfully useful for all of you who actually know Excel. The nice thing is that you leverage pure Excel for all operations (no plug-ins). That means no new tools to learn, no new controls, all just pure Excel. THEdatabasemachine Got some very good questions in my "what makes Exadata fast" session and overall, the interest in Exadata is overwhelming. One of the things that I did try to do in my session is to get people to think in new patterns rather than in patterns based on Oracle 9i running on some random hardware configuration. We talked a little bit about the often over-indexing and how everyone has to unlearn all of that on Exadata. The main thing however is that everyone needs to get used to the shear size of some of the components in a Database machine V2. 5TB of flash cache is a lot of very fast data storage, half a TB of memory gets quite interesting as well. So what I did there was really focus on some of the content in these earlier posts on Upward ILM and In-Memory processing. In short, I do believe the these newer media point out a trend. In-memory and other fast media will get cheaper and will see more use. Some of that we do automatically by adding new functionality, but in some cases I think the end user of the system needs to start thinking about how to leverage all this new hardware. I think most people got very excited about these new capabilities and opportunities. THEcoolkids One of the cool things about the BIWA track was the hand-on track. Very cool to see big crowds for both OLAP and OWB hands-on. Also quite nice to see that the folks at RittmanMead spent so much time on preparing for that session. While all of them put down cool stuff, none was more cool that seeing Data Mining on an Apple iPAD... it all just looks great on an iPAD! Very disappointing to see that Mark Rittman still wasn't showing OWB on his iPAD ;-) THEend All in all this was a great set of sessions in the BIWA track. Lots of value to our guests (we hope) and we hope they all come again next year!

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  • Oracle Technológia Fórum rendezvény, 2010. május 5. szerda

    - by Fekete Zoltán
    Jövo hét szerdán Oracle Technology Fórum napot tartunk, ahol az adatbázis-kezelési és a fejlesztoi szekciókban hallgathatók meg eloadások illetve kaphatók válaszok a kérdésekre. Jelentkezés a rendezvényre. Az adatbázis szekcióban fogok beszélni a Sun Oracle Database Machine / Exadata megoldások technikai gyöngyszemeirol mind a tranzakciós (OLTP) mind az adattárházas (DW) és adatbázis konszolidáció oldaláról. Emellett kiemelem majd az Oracle Data Mining (adatbányászat) és OLAP újdonságait, érdekességeit. Megemlítem majd az Oracle's Data Warehouse Reference Architecture alkalmazási lehetoségeit is.

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  • BI fejlesztés: mi a megoldás, ha nem látod az aggregált adatokat a pivot táblában?

    - by Fekete Zoltán
    Mindenkivel elofordulhat, hogy BI elemzések, jelentések készítése közben ráébred: a táblázatban látja az adatokat, de a pivot táblában nem lát adatot. Üres... Mi a megoldás? A következo: A BI Administration eszközben be kell állítani az aggregációs szabályt a megfelelo adatpontra, s akkor a hierarchia magasabb szintjén is látszanak majd az adatok. Persze összegzo táblák, nézetek bekapcsolásával, vagy Oracle OLAP illetve Essbase kockák alkalmazásával is kezelhetjük az aggregálásokat.

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  • Putting our OLTP and OLAP services on the same cluster

    - by Dynamo
    We're currently in a bit of a debate about what to do with our scattered SQL environment. We are setting up a cluster for our data warehouses for sure and are now in the process of deciding if our OLTP databases should go on the same one. The cluster will be active/active with database services running on one node and reporting and analytical services on the other node. From a technical standpoint I don't see an issue here. With the services being run on different nodes they shouldn't compete too heavily for resources. The only physical resource that may be an issue would be the shared disk space. Our environment is also quite small. Our biggest OLAP database at the moment is only about 40GB and our OLTP are all under 10GB. I see a potential political issue here as different groups are involved but I'm just strictly wondering if there would be any major technical issues that could arise from this setup.

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  • Now Shipping! NetAdvantage for .NET 2010 Volume 3!

    The new NetAdvantage Ultimate includes all four Line of Business user interface control sets for ASP .NET, Windows Forms, WPF and Silverlight plus two advanced Data Visualization UI control sets for WPF and Silverlight. With six NetAdvantage products in one robust package, Infragistics® gives you hundreds of controls and infinite development possibilities. Unified XAML Product Strategy-Share Code, Get More Controls In the 10.3 release, Infragistics continues to deliver code parity between the XAML platforms, WPF and Silverlight. In the line of business toolsets, Infragistics introduces the new xamSchedule™, full-featured, Outlook® 2010-style schedule controls, and the new xamDataTree™, a data bound tree view that comfortably handles tens of thousands of tree nodes. Mimicking our Silverlight Drag and Drop Framework, the WPF Drag and Drop Framework CTP empowers you to add your own rich touches to your applications. Track Users' Behaviors New to all NetAdvantage Silverlight controls is the Infragistics Analytics Framework (IGAF), which empowers you to track user behavior in RIAs running on Silverlight 4. Building on the Microsoft® Silverlight Analytics Framework, with IGAF you can analyze the user's behaviors to ensure the experience you want to deliver. NetAdvantage for Windows Forms--New Office® 2010 Ribbon and Application Menu 2010 Create new experiences with Windows Forms. Now with Office 2010 styling, NetAdvantage for Windows Forms has new features such as Microsoft® Office 2010 ribbon and enhanced Infragistics.Excel to export the contents of the high performance WinGrid™ into Microsoft Excel® 2010. The new Windows Message Support enables Infragistics standalone editor controls to process numerous Windows® OS messages, allowing them to respond just like native controls to changes in the Windows environment. Create Faster Web 2.0 Experiences with NetAdvantage for ASP .NET Infragistics continues to push the envelope to deliver the fastest ASP .NET WebForms controls available on the market. Our lightning fast ASP .NET grids are now enhanced with XPS/PDF Exporting and Summary Rows. This release also includes support for jQuery Templating (as a CTP) within our WebDataGrid™ and WebDataTree™ controls allowing you to quickly cut down overall page size. Deliver Business Intelligence with Power, Flexibility and the Office 2010 Experience NetAdvantage for WPF Data Visualization and NetAdvantage for Silverlight Data Visualization help you deliver flexible, powerful and usable end user experiences in Business Intelligence applications. Both suites include the Pivot Grid that delivers the full power of online analytical processing (OLAP) to present multi-dimensional data, sliced and diced in cross-tabulated form for end users to drill down into, interact with and easily extract meaning from the data. Mapping Made Easy 10.3 marks the official release of the WPF Data Visualization xamMap™ control to map anything and everything from geographic to geo-spacial mapping data. Map layers allow you to add successive levels of detail, navigational panes for panning in all directions, color swatch panes that facilitate value scales like Choropleth shading, and scale panes allowing users to zoom-in and out. Both toolsets introduce the first of many relationship maps! With the xamOrgChart™ CTP you can map out organizational charts of up to 50K employees, competitive brackets (think World Cup) and any other relational, organizational map your application needs. http://www.infragistics.com span.fullpost {display:none;}

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