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  • Meet SQLBI at PASS Summit 2012 #sqlpass

    - by Marco Russo (SQLBI)
    Next week I and Alberto Ferrari will be in Seattle at PASS Summit 2012. You can meet us at our sessions, at a book signing and hopefully watching some other session during the conference. Here are our appointments: Thursday, November 08, 2012, 10:15 AM - 11:45 AM – Alberto Ferrari – Room 606-607 Querying and Optimizing DAX (BIA-321-S) Do you want to learn how to write DAX queries and how to optimize them? Don’t miss this session! Thursday, November 08, 2012, 12:00 PM - 12:30 PM – Bookstore Book signing event at the Bookstore corner with Alberto Ferrari, Marco Russo and Chris Webb Visit the bookstore and sign your copy of our Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model book. Thursday, November 08, 2012, 1:30 PM - 2:45 PM – Marco Russo – Room 611 Near Real-Time Analytics with xVelocity (without DirectQuery) (BIA-312) What’s the latency you can tolerate for your data? Discover what is the limit in Tabular without using DirectQuery and learn how to optimize your data model and your queries for a near real-time analytical system. Not a trivial task, but more affordable than you might think. Friday, November 09, 2012, 9:45 AM - 11:00 AM Parent-Child Hierarchies in Tabular (BIA-301) Multidimensional has a more advanced support for hierarchies than Tabular, but in reality you can do almost the same things by using data modeling, DAX functions and BIDS Helper!  Friday, November 09, 2012, 1:00 PM - 2:15 PM – Marco Russo – Room 612 Inside DAX Query Plans (BIA-403) Discover the query plan for your DAX query and learn how to read it and how to optimize a DAX query by using these information. If you meet us at the conference, stop us and say hello: it’s always nice to know our readers!

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  • New Whitepaper from SQLBI: Vertipaq vs ColumnStore

    - by AlbertoFerrari
    At the end of June 2012, I was in Amsterdam to present some sessions at Teched Europe 2012 and, while preparing the material for the demos (yes, the best demos are the ones I prepare at the last minute), I decided to make a comparison between the two implementations of xVelocity of SQL 2012, one is the VertiPaq engine in SSAS Tabular and the other one is the ColumnStore index in SQL Server. After some trials, I decided that ColumnStore was a clear loser, because I was not able to see a real improvement...(read more)

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  • Columnstore Case Study #2: Columnstore faster than SSAS Cube at DevCon Security

    - by aspiringgeek
    Preamble This is the second in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in my big deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. See also Columnstore Case Study #1: MSIT SONAR Aggregations Why Columnstore? As stated previously, If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. The Customer DevCon Security provides home & business security services & has been in business for 135 years. I met DevCon personnel while speaking to the Utah County SQL User Group on 20 February 2012. (Thanks to TJ Belt (b|@tjaybelt) & Ben Miller (b|@DBADuck) for the invitation which serendipitously coincided with the height of ski season.) The App: DevCon Security Reporting: Optimized & Ad Hoc Queries DevCon users interrogate a SQL Server 2012 Analysis Services cube via SSRS. In addition, the SQL Server 2012 relational back end is the target of ad hoc queries; this DW back end is refreshed nightly during a brief maintenance window via conventional table partition switching. SSRS, SSAS, & MDX Conventional relational structures were unable to provide adequate performance for user interaction for the SSRS reports. An SSAS solution was implemented requiring personnel to ramp up technically, including learning enough MDX to satisfy requirements. Ad Hoc Queries Even though the fact table is relatively small—only 22 million rows & 33GB—the table was a typical DW table in terms of its width: 137 columns, any of which could be the target of ad hoc interrogation. As is common in DW reporting scenarios such as this, it is often nearly to optimize for such queries using conventional indexing. DevCon DBAs & developers attended PASS 2012 & were introduced to the marvels of columnstore in a session presented by Klaus Aschenbrenner (b|@Aschenbrenner) The Details Classic vs. columnstore before-&-after metrics are impressive. Scenario   Conventional Structures   Columnstore   Δ SSRS via SSAS 10 - 12 seconds 1 second >10x Ad Hoc 5-7 minutes (300 - 420 seconds) 1 - 2 seconds >100x Here are two charts characterizing this data graphically.  The first is a linear representation of Report Duration (in seconds) for Conventional Structures vs. Columnstore Indexes.  As is so often the case when we chart such significant deltas, the linear scale doesn’t expose some the dramatically improved values corresponding to the columnstore metrics.  Just to make it fair here’s the same data represented logarithmically; yet even here the values corresponding to 1 –2 seconds aren’t visible.  The Wins Performance: Even prior to columnstore implementation, at 10 - 12 seconds canned report performance against the SSAS cube was tolerable. Yet the 1 second performance afterward is clearly better. As significant as that is, imagine the user experience re: ad hoc interrogation. The difference between several minutes vs. one or two seconds is a game changer, literally changing the way users interact with their data—no mental context switching, no wondering when the results will appear, no preoccupation with the spinning mind-numbing hurry-up-&-wait indicators.  As we’ve commonly found elsewhere, columnstore indexes here provided performance improvements of one, two, or more orders of magnitude. Simplified Infrastructure: Because in this case a nonclustered columnstore index on a conventional DW table was faster than an Analysis Services cube, the entire SSAS infrastructure was rendered superfluous & was retired. PASS Rocks: Once again, the value of attending PASS is proven out. The trip to Charlotte combined with eager & enquiring minds let directly to this success story. Find out more about the next PASS Summit here, hosted this year in Seattle on November 4 - 7, 2014. DevCon BI Team Lead Nathan Allan provided this unsolicited feedback: “What we found was pretty awesome. It has been a game changer for us in terms of the flexibility we can offer people that would like to get to the data in different ways.” Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the second in a series of reports on columnstore implementations, results from DevCon Security, a live customer production app for which performance increased by factors of from 10x to 100x for all report queries, including canned queries as well as reducing time for results for ad hoc queries from 5 - 7 minutes to 1 - 2 seconds. As a result of columnstore performance, the customer retired their SSAS infrastructure. I invite you to consider leveraging columnstore in your own environment. Let me know if you have any questions.

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  • AdventureWorks 2014 Sample Databases Are Now Available

    - by aspiringgeek
      Where in the World is AdventureWorks? Recently, SQL Community feedback from twitter prompted me to look in vain for SQL Server 2014 versions of the AdventureWorks sample databases we’ve all grown to know & love. I searched Codeplex, then used the bing & even the google in an effort to locate them, yet all I could find were samples on different sites highlighting specific technologies, an incomplete collection inconsistent with the experience we users had learned to expect.  I began pinging internally & learned that an update to AdventureWorks wasn’t even on the road map.  Fortunately, SQL Marketing manager Luis Daniel Soto Maldonado (t) lent a sympathetic ear & got the update ball rolling; his direct report Darmodi Komo recently announced the release of the shiny new sample databases for OLTP, DW, Tabular, and Multidimensional models to supplement the extant In-Memory OLTP sample DB.  What Success Looks Like In my correspondence with the team, here’s how I defined success: 1. Sample AdventureWorks DBs hosted on Codeplex showcasing SQL Server 2014’s latest-&-greatest features, including:  In-Memory OLTP (aka Hekaton) Clustered Columnstore Online Operations Resource Governor IO 2. Where it makes sense to do so, consolidate the DBs (e.g., showcasing Columnstore likely involves a separate DW DB) 3. Documentation to support experimenting with these features As Microsoft Senior SDE Bonnie Feinberg (b) stated, “I think it would be great to see an AdventureWorks for SQL 2014.  It would be super helpful for third-party book authors and trainers.  It also provides a common way to share examples in blog posts and forum discussions, for example.”  Exactly.  We’ve established a rich & robust tradition of sample databases on Codeplex.  This is what our community & our customers expect.  The prompt response achieves what we all aim to do, i.e., manifests the Service Design Engineering mantra of “delighting the customer”.  Kudos to Luis’s team in SQL Server Marketing & Kevin Liu’s team in SQL Server Engineering for doing so. Download AdventureWorks 2014 Download your copies of SQL Server 2014 AdventureWorks sample databases here.

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  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • Columnstore Case Study #2: Columnstore faster than SSAS Cube at DevCon Security

    - by aspiringgeek
    Preamble This is the second in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in my big deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. See also Columnstore Case Study #1: MSIT SONAR Aggregations Why Columnstore? As stated previously, If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. The Customer DevCon Security provides home & business security services & has been in business for 135 years. I met DevCon personnel while speaking to the Utah County SQL User Group on 20 February 2012. (Thanks to TJ Belt (b|@tjaybelt) & Ben Miller (b|@DBADuck) for the invitation which serendipitously coincided with the height of ski season.) The App: DevCon Security Reporting: Optimized & Ad Hoc Queries DevCon users interrogate a SQL Server 2012 Analysis Services cube via SSRS. In addition, the SQL Server 2012 relational back end is the target of ad hoc queries; this DW back end is refreshed nightly during a brief maintenance window via conventional table partition switching. SSRS, SSAS, & MDX Conventional relational structures were unable to provide adequate performance for user interaction for the SSRS reports. An SSAS solution was implemented requiring personnel to ramp up technically, including learning enough MDX to satisfy requirements. Ad Hoc Queries Even though the fact table is relatively small—only 22 million rows & 33GB—the table was a typical DW table in terms of its width: 137 columns, any of which could be the target of ad hoc interrogation. As is common in DW reporting scenarios such as this, it is often nearly to optimize for such queries using conventional indexing. DevCon DBAs & developers attended PASS 2012 & were introduced to the marvels of columnstore in a session presented by Klaus Aschenbrenner (b|@Aschenbrenner) The Details Classic vs. columnstore before-&-after metrics are impressive. Scenario Conventional Structures Columnstore ? SSRS via SSAS 10 - 12 seconds 1 second >10x Ad Hoc 5-7 minutes (300 - 420 seconds) 1 - 2 seconds >100x Here are two charts characterizing this data graphically.  The first is a linear representation of Report Duration (in seconds) for Conventional Structures vs. Columnstore Indexes.  As is so often the case when we chart such significant deltas, the linear scale doesn’t expose some the dramatically improved values corresponding to the columnstore metrics.  Just to make it fair here’s the same data represented logarithmically; yet even here the values corresponding to 1 –2 seconds aren’t visible.  The Wins Performance: Even prior to columnstore implementation, at 10 - 12 seconds canned report performance against the SSAS cube was tolerable. Yet the 1 second performance afterward is clearly better. As significant as that is, imagine the user experience re: ad hoc interrogation. The difference between several minutes vs. one or two seconds is a game changer, literally changing the way users interact with their data—no mental context switching, no wondering when the results will appear, no preoccupation with the spinning mind-numbing hurry-up-&-wait indicators.  As we’ve commonly found elsewhere, columnstore indexes here provided performance improvements of one, two, or more orders of magnitude. Simplified Infrastructure: Because in this case a nonclustered columnstore index on a conventional DW table was faster than an Analysis Services cube, the entire SSAS infrastructure was rendered superfluous & was retired. PASS Rocks: Once again, the value of attending PASS is proven out. The trip to Charlotte combined with eager & enquiring minds let directly to this success story. Find out more about the next PASS Summit here, hosted this year in Seattle on November 4 - 7, 2014. DevCon BI Team Lead Nathan Allan provided this unsolicited feedback: “What we found was pretty awesome. It has been a game changer for us in terms of the flexibility we can offer people that would like to get to the data in different ways.” Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the second in a series of reports on columnstore implementations, results from DevCon Security, a live customer production app for which performance increased by factors of from 10x to 100x for all report queries, including canned queries as well as reducing time for results for ad hoc queries from 5 - 7 minutes to 1 - 2 seconds. As a result of columnstore performance, the customer retired their SSAS infrastructure. I invite you to consider leveraging columnstore in your own environment. Let me know if you have any questions.

    Read the article

  • Columnstore Case Study #1: MSIT SONAR Aggregations

    - by aspiringgeek
    Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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  • MDX Studio download #mdx #ssas

    - by Marco Russo (SQLBI)
    Short version: the latest available version of MDX Studio can be downloaded from http://www.sqlbi.com/tools/mdx-studio/ Long version: Last week Stacia Misner twitted that the online version of MDX Studio was no longer available. It was hosted on http://mdx.mosha.com. It was a sad news, and it is also not good that nobody is maintaining the desktop version of MDX Studio. The latest release is the 0.4.14 and as I am writing it is still available on a SkyDrive link provided by Mosha Pasumansky, who wrote MDX Studio. Mosha does not work in Microsoft now and the entire BI community hopes that somebody will continue its work on this product. Unfortunately, it cannot be published on CodePlex because of some IP restrictions. Only bad news? Well, I hope no. The first good news is that MDX Studio also works with Analysis Services 2012 in Multidimensional mode. The second news is that, after having checked that we can do that, we created a web page on SQLBI web site to download the latest available release of MDX Studio. I hope it will be necessary to update it in the future, by now it is just a way to simplify the finding and download of this precious tool, and to grant that it will not disappear in case the current SkyDrive using to host the download would be discontinued, like it happened to the MDX Studio online version. Now a question to the BI Community: I know that there was some content available regarding tutorial on MDX Studio. I’d like to gather it and to put all in a single place. If you have such content, please contact me directly writing to marco (dot) russo (at) sqlbi [dot] com. Thanks!

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  • New training on Power Pivot with recorded video courses

    - by Marco Russo (SQLBI)
    I and Alberto Ferrari started delivering training on Power Pivot in 2010, initially in classrooms and then also online. We also recorded videos for Project Botticelli, where you can find content about Microsoft tools and services for Business Intelligence. In the last months, we produced a recorded video course for people that want to learn Power Pivot without attending a scheduled course. We split the entire Power Pivot course training in three editions, offering at a lower price the more introductive modules: Beginner: introduces Power Pivot to any user who knows Excel and want to create reports with more complex and large data structures than a single table. Intermediate: improves skills on Power Pivot for Excel, introducing the DAX language and important features such as CALCULATE and Time Intelligence functions. Advanced: includes a depth coverage of the DAX language, which is required for writing complex calculations, and other advanced features of both Excel and Power Pivot. There are also two bundles, that includes two or three editions at a lower price. Most important, we have a special 40% launch discount on all published video courses using the coupon SQLBI-FRNDS-14 valid until August 31, 2014. Just follow the link to see a more complete description of the editions available and their discounted prices. Regular prices start at $29, which means that you can start a training with less than $18 using the special promotion. P.S.: we recently launched a new responsive version of the SQLBI web site, and now we also have a page dedicated to all videos available about our sessions in conferences around the world. You can find more than 30 hours of free videos here: http://www.sqlbi.com/tv.

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  • Converting #MDX to #DAX and PowerPivot Workshop online #ppws

    - by Marco Russo (SQLBI)
    I just published the article Converting MDX to DAX – First Steps on the renewed SQLBI web site about converting MDX to DAX. The reason is that with BISM Tabular in Analysis Services 2012 you will be able to write queries in both DAX and MDX. If you already know MDX, you might wonder how to “translate” your MDX knowledge in DAX. I think that this is another way you can improve your knowledge about DAX: it has different concepts behind and this comparison should be helpful in this purpose. This is...(read more)

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  • Microsoft BI Conference 2010 Recap & books promo

    - by Marco Russo (SQLBI)
    Last week I’ve been at Microsoft BI Conference and I presented an interactive session about PowerPivot DAX Patterns. Unfortunately only the breakout session were recorded and available on TechEd Online . The room was full and there were probably many other people in an overflow room.  I would like to thanks all the attendees of my session and you can write me (marco dot russo [at] sqlbi dot com) if you have other questions and/or feedback about the session. The interest about PowerPivot (especially...(read more)

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  • Microsoft BI Conference 2010 Recap & books promo

    - by Marco Russo (SQLBI)
    Last week I’ve been at Microsoft BI Conference and I presented an interactive session about PowerPivot DAX Patterns. Unfortunately only the breakout session were recorded and available on TechEd Online . The room was full and there were probably many other people in an overflow room.  I would like to thanks all the attendees of my session and you can write me (marco dot russo [at] sqlbi dot com) if you have other questions and/or feedback about the session. The interest about PowerPivot (especially...(read more)

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  • CUBEMEMBER and CUBEVALUE stop working after #PowerPivot upgrade to #Excel 2013

    - by Marco Russo (SQLBI)
    I found an issue upgrading an Excel workbook containing PowerPivot data from Excel 2010 to Excel 2013. All CUBEMEMBER and CUBEVALUE functions point to a cube name that has been changed between the two version – you have to no longer reference the PowerPivot Data name, replacing it with ThisWorkbookDataModel instead. I wrote an article describing the change that you have to manually make to these Excel formulas in this article on SQLBI web site.

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  • Tips for adapting Date table to Power View forecasting #powerview #powerbi

    - by Marco Russo (SQLBI)
    During the keynote of the PASS Business Analytics Conference, Amir Netz presented the new forecasting capabilities in Power View for Office 365. I immediately tried the new feature (which was immediately available, a welcome surprise in a Microsoft announcement for a new release) and I had several issues trying to use existing data models. The forecasting has a few requirements that are not compatible with the “best practices” commonly used for a calendar table until this announcement. For example, if you have a Year-Month-Day hierarchy and you want to display a line chart aggregating data at the month level, you use a column containing month and year as a string (e.g. May 2014) sorted by a numeric column (such as 201405). Such a column cannot be used in the x-axis of a line chart for forecasting, because you need a date or numeric column. There are also other requirements and I wrote the article Prepare Data for Power View Forecasting in Power BI on SQLBI, describing how to create columns that can be used with the new forecasting capabilities in Power View for Office 365.

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  • Understanding #DAX Query Plans for #powerpivot and #tabular

    - by Marco Russo (SQLBI)
    Alberto Ferrari wrote a very interesting white paper about DAX query plans. We published it on a page where we'll gather articles and tools about DAX query plans: http://www.sqlbi.com/topics/query-plans/I reviewed the paper and this is the result of many months of study - we know that we just scratched the surface of this topic, also because we still don't have enough information about internal behavior of many of the operators contained in a query plan. However, by reading the paper you will start reading a query plan and you will understand how it works the optimization found by Chris Webb one month ago to the events-in-progress scenario. The white paper also contains a more optimized query (10 time faster), even if the performance depends on data distribution and the best choice really depends on the data you have. Now you should be curious enough to read the paper until the end, because the more optimized query is the last example in the paper!

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  • Tips for adapting Date table to Power View forecasting #powerview #powerbi

    - by Marco Russo (SQLBI)
    During the keynote of the PASS Business Analytics Conference, Amir Netz presented the new forecasting capabilities in Power View for Office 365. I immediately tried the new feature (which was immediately available, a welcome surprise in a Microsoft announcement for a new release) and I had several issues trying to use existing data models. The forecasting has a few requirements that are not compatible with the “best practices” commonly used for a calendar table until this announcement. For example, if you have a Year-Month-Day hierarchy and you want to display a line chart aggregating data at the month level, you use a column containing month and year as a string (e.g. May 2014) sorted by a numeric column (such as 201405). Such a column cannot be used in the x-axis of a line chart for forecasting, because you need a date or numeric column. There are also other requirements and I wrote the article Prepare Data for Power View Forecasting in Power BI on SQLBI, describing how to create columns that can be used with the new forecasting capabilities in Power View for Office 365.

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  • Optimize SUMMARIZE with ADDCOLUMNS in Dax #ssas #tabular #dax #powerpivot

    - by Marco Russo (SQLBI)
    If you started using DAX as a query language, you might have encountered some performance issues by using SUMMARIZE. The problem is related to the calculation you put in the SUMMARIZE, by adding what are called extension columns, which compute their value within a filter context defined by the rows considered in the group that the SUMMARIZE uses to produce each row in the output. Most of the time, for simple table expressions used in the first parameter of SUMMARIZE, you can optimize performance by removing the extended columns from the SUMMARIZE and adding them by using an ADDCOLUMNS function. In practice, instead of writing SUMMARIZE( <table>, <group_by_column>, <column_name>, <expression> ) you can write: ADDCOLUMNS(     SUMMARIZE( <table>, <group by column> ),     <column_name>, CALCULATE( <expression> ) ) The performance difference might be huge (orders of magnitude) but this optimization might produce a different semantic and in these cases it should not be used. A longer discussion of this topic is included in my Best Practices Using SUMMARIZE and ADDCOLUMNS article on SQLBI, which also include several details about the DAX syntax with extended columns. For example, did you know that you can create an extended column in SUMMARIZE and ADDCOLUMNS with the same name of existing measures? It is *not* a good thing to do, and by reading the article you will discover why. Enjoy DAX!

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  • Optimize Many-to-Many with SUMMARIZE and Other Techniques

    - by Marco Russo (SQLBI)
    We are still in the early days of DAX and even if I have been using it since 2 years ago, there is still a lot to learn on that. One of the topics that historically interests me (and many of the readers here, probably) is the many-to-many relationships between dimensions in a dimensional data model. When I and Alberto wrote the The Many to Many Revolution 2.0 we discovered the SUMMARIZE based pattern very late in the whitepaper writing. It is very important for performance optimization and it should be always used. In the last month, Gerhard Brueckl also presented an approach based on cross table filtering behavior that simplify the syntax involved, even if it’s harder to explain how it works internally. I published a short article titled Optimize Many-to-Many Calculation in DAX with SUMMARIZE and Cross Table Filtering on SQLBI website just to provide a quick reference to the three patterns available. A further study is still required to compare performance between SUMMARIZE and Cross Table Filtering patterns. Up to now, I haven’t observed big differences between them, even if their execution plans might be not identical and this suggest me that depending on other conditions you might favor one over the other.

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  • LASTDATE dates arguments and upcoming events #dax #tabular #powerpivot

    - by Marco Russo (SQLBI)
    Recently I had to write a DAX formula containing a LASTDATE within the logical condition of a FILTER: I found that its behavior was not the one I expected and I further investigated. At the end, I wrote my findings in this article on SQLBI, which can be applied to any Time Intelligence function with a <dates> argument.The key point is that when you write LASTDATE( table[column] )in reality you obtain something like LASTDATE( CALCULATETABLE( VALUES( table[column] ) ) )which converts an existing row context into a filter context.Thus, if you have something like FILTER( table, table[column] = LASTDATE( table[column] ) the FILTER will return all the rows of table, whereas you probably want to use FILTER( table, table[column] = LASTDATE( VALUES( table[column] ) ) )so that the existing filter context before executing FILTER is used to get the result from VALUES( table[column] ), avoiding the automatic expansion that would include a CALCULATETABLE that would hide the existing filter context.If after reading the article you want to get more insights, read the Jeffrey Wang's post here.In these days I'm speaking at SQLRally Nordic 2012 in Copenhagen and I will be in Cologne (Germany) next week for a SSAS Tabular Workshop, whereas Alberto will teach the same workshop in Amsterdam one week later. Both workshops still have seats available and the Amsterdam's one is still in early bird discount until October 3rd!Then, in November I expect to meet many blog readers at PASS Summit 2012 in Seattle and I hope to find the time to write other article on interesting things on Tabular and PowerPivot. Stay tuned!

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  • Optimize Many-to-Many with SUMMARIZE and Other Techniques

    - by Marco Russo (SQLBI)
    We are still in the early days of DAX and even if I have been using it since 2 years ago, there is still a lot to learn on that. One of the topics that historically interests me (and many of the readers here, probably) is the many-to-many relationships between dimensions in a dimensional data model. When I and Alberto wrote the The Many to Many Revolution 2.0 we discovered the SUMMARIZE based pattern very late in the whitepaper writing. It is very important for performance optimization and it should be always used. In the last month, Gerhard Brueckl also presented an approach based on cross table filtering behavior that simplify the syntax involved, even if it’s harder to explain how it works internally. I published a short article titled Optimize Many-to-Many Calculation in DAX with SUMMARIZE and Cross Table Filtering on SQLBI website just to provide a quick reference to the three patterns available. A further study is still required to compare performance between SUMMARIZE and Cross Table Filtering patterns. Up to now, I haven’t observed big differences between them, even if their execution plans might be not identical and this suggest me that depending on other conditions you might favor one over the other.

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  • Difference between LASTDATE and MAX for semi-additive measures in #DAX

    - by Marco Russo (SQLBI)
    I recently wrote an article on SQLBI about the semi-additive measures in DAX. I included the formulas common calculations and there is an interesting point that worth a longer digression: the difference between LASTDATE and MAX (which is similar to FIRSTDATE and MIN – I just describe the former, for the latter just replace the correspondent names). LASTDATE is a dax function that receives an argument that has to be a date column and returns the last date active in the current filter context. Apparently, it is the same value returned by MAX, which returns the maximum value of the argument in the current filter context. Of course, MAX can receive any numeric type (including date), whereas LASTDATE only accepts a column of type date. But overall, they seems identical in the result. However, the difference is a semantic one. In fact, this expression: LASTDATE ( 'Date'[Date] ) could be also rewritten as: FILTER ( VALUES ( 'Date'[Date] ), 'Date'[Date] = MAX ( 'Date'[Date] ) ) LASTDATE is a function that returns a table with a single column and one row, whereas MAX returns a scalar value. In DAX, any expression with one row and one column can be automatically converted into the corresponding scalar value of the single cell returned. The opposite is not true. So you can use LASTDATE in any expression where a table or a scalar is required, but MAX can be used only where a scalar expression is expected. Since LASTDATE returns a table, you can use it in any expression that expects a table as an argument, such as COUNTROWS. In fact, you can write this expression: COUNTROWS ( LASTDATE ( 'Date'[Date] ) ) which will always return 1 or BLANK (if there are no dates active in the current filter context). You cannot pass MAX as an argument of COUNTROWS. You can pass to LASTDATE a reference to a column or any table expression that returns a column. The following two syntaxes are semantically identical: LASTDATE ( 'Date'[Date] ) LASTDATE ( VALUES ( 'Date'[Date] ) ) The result is the same and the use of VALUES is not required because it is implicit in the first syntax, unless you have a row context active. In that case, be careful that using in a row context the LASTDATE function with a direct column reference will produce a context transition (the row context is transformed into a filter context) that hides the external filter context, whereas using VALUES in the argument preserve the existing filter context without applying the context transition of the row context (see the columns LastDate and Values in the following query and result). You can use any other table expressions (including a FILTER) as LASTDATE argument. For example, the following expression will always return the last date available in the Date table, regardless of the current filter context: LASTDATE ( ALL ( 'Date'[Date] ) ) The following query recap the result produced by the different syntaxes described. EVALUATE     CALCULATETABLE(         ADDCOLUMNS(              VALUES ('Date'[Date] ),             "LastDate", LASTDATE( 'Date'[Date] ),             "Values", LASTDATE( VALUES ( 'Date'[Date] ) ),             "Filter", LASTDATE( FILTER ( VALUES ( 'Date'[Date] ), 'Date'[Date] = MAX ( 'Date'[Date] ) ) ),             "All", LASTDATE( ALL ( 'Date'[Date] ) ),             "Max", MAX( 'Date'[Date] )         ),         'Date'[Calendar Year] = 2008     ) ORDER BY 'Date'[Date] The LastDate columns repeat the current date, because the context transition happens within the ADDCOLUMNS. The Values column preserve the existing filter context from being replaced by the context transition, so the result corresponds to the last day in year 2008 (which is filtered in the external CALCULATETABLE). The Filter column works like the Values one, even if we use the FILTER instead of the LASTDATE approach. The All column shows the result of LASTDATE ( ALL ( ‘Date’[Date] ) ) that ignores the filter on Calendar Year (in fact the date returned is in year 2010). Finally, the Max column shows the result of the MAX formula, which is the easiest to use and only don’t return a table if you need it (like in a filter argument of CALCULATE or CALCULATETABLE, where using LASTDATE is shorter). I know that using LASTDATE in complex expressions might create some issue. In my experience, the fact that a context transition happens automatically in presence of a row context is the main reason of confusion and unexpected results in DAX formulas using this function. For a reference of DAX formulas using MAX and LASTDATE, read my article about semi-additive measures in DAX.

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  • SSAS Maestro Training in July 2012 #ssasmaestro #ssas

    - by Marco Russo (SQLBI)
    A few hours ago Chris Webb blogged about SSAS Maestro and I’d like to propagate the news, adding also some background info. SSAS Maestro is the premier certification on Analysis Services that selects the best experts in Analysis Services around the world. In 2011 Microsoft organized two rounds of training/exams for SSAS Maestros and up to now only 11 people from the first wave have been announced – around 10% of attendees of the course! In the next few days the new Maestros from the second round should be announced and this long process is caused by many factors that I’m going to explain. First, the course is just a step in the process. Before the course you receive a list of topics to study, including the slides of the course. During the course, students receive a lot of information that might not have been included in the slides and the best part of the course is class interaction. Students are expected to bring their experience to the table and comparing case studies, experiences and having long debates is an important part of the learning process. And it is also a part of the evaluation: good questions might be also more important than good answers! Finally, after the course, students have their homework and this may require one or two months to be completed. After that, a long (very long) evaluation process begins, taking into account homework, labs, participation… And for this reason the final evaluation may arrive months later after the course. We are going to improve and shorten this process with the next courses. The first wave of SSAS Maestro had been made by invitation only and now the program is opening, requiring a fee to participate in order to cover the cost of preparation, training and exam. The number of attendees will be limited and candidates will have to send their CV in order to be admitted to the course. Only experienced Analysis Services developers will be able to participate to this challenging program. So why you should do that? Well, only 10% of students passed the exam until now. So if you need 100% guarantee to pass the exam, you need to study a lot, before, during and after the course. But the course by itself is a precious opportunity to share experience, create networking and learn mission-critical enterprise-level best practices that it’s hard to find written on books. Oh, well, many existing white papers are a required reading *before* the course! The course is now 5 days long, and every day can be *very* long. We’ll have lectures and discussions in the morning and labs in the afternoon/evening. Plus some more lectures in one or two afternoons. A heavy part of the course is about performance optimization, capacity planning, monitoring. This edition will introduce also Tabular models, and don’t expect something you might find in the SSAS Tabular Workshop – only performance, scalability monitoring and optimization will be covered, knowing Analysis Services is a requirement just to be accepted! I and Chris Webb will be the teachers for this edition. The course is expensive. Applying for SSAS Maestro will cost around 7000€ plus taxes (reduced to 5000€ for students of a previous SSAS Maestro edition). And you will be locked in a training room for the large part of the week. So why you should do that? Well, as I said, this is a challenging course. You will not find the time to check your email – the content is just too much interesting to think you can be distracted by something else. Another good reason is that this course will take place in Italy. Well, the course will take place in the brand new Microsoft Innovation Campus, but in general we’ll be able to provide you hints to get great food and, if you are willing to attach one week-end to your trip, there are plenty of places to visit (and I’m not talking about the classic Rome-Florence-Venice) – you might really need to relax after such a week! Finally, the marking process after the course will be faster – we’d like to complete the evaluation within three months after the course, considering that 1-2 months might be required to complete the homework. If at this point you are not scared: registration will open in mid-April, but you can already write to [email protected] sending your CV/resume and a short description of your level of SSAS knowledge and experience. The selection process will start early and you may want to put your admission form on top of the FIFO queue!

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  • PowerShell Script to Deploy Multiple VM on Azure in Parallel #azure #powershell

    - by Marco Russo (SQLBI)
    This blog is usually dedicated to Business Intelligence and SQL Server, but I didn’t found easily on the web simple PowerShell scripts to help me deploying a number of virtual machines on Azure that I use for testing and development. Since I need to deploy, start, stop and remove many virtual machines created from a common image I created (you know, Tabular is not part of the standard images provided by Microsoft…), I wanted to minimize the time required to execute every operation from my Windows Azure PowerShell console (but I suggest you using Windows PowerShell ISE), so I also wanted to fire the commands as soon as possible in parallel, without losing the result in the console. In order to execute multiple commands in parallel, I used the Start-Job cmdlet, and using Get-Job and Receive-Job I wait for job completion and display the messages generated during background command execution. This technique allows me to reduce execution time when I have to deploy, start, stop or remove virtual machines. Please note that a few operations on Azure acquire an exclusive lock and cannot be really executed in parallel, but only one part of their execution time is subject to this lock. Thus, you obtain a better response time also in these scenarios (this is the case of the provisioning of a new VM). Finally, when you remove the VMs you still have the disk containing the virtual machine to remove. This cannot be done just after the VM removal, because you have to wait that the removal operation is completed on Azure. So I wrote a script that you have to run a few minutes after VMs removal and delete disks (and VHD) no longer related to a VM. I just check that the disk were associated to the original image name used to provision the VMs (so I don’t remove other disks deployed by other batches that I might want to preserve). These examples are specific for my scenario, if you need more complex configurations you have to change and adapt the code. But if your need is to create multiple instances of the same VM running in a workgroup, these scripts should be good enough. I prepared the following PowerShell scripts: ProvisionVMs: Provision many VMs in parallel starting from the same image. It creates one service for each VM. RemoveVMs: Remove all the VMs in parallel – it also remove the service created for the VM StartVMs: Starts all the VMs in parallel StopVMs: Stops all the VMs in parallel RemoveOrphanDisks: Remove all the disks no longer used by any VMs. Run this script a few minutes after RemoveVMs script. ProvisionVMs # Name of subscription $SubscriptionName = "Copy the SubscriptionName property you get from Get-AzureSubscription"   # Name of storage account (where VMs will be deployed) $StorageAccount = "Copy the Label property you get from Get-AzureStorageAccount"   function ProvisionVM( [string]$VmName ) {     Start-Job -ArgumentList $VmName {         param($VmName) $Location = "Copy the Location property you get from Get-AzureStorageAccount" $InstanceSize = "A5" # You can use any other instance, such as Large, A6, and so on $AdminUsername = "UserName" # Write the name of the administrator account in the new VM $Password = "Password"      # Write the password of the administrator account in the new VM $Image = "Copy the ImageName property you get from Get-AzureVMImage" # You can list your own images using the following command: # Get-AzureVMImage | Where-Object {$_.PublisherName -eq "User" }         New-AzureVMConfig -Name $VmName -ImageName $Image -InstanceSize $InstanceSize |             Add-AzureProvisioningConfig -Windows -Password $Password -AdminUsername $AdminUsername|             New-AzureVM -Location $Location -ServiceName "$VmName" -Verbose     } }   # Set the proper storage - you might remove this line if you have only one storage in the subscription Set-AzureSubscription -SubscriptionName $SubscriptionName -CurrentStorageAccount $StorageAccount   # Select the subscription - this line is fundamental if you have access to multiple subscription # You might remove this line if you have only one subscription Select-AzureSubscription -SubscriptionName $SubscriptionName   # Every line in the following list provisions one VM using the name specified in the argument # You can change the number of lines - use a unique name for every VM - don't reuse names # already used in other VMs already deployed ProvisionVM "test10" ProvisionVM "test11" ProvisionVM "test12" ProvisionVM "test13" ProvisionVM "test14" ProvisionVM "test15" ProvisionVM "test16" ProvisionVM "test17" ProvisionVM "test18" ProvisionVM "test19" ProvisionVM "test20"   # Wait for all to complete While (Get-Job -State "Running") {     Get-Job -State "Completed" | Receive-Job     Start-Sleep 1 }   # Display output from all jobs Get-Job | Receive-Job   # Cleanup of jobs Remove-Job *   # Displays batch completed echo "Provisioning VM Completed" RemoveVMs # Name of subscription $SubscriptionName = "Copy the SubscriptionName property you get from Get-AzureSubscription"   function RemoveVM( [string]$VmName ) {     Start-Job -ArgumentList $VmName {         param($VmName)         Remove-AzureService -ServiceName $VmName -Force -Verbose     } }   # Select the subscription - this line is fundamental if you have access to multiple subscription # You might remove this line if you have only one subscription Select-AzureSubscription -SubscriptionName $SubscriptionName   # Every line in the following list remove one VM using the name specified in the argument # You can change the number of lines - use a unique name for every VM - don't reuse names # already used in other VMs already deployed RemoveVM "test10" RemoveVM "test11" RemoveVM "test12" RemoveVM "test13" RemoveVM "test14" RemoveVM "test15" RemoveVM "test16" RemoveVM "test17" RemoveVM "test18" RemoveVM "test19" RemoveVM "test20"   # Wait for all to complete While (Get-Job -State "Running") {     Get-Job -State "Completed" | Receive-Job     Start-Sleep 1 }   # Display output from all jobs Get-Job | Receive-Job   # Cleanup Remove-Job *   # Displays batch completed echo "Remove VM Completed" StartVMs # Name of subscription $SubscriptionName = "Copy the SubscriptionName property you get from Get-AzureSubscription"   function StartVM( [string]$VmName ) {     Start-Job -ArgumentList $VmName {         param($VmName)         Start-AzureVM -Name $VmName -ServiceName $VmName -Verbose     } }   # Select the subscription - this line is fundamental if you have access to multiple subscription # You might remove this line if you have only one subscription Select-AzureSubscription -SubscriptionName $SubscriptionName   # Every line in the following list starts one VM using the name specified in the argument # You can change the number of lines - use a unique name for every VM - don't reuse names # already used in other VMs already deployed StartVM "test10" StartVM "test11" StartVM "test11" StartVM "test12" StartVM "test13" StartVM "test14" StartVM "test15" StartVM "test16" StartVM "test17" StartVM "test18" StartVM "test19" StartVM "test20"   # Wait for all to complete While (Get-Job -State "Running") {     Get-Job -State "Completed" | Receive-Job     Start-Sleep 1 }   # Display output from all jobs Get-Job | Receive-Job   # Cleanup Remove-Job *   # Displays batch completed echo "Start VM Completed"   StopVMs # Name of subscription $SubscriptionName = "Copy the SubscriptionName property you get from Get-AzureSubscription"   function StopVM( [string]$VmName ) {     Start-Job -ArgumentList $VmName {         param($VmName)         Stop-AzureVM -Name $VmName -ServiceName $VmName -Verbose -Force     } }   # Select the subscription - this line is fundamental if you have access to multiple subscription # You might remove this line if you have only one subscription Select-AzureSubscription -SubscriptionName $SubscriptionName   # Every line in the following list stops one VM using the name specified in the argument # You can change the number of lines - use a unique name for every VM - don't reuse names # already used in other VMs already deployed StopVM "test10" StopVM "test11" StopVM "test12" StopVM "test13" StopVM "test14" StopVM "test15" StopVM "test16" StopVM "test17" StopVM "test18" StopVM "test19" StopVM "test20"   # Wait for all to complete While (Get-Job -State "Running") {     Get-Job -State "Completed" | Receive-Job     Start-Sleep 1 }   # Display output from all jobs Get-Job | Receive-Job   # Cleanup Remove-Job *   # Displays batch completed echo "Stop VM Completed" RemoveOrphanDisks $Image = "Copy the ImageName property you get from Get-AzureVMImage" # You can list your own images using the following command: # Get-AzureVMImage | Where-Object {$_.PublisherName -eq "User" }   # Remove all orphan disks coming from the image specified in $ImageName Get-AzureDisk |     Where-Object {$_.attachedto -eq $null -and $_.SourceImageName -eq $ImageName} |     Remove-AzureDisk -DeleteVHD -Verbose  

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  • How to install SpeedFiler on Outlook 2010 (aka Outlook 14)

    - by Marco Russo (SQLBI)
    This is off-topic here on SQLBlog, I know, but I think there will be many users like me wanting to find the solution for this problem. If you have SpeedFiler there is a problem installing it on Outlook 2010. The setup of SpeedFiler stop showing this message: SpeedFiler 2.0.0.0 works with the following products Microsoft Office Outlook 2003 Microsoft Office Outlook 2007 None of these products seems to be installed on your system. SpeedFiler will not be installed. Well, in reality SpeedFiler works...(read more)

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  • SSIS packages incompatibilities between SSIS 2008 and SSIS 2008 R2

    - by Marco Russo (SQLBI)
    When you install SQL 2008 R2 workstation components you get a newer version of BIDS (BI Developer Studio, included in the workstation components) that replaces BIDS 2008 version (BIDS 2005 still live side-by-side). Everything would be good if you can use the newer version to edit any 2008 AND 2008R2 project. SSIS editor doesn't offer a way to set the "compatibility level" of the package, becuase it is almost all unchanged. However, if a package has an ADO.NET Destination Adapter, there is a difference...(read more)

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