All posts by Dustin Ryan

Dustin is a Senior Business Intelligence Consultant with Pragmatic Works. You can find Dustin blogging and speaking at events like SQL Saturday, Code Camp, and SQL Rally. Follow Dustin on Twitter @SQLDusty.
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Script To Populate AdventureWorksDW DimDate

I do quite a bit of training for Pragmatic Works so I find myself working a lot with the AdventureWorksDW data warehouse database. AdventureWorksDW is a great test database if you’re wanting to demonstrate a concept or test a theory, but one of the things that has always bugged me is that the date dimension (dbo.DimDate) has holes in the data! Having a complete date dimension is important when working with SSAS. Well yesterday was the final straw, so I put together the following script that will fill in the missing dates in DimDate. You can specify a start date and end date and make the AdventureWorksDW DimDate date dimension as big as you like. Enjoy!

BEGIN TRAN 

declare @startdate date = '2005-01-01',
    @enddate date = '2014-12-31'

IF @startdate IS NULL
    BEGIN
        Select Top 1 @startdate = FulldateAlternateKey
        From DimDate 
        Order By DateKey ASC 
    END

Declare @datelist table (FullDate date)

while @startdate <= @enddate

Begin 
    Insert into @datelist (FullDate)
    Select @startdate

    Set @startdate = dateadd(dd,1,@startdate)

end 

 Insert into dbo.DimDate 
    (DateKey, 
        FullDateAlternateKey, 
        DayNumberOfWeek, 
        EnglishDayNameOfWeek, 
        SpanishDayNameOfWeek, 
        FrenchDayNameOfWeek, 
        DayNumberOfMonth, 
        DayNumberOfYear, 
        WeekNumberOfYear, 
        EnglishMonthName, 
        SpanishMonthName, 
        FrenchMonthName, 
        MonthNumberOfYear, 
        CalendarQuarter, 
        CalendarYear, 
        CalendarSemester, 
        FiscalQuarter, 
        FiscalYear, 
        FiscalSemester)


select convert(int,convert(varchar,dl.FullDate,112)) as DateKey,
    dl.FullDate,
    datepart(dw,dl.FullDate) as DayNumberOfWeek,
    datename(weekday,dl.FullDate) as EnglishDayNameOfWeek,
    (Select top 1 SpanishDayNameOfWeek From DimDate Where EnglishDayNameOfWeek = datename(weekday,dl.FullDate)) as SpanishDayNameOfWeek,
    (Select top 1 FrenchDayNameOfWeek From DimDate Where EnglishDayNameOfWeek = datename(weekday,dl.FullDate)) as FrenchDayNameOfWeek,
    datepart(d,dl.FullDate) as DayNumberOfMonth,
    datepart(dy,dl.FullDate) as DayNumberOfYear,
    datepart(wk, dl.FUllDate) as WeekNumberOfYear,
    datename(MONTH,dl.FullDate) as EnglishMonthName,
    (Select top 1 SpanishMonthName From DimDate Where EnglishMonthName = datename(MONTH,dl.FullDate)) as SpanishMonthName,
    (Select top 1 FrenchMonthName From DimDate Where EnglishMonthName = datename(MONTH,dl.FullDate)) as FrenchMonthName,
    Month(dl.FullDate) as MonthNumberOfYear,
    datepart(qq, dl.FullDate) as CalendarQuarter,
    year(dl.FullDate) as CalendarYear,
    case datepart(qq, dl.FullDate)
        when 1 then 1
        when 2 then 1
        when 3 then 2
        when 4 then 2
    end as CalendarSemester,
    case datepart(qq, dl.FullDate)
        when 1 then 3
        when 2 then 4
        when 3 then 1
        when 4 then 2
    end as FiscalQuarter,
    case datepart(qq, dl.FullDate)
        when 1 then year(dl.FullDate)
        when 2 then year(dl.FullDate)
        when 3 then year(dl.FullDate) + 1
        when 4 then year(dl.FullDate) + 1
    end as FiscalYear,
    case datepart(qq, dl.FullDate)
        when 1 then 2
        when 2 then 2
        when 3 then 1
        when 4 then 1
    end as FiscalSemester

from @datelist dl left join 
    DimDate dd 
        on dl.FullDate = dd.FullDateAlternateKey
Where dd.FullDateAlternateKey is null 




COMMIT TRAN
I’ve tested the script against AdventureWorksDW2012 and AdventureWorksDW2008R2 and it worked great.
Let me know if you found this useful! Thanks for reading!
null

How To Display 0’s Instead of NULL in your SSAS Cube & MDX Query Results

I’ve done a decent amount of SSAS and MDX query development over my career and one of the requirements that continually pops up is to display zeros instead of NULL when browsing the cube. Take the following query as an example:

Select {[Date].[Calendar Year].Children} on 0,

NON EMPTY {[Geography].[State-Province].members} on 1

From [Adventure Works DW]

Where [Measures].[Reseller Sales Amount]

 

And here are the results:

image

There are a couple ways display 0’s instead of NULL. The first way is to create logic in your MDX script that uses a CASE statement of an IIF expression that manually checks for empty cube space using the ISEMPTY function, similar to the following example:

SCOPE ([Measures].[Reseller Sales Amount]);

THIS=IIF(ISEMPTY([Measures].[Reseller Sales Amount]),0,[Measures].[Reseller Sales Amount]);

END SCOPE;
The problem with this method is that any empty cube space will be populated with 0’s, potentially exploding your results! Rows that should not be displayed are now displayed! In the image below, I’ve highlighted a row that does not have any results that should not be displayed. Queries that usually only return a handful of results could now potentially display thousands or millions of rows! Yikes! And that’s something we definitely want to avoid.
image
There is another way that we can fill the NULL’s with a 0 or any other value we choose: Through the FormatString property. The FormatString property accepts four arguments separated by a semicolon (;). The first argument is how positive value should be formatted, the second is how negative values should be formatted, the third argument is how 0 values should be formatted, and the fourth argument is how NULL should be formatted. The fourth argument is the one we are interested in!
Here is an example of how I am formatting the Reseller Sales Amount: “#,##0.00;-#,##0.00;;0”.
image
By configuring the FormatString property this way, we are no longer filling the empty space in the cube with 0’s, rather we are configuring the measure to only display a 0 should a NULL value appear in the results. Check it out.
image
No more NULL values. If you’d rather display a non-numeric value, you can do this: “#,##0.00;-#,##0.00;;Zero”
image
If you thought that was helpful or enjoyed reading my blog, leave a comment. Feel free to post any questions, as well!

Pragmatic Works Release Doc xPress Server Edition

This month marks the official release of Pragmatic Works’ Doc xPress Server Edition! While everyone knows that Doc xPress gives you the capability to document your SQL Server databases, SSIS packages, SSAS cubes, and SSRS reports in way like no other tool. But with the release of the Server Edition, you can now host your documentation to a hosted web application making it now easier than ever to share documentation, lineage, and data dictionary information across your organization. Imagine being able to provide your technical users as well as your business users a one-stop-shop to all of your organization’s technical documentation without requiring anything to be installed on their desktop! Doc xPress Server Edition provides that capability!

Being able to view your Doc xPress-generated documentation online means that your technical team can quickly and easily assess the impact of changes in your BI environment by conducting a thorough lineage and impact analysis. What SSIS packages, SSRS reports, or SSAS cubes will be affected if I change a single column? Now you know!

How many times have you wondered what your environment looked like in the past? With Doc xPress, you can periodically snapshot your environment and compare snapshots over time so you can quickly and easily assess any changes that have occurred. And now you can view the documentation in your web browser without the need for any desktop configuration or installation.

If Doc xPress Server Edition sounds interesting and you’d like more information on the software, Pragmatic Works is presenting a free online webinar on the technology July 30th at 1:00 pm EST! Head over to PragmaticWorks.com (you’ll need to scroll down a screen or two) to get signed up and registered for the event.

Data Validation Via Data Surf

imageIf you’re a developer like me, you’ve probably at some point had to validate data. Validation is often a tedious and boring job that can involve checking individual records and data values in multiple tables. It’s not fun but its a necessary part of our job after all. Recently I’ve discovered the Data Surf feature of DBA xPress which is part of the Pragmatic Workbench DBA toolset.

Data Surf enables me to begin browsing a database beginning with a single table or even a single record. Using that table or record as a start point, I can navigate to other tables related to my initial selection. I can specify that I’d like to view parent or child records of my selected row in a related table, which makes validating data a snap. So I’d like to show you how we can accomplish that.

I’ll begin by selecting the Data Surf feature from the Pragmatic Workbench.

image

Next I specify the SQL Server and Database I’d like to begin surfing. For this example I’ll start with my trust Adventure Works Data Warehouse.

image

Then I’ll select which table I’d like to begin my surf. Reseller Sales it is. You’ll notice if you hover your mouse over the name of the table, you can view the columns with their data types that exist in the table.

image

I can also optionally specify a specific search criteria. If I’d like to view certain records associated with a product, I can do that. This is a very nice feature for data validation.

image

Now I can see my surf’s starting point. I have a quick view of the records in the table as well as a row count.

image

If I click one of the records in the table , some options appear. On the left side of the screen, I can see related parent tables as well as child tables. In the case of the FactResellerSales, there happen to be no related child tables.

image

In the margin, if I click the dbo.DimEmployee table, I can now see the relationship between DimEmployee and FactResellerSales. I can also see the related Employee record based on the record I had initially highlighted in FactResellerSales. Because I had selected a record with an EmployeeKey of 285, when I click DimEmployee I see the record(s) with EmployeeKey 285. Also, by selecting the DimEmployee table, my related child and parent tables on the left have changed.

image

If I click on the DimSalesTerritory table, I can now see the relationship between DimSalesTerritory and DimEmployee. In the record viewer, I also see the DimSalesTerritory record(s) related to the DimEmployee record with EmployeeKey 285.

image

There’s also some other nice features available with Data Surf. I can customize the colors of the nodes in my diagram in order to produce a simple and easy to view ERD.
image

And once you’re done surfing, validating, etc, you can easily save your diagram as an image.

image

All in all, Data Surf is a very simple and easy to use tool that I can see as being very beneficial to the average developer. As a BI developer, I spend most of my time designing data warehouse solutions and having Data Surf in my back pocket is great. Download the free trial at PragmaticWorks.com and check out Data Surf.

SQL Saturday #298 Session Material Now Available (SSAS Tabular Models)

Another fantastic SQL Saturday event is in the books and it did not disappoint! There was a great crowd at University of North Florida in Jacksonville, FL and plenty of amazing sessions and speakers. Thank you to everyone that attended my session! I hope you enjoyed the session as much as I enjoyed presenting. It was a blast!

If you’re interested, the slide deck I used during the presentation is now available for your viewing pleasure, which you can see here.

If you have any questions or would like to contact me, you can always send me a message on Twitter or email me here. Again, thank you to all of my attendees!

Learn to Design Tabular Models at SQL Saturday #298 Jacksonville, FL on 5/10/2014

I’m excited to announce that on Saturday May 10th in a couple Saturdays I’ll be speaking at SQL Saturday 298 in Jacksonville, FL! SQL Saturday in Jacksonville always has an awesome turnout and I love speaking at this event! There will be tons of great free training sessions from the likes of Devin Knight, Robert Cain, Andy Warren, Adam Jorgensen, Max Trinidad, and tons of other great experts! If you’re in the North Florida area on May 10, you need to get registered for this event!

The session I’ll be delivering is called What the Tabular??? and will start at 1:15 PM EST on Saturday, May 10th! If you’re interested in learning how to build a Tabular Model, how to follow Tabular Model design best practices, and how to decide if a Tabular Model is the right solution to your business problem, then my session What the Tabular??? is for you! It’s a great beginner sessions and will feature tons of demos and walkthroughs of the technology!

So if you’re nearby, definitely get signed-up for this awesome event! You won’t regret it!

turtle-slow-seo

Do You Know Why Your MDX Query Is Slow?

Performance tuning MDX queries can often be a daunting and challenging task. But the first step in deciding where to begin your efforts to improve the performance of your query is to diagnose the source of the problem. There are two areas that could be causing our performance issue: 1. The design of our SSAS solution or 2. The design of our query. We need to figure out if we’ve written a bad query or designed a bad cube :).

How Do We Test The Query?

Usually an issue is discovered when a user comes to the BI team with a report that appears to be running slowly. So for our example, I have a pivot table in an excel workbook that is running a little slow. Let’s walk through this together and diagnose what the problem could be. Below here you can see the pivot table.

image

The first thing you’ll need to do is test the query and the best way to do this is to execute the query in isolation so that we can eliminate outside factors as part of the problem. So in this example I would execute the report outside of business hours. I want to make sure I do this outside of peak use time because one of the things I’ll need to do is clear the cache. In order to prevent SSAS from satisfying our query by leveraging the formula cache and the storage engine cache, we need to execute a ClearCache command to prevent our results from being corrupted. To do this, I’ll execute the following XMLA script in SSMS.

 

<ClearCache xmlns=”http://schemas.microsoft.com/analysisservices/2003/engine>
     <Object>
          <DatabaseID>AdventureWorksDW2012MutliDimensional-EE</DatabaseID>
          <CubeID>Adventure Works</CubeID>
     </Object>
</ClearCache>

 

Also, I’ll execute the following MDX to initialize my cube’s MDX script.

 

Select {} on 0 
From [Adventure Works]

For us to figure out how long our query is taking to execute, we’re going to fire up SQL Server Profiler and execute a trace against SSAS while we execute the query so we can gather all the nitty gritty details of our query execution. By running a trace, we will be able to see all kinds of really helpful details like the total duration of the query, the partitions being queries, if aggregations are being used to answer the query, which attributes are being used, and much more.

To begin our trace, go to Tool > SQL Server Profilers in SSMS.

image

Flip the Server type to Analysis Services and set the Server name to your SSAS instance that is home to the SSAS database your report is pointed at.

image

Next you’ll see the Trace Properties window. Go to the Events Selection tab, and check the check box near the bottom right of the window to Show all events. Scroll down a little more than half way and find the events “Get Data From Aggregation” and “Query Subcube Verbose.” The “Get Data From Aggregation” event is fire when an aggregation is used to satisfy a query. This event is also especially helpful when trying to determine if the aggregations you have designed are actually useful. The “Query Subcube Verbose” event will give you very detailed information on which members from which dimension attributes are being queried to satisfy the query. Click Run when you’re done.

image

Now that the trace is running, its time to conduct our test.

1. The first thing I’ll do is execute the Clear Cache command.

 

<ClearCache xmlns=”http://schemas.microsoft.com/analysisservices/2003/engine>
     <Object>
          <DatabaseID>AdventureWorksDW2012MutliDimensional-EE</DatabaseID>
          <CubeID>Adventure Works</CubeID>
     </Object>
</ClearCache>

 

2. Initialize the Calculation script in your cube.

 

Select {} on 0 
From [Adventure Works]
3. Execute the report/query. In my case, my report is an Excel pivot table, so I’ll simply click the refresh button in Excel to execute my query.

The report may take a few seconds or minutes to run depending on the query, but it should probably take longer than you’re used to simply because the query is running against an empty cache and SSAS will have to retrieve all of the data from storage.

When the query is finished executing, pause your Trace in SQL Server Profiler by clicking the pause button at the top of the Trace window. Now its time to take a look at the results. Find the Query End event in the Trace results. Find the Duration column. This number displays the total query duration in milliseconds. In my example, my query took just over 13 seconds to execute. So its not unbelievably slow, but certainly slower than we’d like.

image

In order to take a more in depth look at our query’s performance, lets save the Trace results to a SQL Server table so we can query it. Go to File > Save As > Trace Table.

image

Specify where you’d like to save the results and click OK.

image

Below you’ll see a very useful query that will break down where your query is experiencing a slow down. If you’re using this query for your testing, don’t forget to alter the From clause to query your trace table.

 

SELECT x.ConnectionID,
       x.QueryID,
       x.QueryDuration,
       p.SEDuration,
       CASE 
WHEN p.SEDuration > x.QueryDuration THEN NULL ELSE x.QueryDuration - p.SEDuration 
END AS FEDuration,
       y.[Number of SE Queries],
       y.[Thread Duration of SE Queries],
       w.[Aggregations Read],
       x.TextData
FROM   (SELECT a.ConnectionID,
               a.Duration AS QueryDuration,
               a.TextData,
               CAST (HashBytes('SHA1', CAST (reverse(CAST (TextData AS VARCHAR (MAX))) AS NVARCHAR (4000))) AS INT) AS QueryID
        FROM   MyTraceTable AS a
        WHERE  a.EventClass = 10) AS x -- Query End Event
       LEFT OUTER JOIN
       (/* Determine Query Subcube Verbose of Non-cache data */
       SELECT   ConnectionID,
                 COUNT(*) AS [Number of SE Queries],
                 SUM(Duration) AS [Thread Duration of SE Queries]
        FROM     MyTraceTable
        WHERE    EventClass = 12 -- Query Subcube Verbose
                 AND EventSubclass = 22 -- Non-cache data
        GROUP BY ConnectionID) AS y
       ON y.ConnectionID = x.ConnectionID
       LEFT OUTER JOIN
       (/* Determine Aggregations that are ready from */
       SELECT   ConnectionID,
                 COUNT(*) AS [Aggregations Read]
        FROM     MyTraceTable
        WHERE    EventClass = 60 -- Read from Aggregations
        GROUP BY ConnectionID) AS w
       ON w.ConnectionID = x.ConnectionID
       LEFT OUTER JOIN
       (/* Determine SE time */
       SELECT   ConnectionID,
                 SUM(Duration) AS SEDuration
        FROM     MyTraceTable
        WHERE    EventClass = 11 -- Query SubCube
        GROUP BY ConnectionID) AS p
       ON p.ConnectionID = x.ConnectionID;

 

 

The results here are very telling. The column “QueryDuration” shows us the total execution time of the query. The column “SEDuration” shows us the amount of time SSAS spent pulling the data from storage (Storage Engine). The column “FEDuration” shows how long SSAS spent calculating our queries results (Formula Engine).

image

In this particular example, the vast majority of our query’s execution time is spent in the Formula Engine. Of the 13+ seconds spent executing the query, the query spends more than 12 seconds in the Formula Engine and only 297 milliseconds pulling the data from storage. This tells us that the problem is probably not with the design our SSAS solution, but rather a poorly written query. Unfortunately, this being an MDX query generated by Excel, there’s not a lot we can do about altering the query.

How Do We Fix The Query?

Typically when deciding where to spend your performance tuning efforts you want to start in the area where your query spends more than 30% of its time (If its a 50/50 split make an educated decision). In the previous example, we’ve determined our problem is with the query.

What can I do to improve my MDX query?

If you determine your query’s problem is the query itself, ensure SSAS is utilizing subspace computation instead of cell by cell computation. SSAS will usually evaluate groups of cells in your cube at a time but in certain situations SSAS will evaluate cube space one cell at a time. We want to to avoid that. You can get a hint that SSAS is calculation your results one cell at a time if the query on our trace table shows a large amount of Storage Engine queries. Certain SSAS/MDX functions can disable subspace computation.

1. Late binding functions (ex. StrtoMember, StrtoSet functions)
2. Set aliases
3. LookupCube function

Also, check out this blog for more info on ways to improve your MDX query.

But what if the problem is my SSAS solution’s design?

If you conduct your test and determine the majority of the query duration is spent pulling the data from storage, there’s a lot to consider when discussing cube design best practices. But here are some brief highlights of things to consider.

1. Can we design aggregations to help our query? Look at your test results and see if aggregations are being used to satisfy the query.
2. Can we implement a partition design strategy that keeps SSAS from having to query larger partitions?
3. Are the right partitions being queried? For example, if your query is asking for data for 2010 and you notice in your trace that the partitions for all the other years are being queried, this could indicate that SSAS is having a hard time figuring out which partition has the correct data. You may need to set the Slice property on your partition.
4. Create Attribute relationships
5. Leverage Natural Hierarchies

There are many more best practices for cube design, but this is probably a good starting point.

If you’ve found this helpful, share it with a colleague or a friend and leave me a comment. Feel free to leave me a question or feedback in the comments here or send me a note on Twitter! I love discussing new ideas and learning so don’t hesitate!

SSAS Static Named Sets Vs. Dynamic Named Sets

So I’m 95% sure that I blogged about this topic at some point over the last couple years, but every time I try to find the link to show a class I’m teaching or to show a client, I can never find the darn thing. This is why I’m writing this blog. That and its also nice to have a good example of this on hand, which is what we have here.

In SSAS we have the ability to create named sets. An named set is basically an aliased set expression that we can use within our MDX queries. This is very useful if we have a set that is commonly used in our organization’s reporting solution.

But there are two types of named sets: static and dynamic. Static and dynamic sets appear very similar but they actually behave very differently, which is why I present to you the following example.

Below you will see a snippet of MDX from my cube script that creates a named set called Top 10 Customers – Static. This is the basic syntax for creating a named set in your cube’s MDX script. You’ll notice the static keyword, highlighted in blue. This specifies that we wish this named set to be static. The static keyword is actually optional, because if we leave the static keyword out of the create set statement, the set will still be created as a static named set.

CREATE STATIC SET CURRENTCUBE.[Top 10 Customers - Static]

AS topcount(

[Customer].[Customer].children,

10,

[Measures].[Internet Sales Amount]

) ;

The next create set statement creates a dynamic named set called Top 10 Customers – Dynamic, the big difference here being obviously the keyword dynamic, highlighted in blue. This specifies that this named set should be created as a dynamic named set.

CREATE DYNAMIC SET CURRENTCUBE.[Top 10 Customers - Dynamic]

AS topcount(

[Customer].[Customer].children,

10,

[Measures].[Internet Sales Amount]

) ;

Here you can see the create set statements in my Adventure Works cube script.

image 

And here we can see the two sets as they appear in our cube’s metadata tab in SQL Server Management Studio.

image

Now this is where it gets interesting. Below we have an example of our static named set being used in a query on the row axis. And we can see that it works fine.

image

But what happens if add a constraint in the Where clause? Uh oh, we run into an issue. The static named set does not respect the Where clause (or a subselect statement in the From clause for that matter). The named set displays the same members instead of displaying the top 10 customers from the year 2006. This could be a problem for our users depending on the requirements of the reporting solution.

image

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This could be where a dynamic named set may be more useful. Here you can see an example of  a query that uses our dynamic named set.

image

Except when we provide a constraint in the Where clause, the named set listens to the Where clause and displays the correct data. I know the Internet Sales Amount numbers are all the same but that’s just the nature of the Adventure Works data.

image

I think this perfectly demonstrates the differences between static and dynamic named sets. Static named sets behave exactly as their name suggests: They are static and do not respect the Where clause or a subselect statement in the From clause. The dynamic named set is dynamic and will listen to a Where clause slicer or a subselect in the From clause.

If this is all a little overwhelming to you and anytime someone mentions using MDX you curl up into the fetal position, suck your thumb, and sob uncontrollably, I would suggest taking a look at the BI xPress calculation builder. BI xPress has a nifty little wizard that will help you create MDX calculations and named sets without you having to do any of the tough MDX writing on your own.

To create a named set with the BI xPress calculation builder, click the little calculate icon in the Calculations tab of the cube designer in BIDS or SSDT. This will open up the MDX Calculation Builder Wizard part of BI xPress.

image

Choose the Top 10 Count template under the Sets folder and click Next.

image

On the next screen we can pick the attribute required for our set. In this case, I’ll select the Customer attribute of the Customer dimension in order to create the Top 10 Customers set we were playing with earlier.

image 

Then we select the measure we want to use to rank our customers. I’m selecting the Internet Sales Amount measure.

image 

Lastly we give our named set a name and click finish. On this screen we can preview the MDX the BI xPress MDX Calculation Builder wrote for us.

image 

And we’re done!

image 

The BI xPress MDX Calculation Builder wrote all the MDX for us without us having to know a lick of MDX! Pretty nifty if I do say so myself. For more information on BI xPress or the BI xPress MDX Calculation Builder, head over to PragmaticWorks.com and download the free trial of BI xPress.

And if you have any questions or comments, please feel free to leave a comment or shout out on Twitter @SQLDusty! Thanks!

Recording Now Available For The Webinar, Choosing The Right Analysis Services: MOLAP Vs. Tabular

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Thanks to everyone that attended Devin’s and my webinar called Choosing The Right Analysis Services: MOLAP vs. Tabular. I’m pleased to announce that the recording is now available to watch for free over at PragmaticWorks.com, so please go check it out. It’s a little less than an hour so you can watch it during your lunch break.

Also, the PowerPoint slide deck Devin and I used during the webinar is also available for viewing now! Please visit this link to download the slide deck.

Now for the questions! Many of you asked some great questions but unfortunately we ran out of time to answer all of the questions during the webinar. So here are a few of the questions we didn’t get to.

Q: How do i link if column have more than one column is key column in tabular?
A: If you need to create a composite key in a Tabular model table, you will need to create a calculated column that concatenate the columns that make up your composite key. You’ll need to do this in both tables you wish to relate. Once you’ve done that, then you can create the relationship between the two tables using your new columns.

Q: Can DAX be used to access cubes?
A: In the SQL 2012 SP1 CU4 release, DAX support for multidimensional cubes was added, so as long as you are running on SQL 2012 SP1 CU4 or later, you should be able to query cubes with DAX expressions. On a side note, MDX can also be used to query a Tabular model.

Q: Since tablular solution is many ways better than Muti Dimensional..then my question is when to go for Multi dimensional solution
A: This is one we covered extensively during the webinar. Here are some of the things to consider:

  1. How much data are you dealing with? If its too much to fit into memory for your Tabular model, then MOLAP is the way to go.
  2. Do you have a need for complex relationships? If so, MOLAP may be the answer. Role playing dimensions and many-to-many relationships are possible to create in a Tabular model, but they’re easier to create and manage in a MOLAP cube.
  3. Do you need to perform many complex calculations involving complex Scope assignments? If so, MOLAP is the answer here.

Q: Can you use a Multidimensional database as the source for a Tabular model and improve performance when creating low level granular reports?? This goes back to the performance differences between Multidimensional vs Tabular when creating granular reports.
A: You can use a Multidimensional database as a data source for a Tabular model, but I would suggest getting the data from the original source for the tabular model. If granular type queries are slow against your cube, those same queries are still going to be slow when you execute them to process your Tabular model.

Thanks to everyone that attending Devin’s and my webinar! If you have any other questions, please feel free to leave a comment or send me a message on Twitter!