Books

• SQL Server 2008 MDX Step by Step, Bryan C. Smith, C. Ryan Clay, Hitachi Consulting (Microsoft Press, 2009). This book is useful for beginners, and leads you through the key concepts of MDX as the name implies…step by step. Although it’s written for SQL Server 2008, you will find it useful for SQL Server 2008 R2 as well and, although I haven’t tested it, it should work for SQL Server 2012.
• MDX with SQL Server 2008 R2 Analysis Services Cookbook, Tomislave Piasevoli (Packt Publishing, 2011). I really like this book, as you might surmise from my review, and picked up a few tips myself. However, you’re not going to start learning MDX with this book. I recommend that you first read the Step by Step or some of the online resources first to get the most value from it.
• MDX Solutions, 2^nd edition. George Spofford, Sivakumar Harinath, Christopher Webb, and Dylan Hai Huang (Wiley, 2006). Although this book is focused on SQL Server 2005, much of it still applies to 2008 and later versions as the language remains largely intact. However, I must say this is not a book for beginners. There is lots of useful information here, but you will find it more useful if you already have a good grasp of the basics.
• Practical MDX Queries for Microsoft SQL Server Analysis Services 2008, Art Tennick (McGraw-Hill, 2010). I haven’t read this book yet, but it’s in my queue to read this year. If you’ve read it, I invite you to add a comment to this post with your impression.

 Online Resources

• Stairway to MDX Series, I have long recommended Bill Pearson’s (@Bill_Pearson) online writings, such as MDX Essentials at Database Journal, and of course must heartily recommend his latest endeavor at SQL Server Central.
• MDX Studio. This resource is not one designed to teach you MDX, but I include it as a tool that you can use to improve your MDX as it is a tool that you can use to identify problems  in your query and to capture statistics for your query if you’re attempting to improve performance. Mosha, the developer of this tool, also developed an online version for formatting and analysis.

Blogs

• Mosha Pasumansky is one of the architects responsible for the MDX language, so what better resource could you ask for? He’s no longer at Microsoft, but fortunately his legacy persists at SQLBlog.com with many posts dedicated to providing insights into MDX.
• Chris Webb (@Technitrain) is a coauthor of the second edition of the MDX Solutions mentioned above and writes frequently about  MDX. He has some very creative ideas that I have found useful in some of the more bleeding edge projects I have worked on.
• Another terrific MDX online resource is Marco Russo (@MarcoRus). Marco and Chris Webb (along with Alberto Ferrari) co-authored Expert Cube Development with Microsoft SQL Server 2008 Analysis Services which I recommend if you want to learn more about developing the cubes that we use as a source for MDX queries.
• Boyan Penev (@BoyanPenev) also writes on SSAS topics that you might find helpful for improving your MDX skills
• Greg Galloway has some interesting studies on MDX at his blog.
• SSAS-info, run by Vidas Matelis (@VidasM) is a great aggregation site that includes MDX topics.
• And of course, I have my own series which will expand over time, so keep checking back here for everything I write on MDX.

Whitepapers/Best Practices

• Identifying and Resolving MDX Query Performance Bottlenecks. MDX performance is partly dependent on the choices you make when constructing the query and partly dependent on the cube structure. This whitepaper explains how to determine where to focus your attention for resolving query performance issues.
• Analysis Services 2008 Performance Guide. Here you’ll find some good information about avoiding MDX functions that cause performance problems. You can use this whitepaper in conjunction with the performance bottleneck whitepaper to ferret out the problems you might be experiencing with slow queries.
• Analysis Services Query Performance Top 10 Best Practices. As you build your MDX skills, it’s a very good idea to keep best practices in mind. This is  a nice quick reference to have handy.

You might do this during cube development to test out calculation syntax before adding it to the cube, or you might do this only when you need to run a one-time query to answer a specific question or test the cube. You can even use this technique for Reporting Services reports or with  other tools. But I have to recommend that, wherever possible, you should put calculations inside a cube which would render moot what I’m about to show you.  Why? So you don’t have to reinvent the wheel each time you need a calculation. One of the reasons we build cubes is to centralize business logic, which includes calculations. That said, some calculations can’t go in the cube. Whatever your reason, there will likely come a time that you’ll need a calculation added to your query, so the point of this post is to prepare you for that time.

We’ll start with an analogy in Excel, using a percent of total calculation as shown below. The formula consists of a numerator to get the sales amount on the current row and a denominator that references the total sales amount in cell B2. We use the terms relative reference and absolute reference to describe the values in the numerator and the denominator, respectively. So for Road-250 Black, 44 on row 4, we have a formula =B4/$B$2, where B4 is a relative reference and $B$2 is the absolute reference. As we paste the formula into rows 2, 3, and 5, the formula changes to =B2/$B$2, =B3/$B$2, and B5/$B$2.

We can get similar behavior in an MDX query by using tuples. I can create one tuple as an expression (the term we use in Analysis Services that corresponds to a formula in Excel) for the numerator, and a second tuple as an expression for the numerator. Let’s create a query that has a calculation for the numerator and denominator separately, and then a third calculation for the final calculation so that we can see the results independently.

First, I like to create the query with placeholders, just to get the query structure straight before I introduce anything else that might compromise the query as I add complexity. Not that this example is complex, but it’s a good habit to develop. Start simple, and build up from there. It’s too easy to mess yourself up with a stray comma or parenthesis. Note that we add in each expression in a WITH clause that precedes the SELECT statement, and that we add the expressions as measures. Then we can use them just like a measure that comes from the cube in the SELECT statement.

WITH
MEMBER [Measures].[Numerator] AS NULL
MEMBER [Measures].[Denominator] AS NULL
MEMBER [Measures].[Pct of Total] AS NULL
SELECT
{[Measures].[Sales Amount],[Measures].[Numerator],[Measures].[Denominator],[Measures].[Pct of Total]}
ON COLUMNS,
[Product].[Category].Members ON ROWS
FROM [Adventure Works]

Now let’s update the numerator expression by replacing NULL with [Measures].[Sales Amount]. You can see below that the query result simply repeats the sales amount value from the first column in the second column. Let’s review what we know about tuples from Location, Location, Location. When the query resolves the tuple for the second column, the current measure – [Measures].[Numerator] – doesn’t really exist in the cube, but its expression redirects to [Measures].[Sales Amount] which then gets combined with the category member on each row to produce a tuple which in turn resolves as a value retrieved from the cube. Just like the tuples for each value in the first column. This tuple is like the relative reference we saw in the Excel example – Sales Amount varies according to the row.

Next let’s update the denominator expression by replacing NULL with a tuple expression: ([Measures].[Sales Amount], [Product].[Category].[All Products]). This time the query results shown below repeat the tuple value that we see at the intersection of the first column and first row. When the query resolves the tuple for the third column, both the measure in the tuple and the category member come from the expression. Thus, we get a constant value for each row in the third column. This tuple is like the absolute value in the Excel example.

So let’s ponder tuple behavior in query results again for a moment. Simply put, a tuple resolves as by combining the current column member, the current row member, and whatever members are in the WHERE clause (aka the slicer). If a member on rows, on columns, or in the WHERE clause is a calculated member, then whatever member the calculated member references in the expression will be overridden in the current row, column, or slicer member. If the expression is complex – that is, composed of operations on multiple tuples – then the contents of each tuple is evaluated independently. Thus, in the percent of total calculation, we have the numerator changing dynamically as row members change but the denominator remaining constant.

Putting the pieces together, we get the final query as shown below. Notice the FORMAT_STRING property added to the Pct of Total expression to get a percentage format. It’s not necessary for Numerator or Denominator because the use of Sales Amount directly (rather than as part of a computation) results in the use of the currency format string as defined for the measure in the cube.

WITH
MEMBER [Measures].[Numerator] AS [Measures].[Sales Amount]
MEMBER [Measures].[Denominator] AS ([Measures].[Sales Amount], [Product].[Category].[All Products])
MEMBER [Measures].[Pct of Total] AS [Measures].[Numerator] / [Measures].[Denominator], FORMAT_STRING='Percent'
SELECT
{[Measures].[Sales Amount],[Measures].[Numerator],[Measures].[Denominator],[Measures].[Pct of Total]}
ON COLUMNS,
[Product].[Category].Members ON ROWS
FROM [Adventure Works]

SQL Community

Early arrival meant Erika and I were both “interviewed” about SQL Community, which was displayed at the opening of the first day’s keynote on Wednesday, October 12. Both of us were also in the Welcome to PASS Summit video.

Connect. Share. Learn.

That was the theme for this year’s Summit. And Erika and I did plenty of each. I connected with friends made at last year’s Summit, friends from my very first Summit in 2002, and of course friends made along the way. It was especially fun to connect with people whom I knew until now only through Twitter. Some people were taller than I imagined, and some were shorter – but I’m not confessing any further details here! One feature of this year’s Summit that I thought was executed quite well was the attention given to first timers. The blogging community provided help in advance, first timers were given special ribbons to wear, and a special reception was held to make first timers feel welcome and learn the ropes. Then they were turned loose into the Summit community! I spoke with many first timers throughout the week and there was much appreciation expressed for the extra effort to help them get acclimated.

For sharing – I participated in several events. First, there was my pre-conference session on Tuesday which had a lot more people than I expected frankly for the somewhat niche topics of MDX, DAX, and DMX. If you were in my class (and even if you weren’t), you can download the demonstration files here, plus in the MDXPrecon.zip file I included the queries that I managed to save off from our detour discussions:

Then on Thursday, Denny Cherry (Blog | Twitter) and I co-presented “So How does the BI Workload Impact the Database Engine?”. We had some great questions from the audience and still managed to get through our content. Originally, we had thought this was a 4-hour session, but discovered a few weeks prior to the Summit that it was only a 3-hour session. I think we did a decent overview and certainly could have talked at least another hour, if not an entire day drilling into the details. Denny put up  a Web page containing links to resources to get background information on the topics we covered in this session.

On Friday, I was super busy with my Connect and Share parts of the program. I hosted a Birds of a Feather table where we discussed Collaborative and Mobile BI. I was surprised how many people chose to be at the table rather than land there by default, considering this topic is still somewhat futuristic! Mostly the conversation centered around Mobile BI. No one here is actively using it yet, although there is interest. We discussed the current offerings in the marketplace, the announcement of Microsoft’s mobile BI strategy, and the challenges everyone has in delivering mobile BI to their users.

After lunch, I dashed off to Starbuck’s for a mini-reunion with my Aspirity colleagues with whom I worked since 2000 (well,  most of them!). Some of us have moved on to independent consulting careers, while others remain with Hitachi Consulting, which acquired Aspirity in 2004. From left to right, Dave DuVarney (Twitter), Dan Reh, me, Hilary Feier, Susan O’Connell, Scott Cameron (we started working together pre-Aspirity in 1997), and Paul Turley (Blog | Twitter) (who joined us after the photo shoot!).

Another connecting/sharing activity on Friday was the panel session, Are You a Linchpin? I was on the panel with Brent Ozar (Blog | Twitter), Jeremiah Peschka (Blog | Twitter), Louis Davidson (Blog | Twitter), Kevin Kline (Blog | Twitter), Andy Warren (Blog | Twitter), and Thomas LaRock (Blog | Twitter). The session was moderated by Andy Leonard (Blog | Twitter). We had a very interesting, interactive discussion about professional development and how one becomes indispensable in today’s fast-paced world. The jumping off point for the discussion was a book by Seth Godin, Linchpin: Are You Indispensable?

Afterwards, Erika reminded me about a tweet I had shared recently. She was just waiting for me to bring it up during the panel discussion, but it had long ago fluttered out of my mind! However, I agree that it was pertinent to the discussion, and am including the link here for those interested. The article, Why Do Some People Learn Faster?, discussed failure as a learning tool. It makes me feel better about all those mistakes I make. I’m just learning faster that way! And you’ll hear this as a common refrain in my classes. I don’t think we learn anything when software, processes, technology, (fill in the blank) just works. It’s only when something breaks that we have the opportunity to investigate to see why it broke and how to keep it from breaking again. That’s true learning.

What else did I learn? Lots of new stuff coming from Microsoft next year, although that’s not huge news to me as I’ve already started writing about SQL Server 2012 (Denali). You can download a draft of the eBook that Ross Mistry and I are co-authoring, Introducing Microsoft SQL Server Code Name Denali. The final book is due out next year.

But I also learned a few new things at the keynotes as well. You can catch my highlights of the keynotes in my previous posts:

• Day 1
• Day 2
• Day 3

There was so much else going on during the week that I can’t possibly capture it all here. I’m looking forward to catching the replay of many sessions as I didn’t get a chance to attend as many as I would have liked. Apparently, my plan to clone myself (via Erika) didn’t enable me to absorb more knowledge. On to Plan B…

SSIS Rules – Green is good. Red is bad. Yellow means wait.

Is there anyone left to tag? Rob Farley (blog | twitter) is always amusing. I’d love to see what he can do with 11 words!

Click here to view the Webcast (after registering with Idera) and click here to download the sample package. Although I created the package using Business Intelligence Development Studio for SQL Server 2008 R2, it will probably work with SQL Server 2008 also, but definitely not with SQL Server 2005.

To execute the package, you will need to download and install the AdventureWorks 2008 R2 sample database and then follow my example in the Webcast to create a data mart. It might work with earlier versions, but is untested.

I will confess to a blooper in the demonstration. I forgot to add in the RegionSourceKey (which is visible in the slides that summarize the steps) to the SalesTerritory dimension, which is required for the FactSales package to execute.

Digression alert…Once upon a time, I owned a copy of Olivia Parr Rud’s book Data Mining Cookbook. I say “once upon a time” because many years ago I had a collection of data mining books that I would take to my Analysis Services classes for students to peruse on the day that we tackled the data mining component. I kept the books in a bag in the trunk of my car, but one day I set the bag in the garage to make space in the trunk and then forgot about the bag. My husband assumed that a bag of books in the garage must mean that my intent was to dispose of the books, so he set the bag out for the garbage collector. It took me a long time to overcome my shock and horror that someone would actually throw books away. (Confession: I hoard books.)

Having read Data Mining Cookbook, I expected Rud’s second book to provide the same type of “how-to” explanation that I found in her first book. It didn’t take me long to realize that my expectations were all wrong.

So what is the book about? The first part of the book concentrates on the state of business in general. There are plenty of problems familiar to those in management positions and BI practitioners cited in Chapter 1, “The Evolving Business Landscape.” One point raised with which I agree wholeheartedly is the acknowledgment that IT for the most part has kept up with advances in technology (such as BI), but business people are often unable to keep the same pace which ultimately leads to failure of the technology.

Overlooking human issues related to the technology is a key contributor to this failure, Rud explains. A survey of the possibilities for addressing this failure is the real premise of the book, rather than a focus on BI as a technology or process. Chapter 2, “Models from Science and Nature,” is an interesting blend of quantum theory, the hive mind, chaos theory, and universality (among others) and a hypothesis that these models might be applied successfully to the business world. But how would you do that?

The goal of Part 2 is to answer that question by enumerating five key competencies that a business must adopt to be competitive and ostensibly to use its information effectively: effective communication, collaboration, innovation, adaptability, and leadership. Each of the five chapters in Part 2 is devoted to one competency. While you won’t find anything that directly relates to BI here, you will find some interesting ideas and brief case studies that you might find helpful if you’re responsible for managing groups of people.

In Part 3, the book transitions to “Models and Practices.” Chapter 8, “Systems Thinking,” is where the book gets technical, but not about BI in the pure sense. Instead, the chapter leads you through the application of systems thinking to business analytics, with lots of diagrams to illustrate recurring patterns commonly found in time-series analysis. The final pages of the chapter explain how this approach ties into BI in general. Chapter 9 introduces “Holacracy,” an innovative way to manage a business that among other non-traditional practices includes “integrative decision-making.”

Part 4 concludes the book with a single chapter, “Possibilities,” which imagines a world in which the practice of Holacracy extends beyond business to community, geographical regions, and beyond. The chapter also includes highlights of a few entrepreneurs that exemplify thinking out of the box and fostering positive change in the world. Whereas the book began with an assessment of the world as we know it, it concludes with an inspired vision of the world as it might be.

In the end, I felt that this book was not really about business intelligence after all. But that was just a matter of my personal expectations. If one considers business intelligence as a way of doing business by interacting intelligently with people, then the book provides ample food for thought and describes interesting aspects of current research in fields that relate directly or indirectly to business management. From that perspective, I think it would make a good addition to the curriculum of an MBA program.

Olivia Parr Rud, Business Intelligence Success Factors: Tools for Aligning your business in the Global Economy, Hoboken, NJ: John Wiley & Sons, 2009. ISBN: 0470392401.

This month’s T-SQL Tuesday is hosted by Jorge Segarra (blog | twitter) who decided that we should write about our favorite new feature in SQL Server 2008 R2. The majority of my published works concentrates on Reporting Services, so the obvious answer for me with regard to favorite new features is…Reporting Services. I can’t pick just one thing in Reporting Services, so instead I thought I’d compile a list of my posts of the new features in Reporting Services 2008 R2:

• Map Wizard for spatial data (The World is But a Stage)
• Pagination features (I’ve Got Your Page Number)
• Lookup functions (Look Up, Look Down, Look All Around – Part I, Part II, Part III)
• Test Connection button (Testing, Testing 1-2-3)
• Conditional formatting based on format, i.e. RenderFormat (As You Like It)

And I wrote an overview of the business intelligence features in SQL Server 2008 R2 for Microsoft Press in the free e-book, Introducing Microsoft SQL Server 2008 R2, if you’re curious about what else is new in both the BI platform as well as the relational engine.