As the release date for Excel 2013 gets nearer (in fact Home Premium has already arrived) the pricing and package offering are becoming clear(er). For many of us the emerging picture is not pretty, mainly due to a u-turn on Microsoft’s part i.e. removing the PowerPivot add-in as an option for most low-end Office 365 packages and ALL retail versions.
Rob Collie goes into the likely back story (and politics) behind such an obviously (well to those of us at the Excel coal face) stupid move. MS not only led some of her most ardent evangelists up the proverbial “garden path” with PowerPivot under Excel 2010 (never before has a major feature been so restricted in a subsequent version) but also gave a false and misleading impression of the new Office 365 packages during the current trial period (i.e. where all versions have the PP add-in (and PowerView!) as an option). Being lied to is the expression the keeps coming to mind.
There is, however, a silver lining, Excel 2010′s Data Model. If I had never seen the power of DAX Measures (which is, in essence, what lack of the add-in deprives you of) I would be completely bowled over by the data handling power of the Data Model (it is, in effect, the Vertipaq Columnar Engine with all its power and glory – minus the ability to create (but can consume) explicit DAX measures & columns ). But I have, so hence my disappointment
But even without the add-in, the Data Model opens up a whole world of data munging potential.
At its simplest, it makes “VLookup hell” a thing of the past. This alone, as any who have laboured under the tyranny of VLookup (and her cousins Index & Match) will agree, is a huge reason to upgrade to Excel 2013.
Also, all versions of Excel (OK, not all: RT and basic Web Apps do not support the DataModel) can consume a DAX-rich model produced by somebody else who has access to the add-in.
Now before you get too excited, models produced by the free and widely available 2010 version must still be upgraded by the 2013 version before they can be used by 2013′s Data Model. UPDATE: Previous statement not quite true, it is possible to transfer the binary data file holding the model from 2010 to 2013 (but must be a V2 generated model, V1s or V2s upgraded from V1, will not work). Have done a POC and will likely add this facility as a public command to HAMMER in the near future.
It is also possible to create DAX Measures and even Calculated Columns against all Data Model enabled versions using an AMO powered .NET add-in. Last week, being a hacker at heart, I created a POC using Python within my HAMMER micro ETL toolset to do just that. No PowerPivot add-in required to populate even the humblest Excel version with sophisticated DAX models!
And there’s more! You also use DAX Queries to generate tabular output from a plain Data Model, see Chris Webb’s example. Again, a PP-add-in-free-zone
But why the title, why are Star Schema’s back on the menu and were they ever off the menu?
In this post (and the related posts linked to within) I’ve argued that the strict “laws and practices” of dimensional modelling (as popularised by Kimball et al.) can be relaxed (and often ignored). The world that spawned such ‘best practices’ was not a world of DAX powered, or even MDX powered, data models, it was one where the RDBMS’s version of a hyper-cube – the start schema – ruled.
The only tool was SQL (with its limited aggregate functions) and the single-issue star-schema was the only viable data structure. But sophisticated summary tables techniques (requiring an army of DBAs) and new RDBMS optimizers were needed to get a reasonable response time. Plus, much of the work (and most of the cost) was in the ETL phase. That’s the world I cut my BI teeth in. How we would have wished for a modern columnar engine! And now, 20 or so years later, such a thing exists, in a spreadsheet! So dust off your Kimball Dimensional modelling books, the plain-old ETL’d star-schema is back in town!
But what about the ETL required to build such pure stars? Again, add-in-free DAX Query to the rescue! Like this, to create a Time Dimension. Dimensional data could easily be enhanced (e.g. adding ABC type attributes) using similar techniques.
Fact data could be more of a problem (due to the 1M row limit of an Excel table – the output medium of Excel-based DAX Queries). But again, if you have access to even the most basic of relational databases (like the inbuilt SQLite engine in HAMMER) even the shaping of very large fact tables can be handled. For more demanding requirements maybe use AWS’s Data Pipeline ”ETL-in-the-cloud”?
Anybody with experience of using pure stars will of course be aware of the downside i.e. the restrictive nature of the end result. Such data models tend to deal with a limited number of “questions” especially compared to DAX-destined data models (which can be very close to their OLTP sources in structure).
But that can also be an upside, depending on the likely end-users of the “cube”. The simple single-issue pure star-schema is often better suited to an environment where most are “consumers” rather than “explorers” of data.
In any case, it would be nice to have the option to service both end-user types. So please Microsoft, at the very minimum, revert back to the 2010 scenario i.e. PowerPivot as downloadable add-in. If you do, the phrase “Oh, it’s just Excel” will for the history books.