If you have slow queries when reporting table format data in Excel from PowerBI.com, Azure Analysis Services or Tabular SSAS, this could be just the performance boost you need.Continue reading “Faster Excel Queries For Power BI & Azure Analysis Services”
Most of our customers use XLCubed for reporting, analysis and data visualisation, but an increasing number use it for data updates, either within budgeting and forecasting solutions or in niche applications.
XL3RunSQL is a neat method for running SQL updates from Excel, and allows users to build input forms (which can ultimately be web-enabled) quickly and easily.
In this blog, we will walk through the steps to set this up.Continue reading “Updating SQL Tables from Excel”
Did you know how easy it is to search for members within XLCubed?
In an ideal world for report designers, all the data required for a report is available in one data source, and the structure and hierarchies perfectly match the reporting needs. Sadly, the world isn’t always ideal (just ask Theresa May…).
We often see scenarios where users are reporting numerical data from Analysis Services but want to include descriptive or textual information held elsewhere. People have approached this in different ways, but XLCubed’s mashup capability can make it a much more streamlined and maintainable process.
Version 9 of XLCubed introduced Pivot-views, which allow our powerful grids, small multiples and slicers to be driven directly from lots of different relational sources.
We also added support for Power BI as a data source, and many of our customers are now using XLCubed on Power BI data for a better in-Excel experience and slice and dice.
Many of you will already be familiar with the data refresh options in Power BI, essentially either a scheduled refresh process, or through the Enterprise Gateway.
With pivot-views we wanted to ensure that the data refresh process is very straightforward, and to enable business users to benefit from them without worrying about scheduling data flows or getting involved with discussions about gateway configuration. Fundamentally, the data will automatically update when the report is opened or if any of the query parameters change (either by updating a slicer or if an Excel cell-based parameter value changes).
This means the report builder doesn’t have to worry about the latest view of the data as that is all handled automatically without any custom code, manual intervention or IT assistance.
Here is how it works in detail.
First insert a grid and select a relational database as the source.
We are presented with the query designer, we can pick a table and setup a parameter by entering “@productgroup”:
This gives us an option to link that parameter to a cell or directly to a slicer:
We can now design the grid we want to see:
And view the result.
Now, if I update the driving cell:
The query is re-run and the grid (and anything else driven from the data has updated to reflect the new query results):
This automatic handling of the data refresh continues once the report is published to XLCubedWeb. Whether accessing from Excel, a browser or the XLCubed Mobile App, the report is based on current data without the need for any additional handling or refresh processing… One less thing for the report designer to worry about, one less thing to go wrong!
XLCubed has supported a drag/drop interface for creating reports against Tabular Analysis Services since the first release of the new engine. It lets users easily create reports which run DAX queries on the cube, and we’ve often seen very good performance at customers when MDX against Tabular was a cause of long running reports.
So when we were approached at SQL Pass in Seattle by some attendees who had a SSAS Tabular performance issue we were optimistic we’d be able to help.
In this case the business wanted to retrieve thousands of rows from the cube at the transactional level, and the first approach had been to use PivotTables in Excel. To get to the lowest level they cross-joined the lowest levels of all the hierarchies on the rows section which would give the right result, but performance was terrible, with several queries taking 20 minutes or more and others not returning at all.
We hoped using an XLCubed table running DAX would be the solution and created the same report in the designer. Sadly while performance was a little better it was still far from acceptable; the model was large, and the number or columns combined with their cardinality meant that a lot of work was being done on the server.
XLCubed’s DAX generator was trying to cross-join all the values from each column, which had worked well for our other customers. But when there are a dozen columns including the transaction ID things do not go so well. DAX in itself is not a magic bullet and SSAS Tabular models can hit performance problems on low level data – we needed a new approach.
After some investigation we discussed the issue and our thinking with our friends at SQLBI and determined that instead of cross-join we wanted an option to use Summarize() instead as this only uses the rows in the database, and it can access columns related to the summarized table which were required for the report.
As the customer’s report had the transaction ID in it the result wasn’t aggregated, even though we were using summarize. But we wanted to add true transactional reporting too, using the Related() function.
Finally, SQL 2016 adds a couple of new functions, SummarizeColumns() and SelectColumns(), both of which are useful for this type of reporting, but offer better performance than the older equivalents.
The end result in XLCubed is a new option for DAX tables to allow users to set the type of report they want to run, and some internal changes so that XLCubed will automatically use the most efficient DAX function where they are available.
A beta was sent to the business users and the results were fantastic. The report which had run for several minutes now completed in a few seconds, and 20 minutes was down to 15 seconds – we had some very happy users!
The changes will be in the next release of XLCubed so that all our customers can benefit from the improvements. It’s always nice when a customer request helps improve the product for everyone.
A sample of the syntax change is included below
EVALUATE FILTER ( ADDCOLUMNS ( KEEPFILTERS ( CROSSJOIN ( VALUES ( 'Customer'[Education] ), VALUES ( 'Product'[Color] ) ) ), "Internet Total Units", 'Internet Sales'[Internet Total Units], "Internet Total Sales", 'Internet Sales'[Internet Total Sales] ), NOT ISBLANK ( [Internet Total Units] ) ) ORDER BY 'Customer'[Education], 'Product'[Color]
EVALUATE FILTER ( ADDCOLUMNS ( KEEPFILTERS ( SUMMARIZE ( 'Internet Sales', 'Customer'[Education], 'Product'[Color] ) ), "Internet Total Units", 'Internet Sales'[Internet Total Units], "Internet Total Sales", 'Internet Sales'[Internet Total Sales] ), NOT ISBLANK ( [Internet Total Units] ) || NOT ISBLANK ( [Internet Total Sales] ) ) ORDER BY 'Customer'[Education], 'Product'[Color]
#5: Lack of Real-time data exploration
The argument is often made that Excel is too inflexible to answer spur of the moment questions quickly and effectively. The scenario given is that you’re in a meeting with your Excel workbook, and someone asks a related question not currently accounted for. How embarrassing to have to look at your back up folder of printed Excel workbooks… Really? It may escape the attention of some, but Excel is actually an electronic product too so as a first point you wouldn’t need to dust off an enormous binder of printed reports.
That aside, the overall argument has some merit in specific cases. If Excel is acting as both the datastore and the presentation tool you have a problem. If the data you need isn’t in the workbook, you’re bang out of luck.
There are two key requirements to address the issue in Excel. Firstly the data needs to be stored outside the workbook; in the case of XLCubed that’s in AS cubes or tabular models. This means when the data isn’t currently visible in the workbook it can still easily be queried and brought into play.
Secondly, while it’s a huge step having the data in a cube, that in itself isn’t enough. You need to be able to get it out quickly, easily and flexibly and to display it as information rather than just data. There are significant limitations with pivot tables when used to report on a cube and XLCubed addresses these while adding a lot more capability on top. The additional data you need to answer the question is readily available, and you have tools to do something meaningful with it using slice and dice tabular reporting, interactive charting and straightforward user calculations.
So when someone asks the question in meeting, you can explore it interactively and on the spot. And in Excel.
“Excel: Great hammer, lousy screwdriver’”
When evaluating BI tools, many of our customers are hit by marketing messages about the limitations and woes of Excel. One white paper we were pointed to is Tableau’s ‘Excel: Great hammer, lousy screwdriver’. It contains 5 key points concerning Excel limitations for BI which we’ll take a look at over the next few weeks, along with a few others which we hear frequently.
“Don’t throw the baby out with the bathwater”
We fully appreciate that Excel isn’t perfect for all needs, but XLCubed chooses to address the weaknesses and embrace the very significant strengths, rather than throwing everything away.
XLCubed helps users get most value and benefit from Microsoft’s Analysis Services platform by taking the best of Excel, and extending that with an optimised query and reporting environment which lets them do more, and do it more quickly. Excel becomes a very flexible presentation layer, and Analysis Services removes the scalability and data integrity issues.
Let’s take a look at some of the key Excel objections raised, with #2 to follow next week:
#1) Limited data volumes
“Excel only handles 1 million rows – that’s not nearly enough for my business”
The advent of Big data makes everyone think of huge data volumes. In reality if you’re looking at core Financial reporting a million rows may well be more than enough but that’s not the point: for sales and operational reporting over several years a million rows won’t come close. Big Data is partly around volumes, but also concerns the data structure. These days the challenge of big data isn’t the ability to store it, it’s the ability to do something useful with it. And doing something useful with it isn’t creating reports which run to a million rows.
We see Excel as a presentation layer, not as a database. While Power Pivot muddies that argument a little, very few people see Power Pivot models as a central repository for Corporate data. XLCubed is a client front end tool for SQL Server Analysis Services (which laughs at 1 million rows). 1 million new rows per day over several years is starting to ramp up the volume, but the technology is designed to scale, and to scale on significantly less expensive hardware than in-memory technologies (of which Analysis Services 2012 of course now has its own player with xVelocity).
So while Excel and hence XLCubed can only display 1 million rows at a time, the underlying cubes can run to billions of rows. XLCubed gives the user flexible and fast filtering and ranking capabilities, simple ways to leverage the cube hierarchies, and effective data visualisation techniques to let you work with these large volumes of data.
Aside from that, if someone wants a report (a report!) which is a million rows long, our first question is always ‘and can you show me how you use that report?’. If you print it you’ll get around 25,000 pages of deforestation. By comparison Tolstoy’s War and Peace is around 1,400 pages in most print editions… We believe there is a lot to be said for a combination of top-down reporting, and ranking and filtering to make that type of data volume useful rather than burdensome.
So in summary, when you’re using XLCubed and the Microsoft BI stack, more than a million rows of data is really not a limitation (though if you put a million rows in the report you’re creating your own limitation in terms of its usefulness).
With the launch of 7.1 of XLCubed Excel Edition we introduced the ability to import ProClarity Briefing Books – with support for ProClarity ending this year and many customers looking for a replacement, now is a great time for us to show you how the import works to help move users from ProClarity to an alternate solution.
Let’s start with importing from ProClarity, we’ve built a simple example briefing book based on the usual AdventureWorks sample cube, it includes a sample grid:
a performance map:
and a chart:
To get to the import option we load Excel and select XLCubed -> Extras -> Import -> Import ProClarity Briefing book. After selecting the file to import we are given a summary of each item that is going to be imported:
At this point you can control the resulting worksheet name, as well as switching the type of XLCubed object you’ll end up with. Clicking “Import” will now give us 1 sheet for each briefing page:
You’ll notice that the import process has created any required slicers so the report is good to go. You could now also spend a bit more time adding any extra XLCubed functionality to the report such as Incell charts or Excel calculations to leverage the power of Excel or publish to XLCubedWeb for consumption by a wider audience.
The import process is very straight forward and we have some great feedback from our customers regarding the speed and ease that they have been able to migrate users’ reports into XLCubed.
Look out for some more blogs showing other features of XLCubed that will help users transition from ProClarity!
So today’s blog is all about pivoting SQL query data columns.
Here we have a small sample of a SQL query report that shows us actual revenue across different products over a number of quarters. There’s nothing wrong with the data being returned but it is pretty difficult to do any comparison analysis.
So what if your task is to report back on actual revenue across the product categories over all the quarters in this report.
This would not be an easy query to write in SQL as we don’t always know which data will be returned by a query, but that’s where XLCubed can come to our rescue!
We just right-click on the column heading which we wish to pivot – in our case cDate and then select XLCubed and Pivot (XLCubed fills in the column heading) as below:
The report is now displayed as with the quarters across the page as columns.
This format is so much easier to read and we can quickly how each of the products are contributing (or not!) to the company’s revenue across a time period.
So that’s how easy it is to pivot column data in XLCubed.