Visualising Power BI Premium And Azure Analysis Services Query Parallelism

In my last post I showed how to connect SQL Server Profiler up to a Power BI Premium dataset but I didn’t give you any examples of why this might be useful. In this post I’ll show you how you can use a Profiler trace to visualise all the queries run by a Power BI report, see when they start to run, see which ones run in parallel with each other and see what the overall time taken to run all the queries is.

Why is this important? When you’re tuning the performance of a Power BI report the first thing to do is to look at the performance of the individual DAX queries run and make them run as fast as possible. However when a Power BI report is rendered any one query is likely to be run at the same time as several other queries run for the same report, and this will have an impact on its performance. How much of an impact there is will depend on how many queries need to be run and the number of back-end v-cores available on your Premium capacity, or the number of QPUs available on your Azure Analysis Services instance if you’re using a Live connection to AAS. The more v-cores/QPUs you have available, the more of the work needed for a query that can be run in parallel; you can see a table listing the number of v-cores for each Premium SKU here, and the number of QPUs for each Azure Analysis Services SKU here. As a result of this if you have reports with a large number of visuals that generate slow DAX queries, scaling up your Power BI Premium capacity or AAS instance may improve overall report performance. Reducing the number of visuals on your report and/or reducing the number of visuals needed to display the same information will also reduce the number of queries that need to be run and therefore improve overall performance.

As I showed last week, SQL Server Profiler can be used to create a trace that logs all the queries run against a Power BI Premium dataset in the same way as it can be used with Azure Analysis Services. Assuming that you have a trace running that uses only the Query End event, this will give you a list of all the queries that are being run along with their start time, end time, duration and a lot of other interesting information. A table with all this data in can still be difficult to interpret though, so I built a Power BI template for a report that visualises all these queries and helps you understand the amount of parallelism that is taking place. You can download the template file here.

To use it, first you need a trace file. Make sure that no-one else is running reports on the Premium capacity you want to test (creating a Power BI Embedded capacity for testing purposes is a good idea) and then, when the trace is running, refresh your report using the technique I described in the “Use the network tab” section of this blog post. This will also allow you to correlate what you see in the trace with the information you see in the DevTools tab in the browser.

Then save the trace file you can created to XML by going to File/Save As/Trace XML File:

SaveToXML

Next, open the Power BI template file and when prompted, enter the full path of the trace XML file you just created:

TemplateOpening

A new Power BI report will then be created. If you want to point the report to a different trace XML file all you need to do is change the value of the TraceXMLFile Power Query parameter.

On the first page you’ll see the name of the trace XML file you connected to plus a bar chart showing each Query End event (with each query identified by a number) on the y axis and the duration of each query on the x axis:

Waterfall

It’s not quite a simple bar chart though. What I’ve done is:

  • Found the start time of the first query run
  • Calculated the start time of every other query in the file relative to this first start time (although, unfortunately, Profiler only gives you start times rounded to the nearest second which means you can’t know exactly when a query starts)
  • Created a stacked bar chart where the first value in the stack is this relative start time and the second value is the duration of the query in seconds
  • Made the colour of the relative start time transparent, so you only see the blue sections of the bar for the query durations. This gives you a waterfall-like effect and allows you to see which queries are run in parallel. This also makes it easy to see the total amount of time taken to run your queries, from the start of the first query to the end of the last query, which is just as useful to know as the duration of any single query.
  • There’s also a drillthrough page so you can right-click on a bar and see a table with the DAX query for the query you clicked on, as well as its start time and duration.

It’s a very basic report, I know, and I would be interested to know if you have any ideas about other ways of visualising this data. What’s more, a visual like this raises more questions than I know how to answer… yet. For example, one thing I want to investigate is the effect that query interleaving has on this graph and both perceived and actual report performance. So stay tuned for more blog posts on this subject!

 

 

 

Excel Dynamic Arrays And The CubeSet Function

Among all the exciting announcements made at Ignite last week, one you may have missed (even in all the Excel-related announcements here) was that dynamic arrays have finally reached GA. Ever since they were announced I’ve been interested in how they can be used with Excel cube functions, which allow you to get data from Analysis Services, Power BI and the Excel Data Model/Power Pivot into cells on the worksheet, and I’ve blogged about this once already. Even though right now the CubeValue function can’t be used with dynamic arrays – alas – there are still some interesting ways dynamic arrays and cube functions can be used together and in this post I’ll show you another one I’ve found.

Say you have the following set of tables in the Excel Data Model:

image

And the following measure that counts the number of customers who bought something:

Distinct Customers:=DISTINCTCOUNT('Internet Sales'[CustomerKey])

Let’s also say you have on a worksheet the following table of data showing the number of distinct customers broken down by product, created using the CubeMember and CubeValue functions:

image

Here are the formulas for this table:

image

So far, all very straightforward. Now let’s say you want to use a dynamic array to filter the rows in this table so you only see the products that have more than a certain number of distinct customers, and you want to see a grand total for this filtered list of customers, like so:

ExcelDynamicArray

Here are the formulas to achieve this:

image

To explain what’s going on here:

  • Cell C2 contains the text “ThisWorkbookDataModel”, the name of the connection to the Excel Data Model. Doing this makes the other Excel formulas here much easier to read!
  • Cell F2 – which I’ve turned into a named range called FilterThreshold – contains the number of customers which a product must exceed to be shown.
  • Cell E5 contains the dynamic array Filter function that filters the rows shown in the first two screenshots above and only returns the products that exceed the threshold. The formula is:
    FILTER(B5:B134,C5:C134>FilterThreshold)
  • Cell E6 does something similar to E5 but returns the number of distinct customers for each product. If all I wanted was the products and the number of distinct customers I wouldn’t have needed two separate formulas, I could have used one, but I need to add the grand total too…
  • Cell E4 contains the formula:
    CUBESET($C$2, E5#, “Total”)
    This creates a set from the spill reference E5#, which contains the cells containing CubeMember formulas returned by the Filter function. For some reason CubeSet returns an error if you try to use a dynamic array formula in its second parameter but using a spill reference works ok; this is why I had to use separate formulas in E5 and E6.
  • Finally, cell F4 contains the formula that returns the total distinct count for all products returned in the filtered list using CubeValue and the set created in cell E4:
    CUBEVALUE($C$2, “[Measures].[Distinct Customers]”, E4)
    Since this is a distinct count the only way to get the correct value here is using CubeSet, CubeValue and the DAX measure – there would be no way to calculate the correct value using Excel formulas. The same could be said of almost any other DAX measure that wasn’t a simple aggregation.

You can download the sample workbook for this post here.

Power BI Report Builder And RSCustomDaxFilter

If you’re building DAX queries using Power BI Report Builder you might notice something that looks like a new DAX function called RSCustomDaxFilter. For example, here’s a simple DAX query built from the Adventure Works Tabular demo database, with one measure and one parameter built Calendar Year:

image

Note that the “Enable Multi Value Parameters” option has been selected. Here’s what you’ll see if you view the text of the DAX query in the Query Designer:

EVALUATE 
SUMMARIZECOLUMNS(
RSCustomDaxFilter(
 @DateCalendarYear,
 EqualToCondition,
 [Date].[Calendar Year],
 Int64
 ), 
"Internet Total Sales", 
[Internet Total Sales]
)

But what is RSCustomDaxFilter? If you run your report and see what happens on the server using SQL Server Profiler, here’s the query that actually gets run (in this case I selected the years 2013 and 2014 for the parameter):

EVALUATE
SUMMARIZECOLUMNS (
    FILTER (
        VALUES ( 'Date'[Calendar Year] ),
        ( 'Date'[Calendar Year] = VALUE ( "2013" ) )
            || ( 'Date'[Calendar Year] = VALUE ( "2014" ) )
    ),
    "Internet Total Sales", [Internet Total Sales]
)

What has happened is that RSCustomDaxFilter has been replaced with an expression using the DAX Filter() function that implements the filter on the selected years; it’s just a placeholder for a dynamically-generated DAX expression that is substituted in at runtime.

Why is it needed? Handling multi-value parameters is difficult in DAX when you don’t know how many values are going to be passed to the parameters (it’s a subject I’ve blogged about here and here) and some kind of dynamic code generation is a reasonable solution to this problem.

There is one drawback with this approach though – it can generate a DAX query that is too long to be executed. Here’s the error message you’ll see:

image

The specified query is too complex to be evaluated as a single statement.

I’m not sure what the maximum length of a query is in DAX – I suspect it’s 32768 characters. You’ll hit this limit if you create a parameter on a field with thousands of values in and then select all but a few of these values; from the example above you can imagine how long the resulting DAX query could be:

image

There’s no single workaround for this but some custom DAX (such as the example here) will be necessary; you’ll just need to ensure that the DAX query is as short as possible.

Excel PivotTable Query Performance With Analysis Services, Power Pivot And Power BI (Revisited)

Back in 2016 I wrote the following blog post about changes to the way Excel 365 generated MDX queries for PivotTables connected to Analysis Services, Power Pivot/the Excel Data Model and Power BI datasets:

https://blog.crossjoin.co.uk/2016/07/08/excel-2016-pivottable-mdx-changes-lead-to-big-query-performance-gains/

I know it sounds boring and not something you need to worry about but trust me, this is important – these changes solved the vast majority of Excel PivotTable performance problems that I encountered when I was a consultant so you should read the above post before continuing.

Unfortunately, earlier this year these changes had to be partially rolled back because in some rare cases the queries generated returned incorrect results; this means that you may find that values for subtotals and grand totals are again being returned even when they aren’t being displayed. The good news is that you should still be able to get the improved performance with a few minor tweaks.

Using the example from the previous post, a PivotTable connected to SSAS MD and the Adventure Works cube (the queries generated for SSAS Tabular, Power Pivot and Power BI may be slightly different and slightly better but the basic problem is the same), if you build the following in Excel:

image

image

…even though the subtotals in the PivotTable are not displayed in Excel, the MDX query generated by Excel not only returns them but also returns others that are not needed at all – in fact 36 rows (although the query returns them as columns) of data rather than the 13 rows that are displayed. Here’s a screenshot of the results returned when you run the MDX query in SQL Server Management Studio:

image

image

Any time you see a row or column containing an All Member (in this case All Customers or All Products) you know you are looking at a subtotal or grand total.

[NB The easiest way to get the MDX query generated by your PivotTable is to install the OLAP PivotTable Extensions add-in https://olappivottableextensions.github.io/]

In this case, to get an efficient query, you need to explicitly turn off subtotals and grand totals for the PivotTable in Excel:

image

image

In the PivotTable the only difference you’ll see is that the grand totals are now not displayed:

image

But the query generated now only returns the values that are actually needed, and as a result will be a lot more efficient and potentially a lot faster. In this case, the query now only returns the 12 rows of data displayed:

image

image

Even with subtotals and grand totals turned off there are still some cases where unwanted values will be returned. Take the following PivotTable, where I have used the Calendar hierarchy from the Date dimension on columns and filtered it so I only see the three months in Q1 CY 2012:

image

image

The MDX query generated by Excel requests requests four extra columns with subtotals and a grand total that aren’t displayed:

image

There is a fairly simple workaround though. By changing how the PivotTable is constructed – in this case by not putting a hierarchy on columns but just the Month Of Year attribute, then adding slicers for Calendar Year and Calendar Quarter to control which months are displayed – you can get an efficient query. This version of the PivotTable:

image

image

…results in this query being generated, which only returns the required values:

image

To sum up, then, if you have a PivotTable that seems a bit slow:

  • Turn off subtotals and grand totals unless you really need to see them
  • Look at the MDX query being generated by Excel and see if it is still returning unnecessary subtotals and grand totals, and if it is try rebuilding the PivotTable to get the same results in a different way (for example by not drilling down on hierarchies as in the last example) to get a more efficient query

[All the queries in this post were generated by Excel 365 version 1910 build 12130.20238]

The AutoSetDefaultInitialCatalog Analysis Services Server Property

In Shabnam Watson’s recent blog post on a bug she found when trying to create a Live connection from Power BI to Analysis Services she mentioned that the AutoSetDefaultInitialCatalog server property could be used to solve her problem. This piqued my interested because I’d seen this property but had no idea what it did exactly or why it was there. Luckily, now I work for Microsoft, it’s even easier for me to find out about things like this from the dev team and Akshai Mirchandani was able to help.

First of all, what does it do? The documentation on this property has just been added here, and this is what it says:

AutoSetDefaultInitialCatalog
A Boolean property. When set to true, new client connections automatically default to the first catalog (database) the user has permissions to connect to.
When set to false, no initial catalog is specified. Clients must select a default catalog prior to running queries or discover operations against a database on the server. If no default catalog is specified, an error is returned. If Initial Catalog property is specified in the connection string, the default catalog will be applied from this property.

The default value for this property is true.

Let me illustrate what this means. Say you have an instance of Analysis Services (in this case it’s Tabular, but it could be Multidimensional) with two databases on it:

image

I’ve expanded the Roles tab for each database reasons that will become clear later.

Next, let’s say you run a simple trace on this server looking at the Discover End and Session Initialize events:

image

…and while this trace is running, you open up SQL Server Management Studio and connect to the SSAS instance. Here’s what you see in Profiler:

image

Now, just to be clear, all I did was open up SQL Server Management Studio and connect to the instance. I did not open up a DAX query window or anything like that; all that happened was the list of databases on the instance was displayed in the Object Explorer pane.

image

The interesting thing to notice from the trace above is that when I did that there are five Session Initialize events and even though the Database column in Profiler is blank, you can see from the list of role names in the TextData column that in each case a connection has been made to the Adventure Works Internet Sales database.

This is because when you open a connection to Analysis Services and do not set the Initial Catalog connection string property, what happens is that you will get a connection to the default database on the instance. Which database is the default? It’s just the first database that the user has permission to access on the instance, which is a bit random.

This happens at other times too. Let’s say you right click on the EmptyDB database and process it in SQL Management Studio:

image

Here’s what I see in Profiler:

image

In this case there are three connections to the default database, Adventure Works Internet Sales, when the database I am processing is EmptyDB!

Most of the time these unnecessary connections have no impact at all but sometimes they can cause problems such as the ones Shabnam describes in her blog post. For example:

  • It can cause performance problems, because there is an overhead to opening a connection – for example roles are evaluated when a connection is opened
  • Monitoring and auditing gets complicated because, as you can see from the traces above, there are a whole lot of connections to the default database taking place that you aren’t expecting
  • Most importantly, when a connection is opened a read-commit lock is acquired on that database and in a few rare cases this can cause deadlocks and other locking-related issues

This is why the AutoSetDefaultInitialCatalog server property was introduced. With this server property set to False, when you open a connection to SSAS with no Initial Catalog set, then you get a connection with no database set. You can find this server property in SQL Server Management Studio in the Analysis Server properties dialog (which you can find by right-clicking on your instance name, selecting Properties, and going to the General tab) and checking the Advanced (All) Properties box.

image

With AutoSetDefaultInitialCatalog set to False, here’s what Profiler shows when I rerun my original test of connecting to SQL Server Management Studio:

image

Note that there are now no Session Initialize events now.

Here’s what opening up a new MDX query window in SQL Management Studio shows with AutoSetDefaultInitialCatalog set to False if you don’t explicitly set a database when you connect:


image

image

Note the empty database dropdown box on the toolbar and the “Error loading metadata: No cubes were found” error message shown in the Metadata pane.

So why didn’t the dev team set AutoSetDefaultInitialCatalog to False by default on new instances? The problem with doing this is that it is a potential breaking change that could cause errors in some client tools. I’m not aware of any specific cases where this might happen but if you did decide to change AutoSetDefaultInitialCatalog to False on your instance you would need to test thoroughly to make sure it didn’t break anything. My feeling is, though, it is probably a good idea to AutoSetDefaultInitialCatalog to False on production servers and do the appropriate testing just in case those unnecessary connections are causing problems.

Setting Azure Analysis Services Server Properties Not Visible In SQL Server Management Studio

Users of on-premises Analysis Services will know that most of the useful server properties can be set in SQL Server Management Studio, some (such as MaxIntermediateRowsetSize) can only be set by editing the msmdsrv.ini file. How do you set these properties in Azure Analysis Services though, when there is no msmdsrv.ini file to edit?

The solution is to use an XMLA script to make the change. The easy way to do this is to open up the server properties dialog in SQL Management Studio by right clicking on your instance name in the Object Explorer and selecting Properties:

image

Then, in the server properties dialog, change any server property but do not click ok. Instead, click on the Script button and then select Script Action to New Query Window:

image

This will create a new XMLA query window in SSMS (the connection dialog for this window will be open too, which will freeze the server properties dialog, so you’ll need to either connect or dismiss the dialog to close the server properties dialog) with the XMLA script to make the server properties change you made. The actual change won’t take place, though, unless you execute the script – so don’t do that.

Instead, change the name of the server property in the script to the one you actually want to set and enter the value you want to set it to:

image

Note that you can’t just enter the name of the server property in most cases because server properties can be grouped into sections, so you’ll need to enter the section names too. For example for the MaxIntermediateRowsetSize property you’ll need to enter DAX\DQ\MaxIntermediateRowsetSize.

After that, all you need to do is hit the Execute button and the change will be made.

DAX Median() Function Does Not Work On Tables With More Than 2 Billion Rows

An interesting – if obscure – fact I learned recently is that a small number of DAX functions such as Median() do not work on tables with more than 2 billion rows in Analysis Services Tabular, Azure AS and Power BI.

It’s quite easy to reproduce in Power BI. The following M expression returns a table with two billion and four rows:

let
    Source = 
    List.Repeat(
        {1,2,3,4},
        500000001
        ),
    #"Converted to Table" = 
    Table.FromList(
        Source, 
        Splitter.SplitByNothing(), 
        null, 
        null, 
        ExtraValues.Error
        ),
    #"Changed Type" = 
    Table.TransformColumnTypes(
        #"Converted to Table",
        {{"Column1", Int64.Type}}
        )
in
    #"Changed Type"

It takes some time to load this table  – around twenty minutes – but because there are only four distinct values in the table the resulting .pbix file is only 31KB thanks to the way Power BI compresses data.

If you load this table into your dataset, call it VeryBigTable and create the following measure:

Median Test = MEDIAN(VeryBigTable[Column1])

…and use the measure in a visual, you’ll see the following error:

image

The current query cannot be evaluated for the ‘VeryBigTable (42)’ table, because the table contains more than two billion rows.

What’s more, the error will always occur even if you apply a filter to the table that returns less than two billion rows. The same problem occurs with some other functions, such as Percentile(), but it’s worth pointing out that the vast majority of DAX functions work as normal with tables with more than two billion rows – for example, in the pbix file used here the Sum() and CountRows() functions not only work fine but return instantly.

Luckily, in the case of the Median() function, there is an easy workaround because you can calculate a median in other ways such as the one described on the DAX Patterns site here. The code is a lot more verbose but it works on a 2 billion+ row table.

image

SSAS Tabular 2019, Calculation Groups And Migration From SSAS Multidimensional

With the release of CTP 2.3 of SQL Server 2019 today there was big news for Analysis Services Tabular developers: Calculation Groups. You can read all about them in detail in this blog post:

https://blogs.msdn.microsoft.com/analysisservices/2019/03/01/whats-new-for-sql-server-2019-analysis-services-ctp-2-3/

In my opinion this is the most important new feature in DAX since… well, forever. It allows you to create a new type of calculation – which in most cases will be a time intelligence like a year-to-date or a previous period growth – that can be applied to multiple measures; basically the same thing that we have been doing in SSAS Multidimensional for years with the time utility/shell/date tool dimension technique. It’s certainly going to solve a lot of problems for a lot of SSAS Tabular implementations, many of which have hundreds or even thousands of measures for every combination of base measure and calculation type needed.

I’m not going to repeat any of the detailed technical information in the blog post here, though. Instead the point I want to make is that this is very big news for SSAS Multidimensional users too. In the past couple of years many people who have existing SSAS Multidimensional implementations have thought about migrating to SSAS Tabular so they can take advantage of its new features or move to the cloud, and indeed many of them have already migrated successfully. However, up to now, the biggest blocker for those wanting to migrate from Multidimensional to Tabular has been the fact that some complex calculations that can be expressed in MDX cannot be recreated (or recreated easily and efficiently) in DAX, because DAX has not had an equivalent of calculated members not on the Measures dimension or the MDX SCOPE statement.

Calculation groups do not remove this roadblock completely, but they do remove the roadblock for a large group of existing SSAS Multidimensional users whose only complex calculation requirement is a time utility/shell/date tool dimension. As a result these SSAS Multidimensional users will now be able to migrate to SSAS Tabular 2019, Azure Analysis Services or Power BI if they want to. Only those people who have more exotic uses for calculated members not on the Measures dimension (which are not very common at all) and those who use SCOPE statements (a larger group – many people working with financial data use SCOPE statements heavily) will find that Multidimensional is still the correct platform for them.

Azure Data Studio Should Support Analysis Services And Power BI Premium Capacities

I’m at the PASS Summit this week, and in this morning’s keynote there was a demo of the newly-released Azure Data Studio  – a modern, cross-platform tool for managing and querying SQL Server, Azure SQL Database and other Azure data services (it’s carefully described as “complementary to” SQL Server Management Studio rather than a replacement for it; this blog post has a detailed discussion of this question).

This video is provides a good, short overview of what it is:

I think it’s pretty cool, BUT… it doesn’t support Analysis Services. I had a moan about this and the generally poor state of Analysis Services tooling on Twitter, was invited to meet some of the developers and was told that if enough people request Analysis Services support it might happen.

What would support for Analysis Services involve? The following springs to mind:

  • I’d like to be able to connect to and manage Analysis Services Multidimensional and Tabular on-premises and Azure Analysis Services; if that’s too ambitious I could settle for supporting only Analysis Services Tabular 2016+ and Azure Analysis Services.
  • Since we will soon be able to connect to a Power BI Premium capacity as if it was an Analysis Services instance via XMLA endpoints, I would want to be able to connect to Power BI Premium capacity too.
  • I’d want to be able to run DAX and M queries, and ideally MDX queries too.
  • I would also want to be able to work with ASSL and TMSL for scripting and editing objects.
  • Azure Data Studio has a Profiler extension that works on xEvents; it would be great if that worked with Analysis Services xEvents too.
  • DAX and M Jupyter notebooks would be really useful!
  • It would make sense for some of the functionality of existing tools like DAX Studio and BISM Normalizer being turned into extensions.

If you want to see Analysis Services support in Azure Data Studio, go to the following issue on the Azure Data Studio GitHub repository:

https://github.com/Microsoft/azuredatastudio/issues/1026

…and click the thumbs-up icon on the first post:

AzureDataStudio

Let’s make our voices heard!

 

 

A Quick Look Some Power BI And SSAS-Related Products And Books

I  don’t like writing reviews of books or products here on my blog for a couple of reasons, the main one being that I don’t usually have the time to read/test/understand something properly so I can write a thorough review. That said I do get sent a lot of free books and evaluation licences for products that deserve a wider audience, so I thought I would write a post rounding up some of them along with a few thoughts of my own.

Custom Visuals

One of the most interesting questions related to Power BI is whether third-party software companies will be able to build businesses selling extensions to it. The most obvious way that Power BI can be extended is through custom visuals and there are several companies that have paid-for (as opposed to free) custom visuals. Zebra BI is one such company and I’ve been really impressed by what they have produced for visualising financial data:

image

I also saw recently that OKViz (part of the Marco and Alberto/SQLBI family) now have a paid-for version of their excellent Smart Filter visual with some premium features – see here for more details; similarly new features in Klaus Birringer’s Ultimate Waterfall and Ultimate Decomposition Tree visuals are only available in the paid version.

I know many Power BI users who use custom visuals have suffered with various bugs and limitations in functionality over the past few users, and I think buying commercial custom visuals rather than relying on free equivalents is one way of dealing with reliability and support problems. It’s certainly in Microsoft’s interests to have a thriving partner community in this space given that flashy visuals are a major selling point of the product. But will Power BI users want to pay for visuals when so much is available out of the box for free, especially when the cost of the visuals seems relatively high when compared to the overall cost of Power BI? I guess we’ll see.

Custom Connectors

A lot of what I’ve just said about custom visuals also applies to custom connectors, although custom connectors are a lot less mature (at the time of writing, support for custom connectors in the on-premises gateway is still in preview). However I was pleased to see this announcement from CData software that they now have over 100 custom connectors available for Power BI. It looks like what they have done is wrapped their existing ODBC providers, and as a result some of their connectors are for sources that are already available in Power BI, but even so there are a lot of new data sources here.

Incidentally, I got very, very excited when I realised that the CData connectors for Excel and Excel Online supported DirectQuery mode as well as import mode. Why, I hear you ask? Well, just think about a planning/budgeting solution where users can enter data into an Excel spreadsheet and when the numbers change in Excel, the numbers change in Power BI too; think also how this could work with Composite Models. I tried this with CData’s Excel on-premises connector and unfortunately it returned errors when the source Excel worksheet was open; I did get it to work with the Excel Online connector but it was painfully slow, even with a small amount of data. If I can get it to work better (and I may be missing some optimisations within the connector) I’ll blog about it.

Books

One of the few Power BI-related books that have been published recently is Phil Seamark’s “Beginning DAX with Power BI”. He was kind enough to send me a review copy; it’s a good introduction to the subject and I particularly like the way he introduces DAX variables early one. Definitely worth a look if you’re just starting to learn DAX.

I was also sent a copy of a slightly older book, David Parker’s “Mastering Data Visualization with Visio 2016”. David knows pretty much all there is to know about using Visio for BI (his blog is great) and while this book doesn’t cover the most exciting new development in this area – the Visio custom visual for Power BI – if you want to learn all the advanced features of Visio that you could take advantage of in Power BI then this is the book to get.

Other Products

I’ve been a big fan of SentryOne’s SSAS monitoring tool, BI Sentry, for years now but up until recently it only supported SSAS Multidimensional. It now supports SSAS Tabular too (details here), and it looks like SentryOne have done a great job of adapting it to the specific needs of the Tabular engine. I always advise my SSAS customers to invest in some kind of monitoring solution because it makes the job of detecting and solving issues like poor query performance so much easier, and to be honest BI Sentry is better than anything you would be able to build yourself.

Moving onto Power BI, if you need to generate documentation for your Power BI datasets and reports check out Power BI Documenter; the August release looks like it has some cool new features. Alternatively the latest release of Power BI Helper also allows you to generate documentation as well as lots of other useful stuff.

Something that hasn’t been properly released yet, but will be incredibly useful when it is, is MAQ Software’s Application Lifecycle Management Toolkit for Power BI. Closely related to BISM Normalizer, it will allow you to compare two Power BI datasets, merge changes, deploy only parts of a dataset (for example individual measures), and deploy to multiple datasets – all of which are things Power BI developers have been crying out for.

Last of all, the guys at DevScope also have a new(ish) product out, Power BI Robots, which automatically takes screenshots of Power BI reports and dashboards and can deliver them to various destinations such as email address and SharePoint. I haven’t looked at it yet but it seems like it could have a lot of interesting uses.

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