2016-11-29

A semi-additive average? What exactly are you trying to calculate? Let me explain first. A semi-additive measure is a measure that can be summed across some dimensions, but not all. Typically it’s the time dimension that isn’t additive. For example, the stock level at various warehouses. You can add all the stock levels of your warehouses together, to get an idea of how much stock you have for your entire company. However, you can’t add the stock level across time. 250 stock yesterday and 240 stock today doesn’t equal 490 stock for the two days. In reality the sum aggregation is replaced with another aggregation when aggregating over the non-additive dimension. In our stock example, we could use the last value known (240) or the average (245). Which aggregation you want depends on the requirements.

In this blog post I’m going to calculate a semi-additive measure, using the average for the non-additive dimension. Quite recently a colleague asked how you could calculate this in DAX. The use case is simple: there are employees that perform hours on specific tasks. The number of hours is our measure. The different tasks (the task dimension) is additive. The employee dimension however is not when we calculate an average. When two employees are selected, the result should not be the average of all the individual hours, but rather the average of the sum of the hours per employee. Let’s illustrate with an example:

That’s really interesting, and a good bit easier to do than the T-SQL equivalent (at least in one step).

# Finding Relational Data Sources In SSAS

2016-11-29

Whenever you are introduced to a new environment, either because you visit a new client or take over a new position from someone else, it’s always crucial to get on top of what’s going on. More often than not, any documentation (if you are lucky to even get hands on that) is out of date or not properly maintained. So going through that may even end up making you even more confused – or in worst case; misinformed.

In a previous engagement of mine came a request from the Data Architecture team. I was asked to produce a list of all servers and cubes running in a specific environment. They provided the list of servers and wanted to know which servers were hit by running solutions. Along with this information the team also needed all sorts of information on the connection strings from the Data Source Views, as well as which credentials were used, if possible.

If you’re dealing with a large number of cubes, this becomes even more useful.

# NUMA-Aware Tabular Models

2016-11-17

Wow, right on the heals of Azure AS and just when you thought things couldn’t get any better for SSAS geeks of the world… Microsoft releases SP1 for SQL Server 2016… an voila, Tabular is now NUMA-Aware!

# Processing Azure Analysis Services

2016-11-16

This post contains a list of various methods that can be used to process (i.e. load data into) an Azure AS tabular model. As you will see – not much has changed from the regular on-premise version (which is a very good thing as it softens the learning curve).

Read on if you’re looking at putting an Analysis Services model into Azure.

# TMSCHEMA DMVs

2016-11-09

It would be great to get the DMVs documented similar to the MDSCHEMA DMVs as they are quite useful for tasks like documenting your tabular model.  Since the TMSCHEMA DMVs work in Azure Analysis Services as well, I have logged this request on the Azure AS User Voice for that. Please lend me a vote so we can make this information more easily available.

# Capturing SSAS Query Activity

2016-11-04

In most environments, it is trivial to obtain the name of the user who ran each query… all you have to do was capture the [QueryEnd] event in a profiler/xevent trace and pull the information from the [NTUserName] field. However, in environments involving Power BI and the Enterprise On-Premise Data Gateway, there’s a bit more to it.

The main issue is how authentication is handled in this type of architecture. When working with Power BI reports connected to an on-premise data source via the On-Premise Data Gateway, the account of the user running the report is passed as the “EffectiveUsername”. The implication here is that the value shown in the [NTUserName] field of the xevent/profiler trace is going to be the Data Gateway account – NOT the account of the user who actually generated the activity.

# Details On Azure SSAS

2016-11-02

• Developers can use SQL Server Data Tools (SSDT) in Visual Studio for creating models and deploying them to the service.  Administrators can manage the models using SQL Server Management Studio (SSMS) and investigate issues using SQL Server Profiler

• Business users can consume the models in any major BI tool.  Supported Microsoft tools include Power BI, Excel, and SQL Server Reporting Services.  Other MDX compliant BI tools can also be used, after downloading and installing the latest drivers

• The service currently supports tabular models (compatibility level 1200 only).  Support for multidimensional models will be considered for a future release, based on customer demand

Between tabular-only support and the max size being 100 GB (if I’m reading this correctly), they’re not yet ready to push the product hard.  Given that it just came out, that makes sense, and hopefully the training wheels come off.

# Cached Azure Analysis Services Logins

When Azure Analysis Services was announced I had to try it out right away. Of course I didn’t read the instructions properly so when I tried to log in to my Azure Analysis Services instance from SQL Server Management Studio, like an idiot I logged in with the wrong username. The problem is that once you’ve done this, with current versions of SQL Server Management Studio there’s no way of logging out and logging in as a different user. Luckily Igor Uzhviev of Microsoft had a solution for me and I thought I’d share it for anyone else who’s made the same mistake. Here’s what you need to do:

This seems a bit much, but should just be a temporary workaround.

# Semantic Layers

Part 1: Why a Semantic Layer Like Azure Analysis Services is Relevant {you are here}

Part 2: Where Azure Analysis Services Fits Into BI & Analytics Architecture {coming soon}

Fundamentally, Analysis Services serves as a semantic layer (see below for further discussion of a semantic layer). Because the business intelligence industry now embraces an array of technology choices, sometimes it seems like a semantic layer is no longer valued like it once was. Well, my opinion is that for many businesses, a semantic layer is tremendously important to support the majority of business users who do *not* want to do their own data wrangling, data prep, and data modeling activities.

We (I) spend so much time thinking about the Brave New World of massive blobs of semi-structured data that it’s a good idea to step back every once in a while and remember that yes, there is a need for sanitized, easy-to-consume data which answers known business questions.  The percentage of people at a company willing to create an R or Python notebook or run a MapReduce job is typically well under 5%.

# Testing Analysis Services Cubes

2016-10-26

Unit testing in Visual Studio is actually not that hard and can save you a lot pain down the road. The testing framework in Visual Studio offers extensive ways of executing batches of tests. You can group tests by Class, Duration, Outcome, Trait or Project.

When you right-click a test, you get the option to select how you want the tests in the Test Explorer to be grouped.

If you have an Analysis Services cube, definitely read this—testing is a vital part of software development, and automating tests can save you significant time later.

September 2017
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