# Chris Webb's BI Blog

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Alberto Ferrari In my last post about Parent/Child hierarchies, there is a question, in the comments, that I found interesting. Nevertheless, the formula is a complex one and cannot be written in a simple comment. Thus, I am making a follow-up to that post. I am not repeating all the stuff of the previous post so, please, read that before reading this one, to have the necessary background. The question looks a simple one: “If you needed a measure (SumOfLeafAmount) that only shows childrens values, what would it be like?” I was about to answer that “I have a formula that is too large to fit in the margin” but… Fermat did it some years ago and caused a real mess. There two interesting points in this simple question: There are two interpretation of the formula: one is “sum only the leaves”, the other ones is “sum only the children”. Let us start with the set of data we are going to work on. Now, what are the desired results? Let us start with SumOfLeaves, which is pretty easy. Now, the solution.

Kasper de Jonge PowerPivot Blog | Bringing BI to the masses Jen Stirrup's Business Intelligence Blog Ryan Adams SQL Blog La BI et les outils Microsoft DAX Statistical Functions Following on from his first four articles on using Data Analysis Expressions (DAX) with tabular databases, Robert Sheldon dives into some of the DAX statistical functions available, demonstrating which are the most useful and examples of how they work. The Data Analysis Expressions (DAX) language includes a wide range of functions that help you refine your queries when retrieving data from a SQL Server Analysis Services (SSAS) tabular database. You’ve seen numerous examples of DAX functions throughout this series on the tabular model. One set of functions that are particularly useful when working with tabular data are the statistical functions, which support various ways to aggregate and analyze data. Statistical functions include not only the typical standbys, such as Min and Max, but also those that help structure that data into tables, such as AddColumns and Summarize. To follow along in this article, you should have a basic understanding of how to write a DAX query. The Row Function

untitled In this post, I will go into why there is a “big graph anti-pattern”, the fundamentally different kinds of graph processing, how to match the technology to the problem, and what are some successful patterns for scalable graph processing. The big graph anti-pattern is “Throw everything into a big graph and then using the same tools that gave us horizontal scaling for other problems: map/reduce and key-value stores.” There are several fallacies here. There are some fundamental architectural differences in systems for high performance graph traversal and graph analytics, systems for high performance graph pattern matching, map/reduce platforms and key-value stores. The only way to get scaling and high throughput for graph traversal and graph mining is to get the architecture, the software, and the hardware right. Kinds of graph processing. Gathered reads for property and link set retrieval. Graph traversal. There are a few problems. Graph query. When should you scale-out a graph database?

SQL Rocks with Sri Sridharan | SQL Rocks – Blog of Sri Sridharan Articles - SSAS-WIKI Articles There are hundreds of free articles on the web about SSAS. Most articles are of very good quality. The goal of this page is to organize them into a single "book".  Getting Started / Tutorials  Proactive Caching  Cache Warming  Killing SSAS Sessions  Microsoft SQL Server Community Code Samples  XMLA script  PowerShell  Processing  .Net stored procedure  Partitions  Write Back  Aggregations   DMV (Dynamic Management View)  Actions  Analysis Management Objects (AMO)  Captions of calculated members  Displaying OLAP Data  Deployment  Backup and Restore  Detach and Attach Database  Connection String to SSAS  Connecting from SSAS to Oracle, Excel  Connecting from SSAS to Teradata  Sorting dimensional members  Connecting to SSAS from: SSIS, T-SQL, Java, Informatica  HTTP Access  Measure Formatting  Conditional formatting  Default Member

Microsoft OLAP Blog by Hilmar Buchta: Semi additive measures in DAX / BISM Tabular SQL Server Denali | PowerPivot Semi additive measures, i.e. measures that have to be aggregated differently over different dimensions, are commonly used in BI solutions. One example could be stock levels. The following example shows how to implement some of the most commonly used semi additive measures in DAX. In my example I’m using PowerPivot (Denali edition), but the same calculations can be used in a BISM Tabular model in Visual Studio. In order to keep things simple, I’m using just a short table of test data: As you see, we only have two products with monthly stock levels in 2010 and 2011. Although not needed for my semi additive measures, I created additional columns in my PowerPivot sheet for convenient reasons: Year, Month, Day (using the corresponding DAX-function with the same name). Finally, I created a hierarchy named ‘Calendar’ on my newly created date columns: Now we’re ready for the semi additive measures. Average (over time) Let’s start with an easy one, the average over time.

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