MDX with SSAS 2012 Cookbook

MDX with SSAS 2012 Cookbook - Second Edition

Overview of this book

MDX is the BI industry standard for multidimensional calculations and queries. Proficiency with this language is essential for the realization of your Analysis Services' full potential. MDX is an elegant and powerful language, and also has a steep learning curve.SQL Server 2012 Analysis Services has introduced a new BISM tabular model and a new formula language, Data Analysis Expressions (DAX). However, for the multi-dimensional model, MDX is still the only query and expression language. For many product developers and report developers, MDX is the preferred language for both the tabular model and multi-dimensional model. MDX with SSAS 2012 Cookbook is a must-have book for anyone who wants to be proficient in the MDX language and to enhance their business intelligence solutions.MDX with SSAS 2012 Cookbook is packed with immediately usable, practical solutions. It starts with elementary techniques that lay the foundation for designing advanced MDX calculations and queries. The discussions after each solution will provide you with a solid foundation and best practices. It covers a broad range of real-world topics and solutions and provides you with learning materials to become proficient in the language.This book will guide you through the hands-on and practical MDX solutions, best practices, and many intricacies that hide within the MDX calculations and queries. We will start by working with sets, creating time-aware, context-aware calculations, and business analytics solutions, through to the techniques of enhancing the cube design when MDX is not enough. We will then move on to capturing MDX generated by SSAS front-ends and using SSAS stored procedures, and we will explore the whole range of MDX solutions for real-world BI projects.

MDX with SSAS 2012 Cookbook

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Elementary MDX Techniques

Introduction

Putting data on x and y axes

Skipping axes

Using a WHERE clause to filter the data returned

Optimizing MDX queries using the NonEmpty() function

Using the PROPERTIES() function to retrieve data from attribute relationships

Basic sorting and ranking

Handling division by zero errors

Setting a default member of a hierarchy in the MDX script

Working with Sets

Introduction

Implementing the NOT IN set logic

Implementing the logical OR on members from different hierarchies

Implementing the logical AND on members from the same hierarchy

Iterating on a set in order to reduce it

Iterating on a set in order to create a new one

Iterating on a set using recursion

Dissecting and debugging MDX queries

Working with Time

Introduction

Calculating the YTD (Year-To-Date) value

Calculating the YoY (Year-over-Year) growth (parallel periods)

Calculating moving averages

Finding the last date with data

Getting values on the last date with data

Calculating today's date using the string functions

Calculating today's date using the MemberValue function

Calculating today's date using an attribute hierarchy

Calculating the difference between two dates

Calculating the difference between two times

Calculating parallel periods for multiple dates in a set

Calculating parallel periods for multiple dates in a slicer

Concise Reporting

Introduction

Isolating the best N members in a set

Isolating the worst N members in a set

Identifying the best/worst members for each member of another hierarchy

Displaying few important members, others as a single row, and the total at the end

Combining two hierarchies into one

Finding the name of a child with the best/worst value

Highlighting siblings with the best/worst values

Implementing bubble-up exceptions

Navigation

Introduction

Detecting a particular member in a hierarchy

Detecting the root member

Detecting members on the same branch

Calculating various percentages

Calculating various averages

Calculating various ranks

Business Analytics

Introduction

Forecasting using the linear regression

Forecasting using the periodic cycles

Allocating the nonallocated company expenses to departments

Analyzing fluctuation of customers

Implementing the ABC analysis

When MDX is Not Enough

Introduction

Using a new attribute to separate members on a level

Using a distinct count measure to implement histograms over existing hierarchies

Using a dummy dimension to implement histograms over nonexisting hierarchies

Creating a physical measure as a placeholder for MDX assignments

Using a new dimension to calculate the most frequent price

Using a utility dimension to implement flexible display units

Using a utility dimension to implement time-based calculations

Advanced MDX Topics

Introduction

Displaying members without children (leaves)

Displaying members with data in parent-child hierarchies

Displaying random values

Displaying a random sample of hierarchy members

Displaying a sample from a random hierarchy

Performing complex sorts

Using recursion to calculate cumulative values

On the Edge

Introduction

Clearing the Analysis Services cache

Using Analysis Services stored procedures

Executing MDX queries in T-SQL environments

Using SSAS Dynamic Management Views (DMV) to fast-document a cube

Using SSAS Dynamic Management View (DMVs) to monitor activity and usage

Capturing MDX queries generated by SSAS frontends

Performing a custom drillthrough

Index

Customer Reviews

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Forecasting using the linear regression

This recipe illustrates the method of estimating the future values based on the linear trend. The linear trend is calculated using the least-square distance, a statistical method of determining the line that fits the best through the set of values in the chart, as displayed in the following figure:

Mathematically, linear regression can be represented by a regression line, y = ax + b, where y is the value of the y-intercept for a particular value of x.

Linear regression is often used in a way that "x" is a period and "y" is the value in a particular time period, for example, a month.

Most importantly, the regression line is used to forecast values outside the initial period. For example, we can use a period of 60 months and forecast the value for each of the following 12 months.

This recipe shows how to estimate the values of the two months subsequent to the period of 36 months that were used in the calculation. The screenshot you saw in the introduction...

MDX with SSAS 2012 Cookbook - Second Edition

MDX with SSAS 2012 Cookbook - Second Edition

Overview of this book

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MDX with SSAS 2012 Cookbook - Second Edition

Forecasting using the linear regression