Expert Cube Development with Analysis Services 2008
Now that I’ve got my copy and read it, I can say a few things about the book Expert Cube Development with Microsoft SQL Server 2008 Analysis Services by Chris Webb, Alberto Ferrari, and Marco Russo. As mentioned in another post, the authors (all MVPs and prominent SSAS experts) don’t disappoint and you won’t go wrong picking up this book. The book is somewhat small in size (330 pages) but big in wisdom and best practices. It also includes plenty of references to external resources and blogs that the authors and other SSAS experts have written when you need further understanding of the topics discussed. Make no mistake though, this is not a step-by-step book and it doesn’t target novice users. As its title suggests, the book is geared toward more advanced users who have a few years of SSAS experience under their belt and are thirsty for more insightful knowledge.
Chapter 1 Designing the Data Warehouse for Analysis Services
The first and most important step for implementing a successful Analysis Services solution has nothing to do with Analysis Services at all. Its success depends on a solid schema design on which you’ll build the cube so the book is right on target to hit this topic first. Chapter 1 discusses common schema modeling challenges and provides plenty of tips to tackle them. One grudge I have here is about the following sentence “Beware of the temptation to avoid building a data warehouse and data marts” which may leave the reader with the impression that a data mart is a must. In fact, call me lazy but the first thing that I’d explore when starting a new SSAS project is if it’s possible to avoid a separate database. While a classic OLAP solution in many cases will require a new database (whatever you call it a data warehouse or a data mart) and the whole ETL enchilada that this entails, you may need to build a cube on top of simpler schemas, such on top of a DSS (decision support system) schema. In this case, I would explore the option to “dimensionalize” the source schema with a layer of SQL views. As with everything else, the keep-it-simple principle works for me.
Chapter 2 Basic Dimensions and Cubes
This chapter discusses the groundwork required to set up the raw dimensional model. This includes working with data sources, data source views, cubes, and dimensions. My favorite part of this chapter was the attribute design and setting up attribute relations. This often overlooked aspect of SSAS development is crucial for achieving good performance and the book explains why.
Chapter 3 Designing More Complex Dimensions
Next, the book explains how to handle more complex dimension schemas and hierarchies. This includes parent-child, ragged, slowly-changing, referenced, fact, and junk dimensions. The authors’ real-life experience shows through. About junk dimensions, my preference is to avoid them wherever possible for performance reasons. Instead, I’d explore the option to keep the low-cardinality attributes in the fact table and use report or URL actions to view them. In my opinion, SSAS is not there yet as far as reporting on details.
Chapter 4 Measures and Measure Groups
The whole purpose of building a cube is to aggregate measures. Analysis Services supports various aggregation functions and the book provides essential coverage. You will learn how to implement semi-additive measures if you cannot afford the Enterprise Edition of SQL Server. Custom aggregation by using unary operators and custom member formulas are presented too. Then the book discusses more complex relationship types, including different grains, linked dimensions and measure groups, role-playing dimensions and fact relationships.
Chapter 5 Adding Transactional Data
Although most users would be interested in aggregated data, sometimes users may need to see the level of details behind a cell. The book explains how you can do this by using drillthrough and actions. Drillthrough options are covered in great details. Excellent coverage is provided for many-to-many relationships as well.
Chapter 6 Adding Calculations to the Cube
Cubes usually include business calculations in the form of MDX expressions. Chapter 6 gives lays the foundation for using MDX. It provides practical examples for calculated members, including YTD, ratios, growths, same period last previous year, moving averages, and ranks. Handling relative dates is a common business requirement. For example, I personally had to implement two custom types of relative dates as explained in this blog. The book provides a great example for implementing a custom calculation dimension for implementing more flexible relative dates that the BI Wizard provides. Finally, the chapter discusses static and dynamic named sets.
Chapter 7 Adding Currency Conversion
I consider myself lucky for never having to support international users because of the complexities surrounding the issue. If you have to, SSAS gives you the necessary features to tackle various localization requirements. This chapter walks you through the details of implementing currency conversion using the BI wizard, measure expressions, and writeback. My advice is to avoid writeback due to its limitations. Instead, consider an URL action that navigates the user to a custom web page for handing data changes and proactive caching for automatically updating the cube.
Chapter 8 Query Performance Tuning
SSAS bends backwards to provide great performance. You can help by following best practices for cube design. This chapter teaches you how to implement partitions and aggregations. I like the sections for monitoring partition and aggregation usage and building aggregations manually. The chapter presents useful tips to diagnose and resolve performance issues, such as by using the MDX Studio, named sets, calculated members, and caching.
Chapter 9 Securing the Cube
Chances are you will need to provide restricted access to the cube for groups of users. This chapter shows you how to implement and test roles. It covers cell and dimension security. It presents solutions for implementing custom dimension data security which are similar to the ones I discussed in this article.
Chapter 9 Productionization
Your SSAS solution is ready and it’s time to see daylight. This chapter covers the tasks associated with deploying and managing your SSAS solution in production environment. These include managing and generating partitions and processing. I liked the coverage of different processing options.
Chapter 10 Monitoring Cube Performance and Usage
As an administrator, your job doesn’t end once the solution is deployed. You need to monitor it on a daily basis to ensure that it performs optimally. The last chapter explains different server monitoring techniques, such as performance counters, tracing, Resource Monitor, SQL Profiler, and using Dynamic Management Views (DMVs). I personally liked the memory management tips.
In summary, I highly recommend this book to anyone who has several years of experience in SSAS development and need a compass check to make sure that he follows the right track. Kudos to the authors for taking time to write it!
