An Introduction to OLAP in SQL Server 2005

f you’re a database developer, you’ve no doubt heard of On-Line Analytical Processing (OLAP) and the advantages of analysis using multi-dimensional, pre-aggregated data. Maybe you’ve even thought about creating your own multidimensional cubes to give your end users true ad hoc capabilities, including the creation of calculated measures/KPIs. If you’ve relegated that task to the back burner because it was too complex, you’ll be happy to know that SQL 2005 has made the process easier.

This article discusses the major OLAP components of Analysis Services, all of which can be implemented by even a first-time cube builder. A follow up article by Mark Frawley will examine the differences between Analysis Services in SQL 2000 and SQL 2005.

Why Use OLAP?
OLAP is useful because it provides fast and interactive access to aggregated data and the ability to drill down to detail. OLAP lets users view and interrogate large volumes of data (often millions of rows) by pre-aggregating the information. It puts the data needed to make strategic decisions directly into the hands of the decision makers, not only through pre-defined queries and reports, but also because it gives end users the ability to perform their own ad hoc queries, minimizing users’ dependence on database developers.

What’s the Secret?
OLAP leverages existing data from a relational schema or data warehouse (data source) by placing key performance indicators (measures) into context (dimensions). Once processed into a multidimensional database (cube), all of the measures are pre-aggregated, which makes data retrieval significantly faster. The processed cube can then be made available to business users who can browse the data using a variety of tools, making ad hoc analysis an interactive and analytical process rather than a development effort. SQL Server 2005’s BI Workbench substantially improves upon SQL Server 2000’s BI capability.

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Figure 1. Analysis Services Architecture: The figure shows the relationship of the various technology tiers involved in Analysis Services.

SQL Server 2005 BI Workbench Platform
The SQL Server 2005 BI Workbench suite consists of five basic tools:

  • SQL Server Relational Database?used to create relational database
  • Analysis Services?used to create multidimensional model (measures, dimensions and schema)
  • Data Transformation Services (DTS)?used to extract, transform and load data from source(s) to the data ware house or schema
  • Reporting Services?used to build and manage enterprise reporting using the relational or multidimensional sources
  • Data Mining?used to extract information based on predetermined algorithms.

This remainder of this article focuses on multidimensional modeling using Analysis Services and briefly touches upon DTS’s role. Figure 1 shows the Analysis Services architecture.

Elements of Multidimensional Models
To fully leverage the SQL Server 2005 BI Workbench platform, one must first understand the basic elements of multidimensional modeling. The basic elements of a multidimensional cube are: measures, dimensions, and schema.

Measures
Measures are the key performance indicators that you want to evaluate. To determine which of the numbers in the data might be measures, a rule of thumb is: If a number makes sense when it is aggregated, then it is a measure. For example, it makes sense to aggregate daily volume to month, quarter and year. On the other hand, aggregating zip codes or telephone numbers would not make sense; therefore, zip codes and telephone numbers are not measures. Typical measures include volume, sales, and cost.

Dimensions
Dimensions are the categories of data analysis. The rule of thumb is: When a report is requested “by” something, that something is usually a dimension. For example, in a revenue report by month by sales region, the two dimensions needed are time and sales region. For this reason, OLAP analysts often nickname dimensions the “bys.” Typical dimensions include product, time, and region.

Dimensions are arranged in hierarchical levels, with unique positions within each level. For example, a time dimension may have four levels, such as Year, Quarter, Month, and Day. Or the dimension might have only three levels, for example, Year, Week, and Day. The values within the levels are called members. For example, the years 2002 and 2003 are members of the level Year in the Time dimension.

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Figure 2. A Tree-Structured Multidimensional schema: The figure shows Excel Pivot Table interface consisting of tree view for a multidimensional structure.

We believe as a best practice, a cube should have no more than twelve dimensions. A cube with more than twelve dimensions becomes difficult to understand and browse. Too many dimensions can cause confusion among end users and having too many dimensions and aggregations can also lead to “data explosion.” As the number of dimensions and levels increase, the amount of data grows exponentially. As mentioned earlier, an OLAP application is typically used to manipulate large volumes of data. To optimize response time, Analysis Services usually pre-aggregate a multidimensional schema.

A dimension can be thought of as a tree structure. Many OLAP tools present it in a tree control (see Figure 2). This familiar software control makes using dimensions easier as it allows dimension members and their relationships to be viewed simultaneously. This simple interface makes using the dimensions extremely user-friendly and allows user to view data of different levels simultaneously.

Schema
The dimensions and measures are physically represented by a star schema. The most basic star schema?arranges the dimension tables around a central fact table that contains the measures (see Figure 3).

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Figure 3. Simple Star Schema: The figure shows a basic star schema with the dimension tables arranged around a central fact table that contains the measures.

A fact table contains a column for each measure as well as a column for each dimension. Each dimension column has a foreign-key relationship to the related dimension table, and the dimension columns taken together are the key to the fact table.

After determining the measures, dimensions, and schema using the BI Workbench, there is one more step?you must decide where the data aggregation is to be stored. Historically, there were three basic storage options: Multidimensional OLAP (MOLAP), Relational OLAP (ROLAP), or Hybrid OLAP (HOLAP). SQL Server 2005’s?introduction of?what Microsoft calls the?Unified Dimensional Model, which leverages the best of relational and OLAP cube technologies, allows the designer many more storage options and unlike SQL Server 2000, allows?combining them in?the same solution.

DTS
Microsoft’s Data Transformation Services (DTS) is perhaps the most critical tool in an OLAP project. DTS is used to pull data from various sources into the star schema. The data warehouse will, in turn, feed the Analysis Services database. More often that not, you must transform data from the source (for example, you may have to convert currency values, balance calculations, and the like) and remap it. Microsoft has estimated that in most cases, organizations spend eighty percent of their data warehousing on the extract, transform, and load (ETL) phase.

Visual Studio 2005 hosts a new tool, BI Workbench, which is a replacement for DTS Designer. Chief among the improvements found in BI Workbench is its separation of control flow (insertions, looping, sequencing, scripting, etc.) from the data flow (source identification, aggregation, character mapping, and data conversion) tasks (see Figure 4 and Figure 5). This separation makes DTS packages easier to read, develop, and maintain. BI Workbench is reason enough to learn and use Visual Studio 2005.

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Figure 4. Control Flow Task Diagram: The figure shows a typical control flow and an associated task statement.
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Figure 5. Data Flow Diagram: The figure shows a typical data flow from source to destination.

Because DTS has been completely reworked in SQL Server 2005, current SQL 2000 DTS user will need to brush up on DTS?and learn a few new tricks.

Working with Analysis Services
After identifying the dimensions and measures you wish to analyze, you can use Analysis Services to construct an OLAP cube.

Analysis Services has built-in wizards that make the actual process of creating dimensions fairly easy, especially if you’re already familiar with SQL 2000’s version, although SQL Server 2005’s version does add one additional step?you must create a Data Source View to import your database objects.

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Figure 6. SQL vs. MDX: The figure compares data extraction using SQL vs. MDX.

MDX
Just as you use SQL to query relational databases, you use MDX to query a multidimensional cube (see Figure 6). For those who are eager to interrogate the cube without learning MDX, there is an Excel Pivot Table add-in that provides a drag and drop query interface. This interface generates MDX and queries the cube on behalf of the user and as a special bonus the results are displayed in Excel!

You use MDX used to create “calculated measures” that would be too complex or impossible to do in SQL. For example, suppose the VP of Sales wants to know what the average sales price of each product is. Unfortunately, average sales price is not a measure in the Sales cube; however, Store Sales and Sales Count are available. Because you can calculate Average Sales Price by dividing Store Sales by Sales Count, you can calculate the measure (ergo the name “calculated measure”) by using MDX. Here’s the MDX code.

   WITH       MEMBER Measures.[Average Sale Price] AS       'Measures.[Store Sales] /       Measures.[Sales Count]'   SELECT       { Measures.[Average Sale Price] } ON COLUMNS,      { Product.CHILDREN } ON ROWS   FROM Sales

Luckily, some third party tools let users create calculated measures that may have been intentionally omitted from the original cube design, such as commission or bonus calculations.

Cube Browser
After creating the cube, you need a cube browser to connect to the cube and display the data. Cube browsers usually provide user-friendly tree-structured dimension filters and/or drag and drop interfaces that allow end users to interrogate the cube. You can set up pre-defined queries, or allow ad hoc querying by letting users combine the various measures with dimensions.

For example, suppose you want to create a report that shows Revenue by Sales Territory by Product. Because dimensions are hierarchical, you can obtain the details of a dimension by drilling down. This usually involves clicking on the dimension (for example, clicking on Sales Territory may reveal each store’s level in that dimension).

Dimensions can have multiple levels (such as year, quarter, and month). Users can mix and match members within the same dimension. Furthermore, some cube browsers enable developers to export cube browsers as a Web part that they can then easily include in a portal site or digital dashboard.

There are three basic types of cube browsers:

  • Office Web components such as the Excel Pivot Table
  • Third-party applications such ProClarity
  • Custom-built applications

Some OLAP developers find debugging cube design and validating data using pivot tables much easier than performing the same tasks using the native Analysis Services screen.

To sum up, here’s the process in a nutshell.

  • Determine the required dimensions and measures.
  • Use Data Transformation Services to extract data from your source databases, transforming the data as needed, and loading the finished data into the cube.
  • Use the BI Workbench’s Analysis Services wizards to build the measures, dimensions, and schema.
  • Provide cube browsers for your users so they can select and view reports. If necessary, write MDX queries or use automated tools, such as Excel Pivot Tables to query the cube.

Hopefully, this primer has whetted your OLAP appetite and given you the confidence to start creating OLAP cubes yourself. A good way to get started is to use the sample Foodmart or Adventure Works databases that ship with SQL Server 2005.

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