TechUpdate TechUpdate is published quarterly and is available exclusively at www.tdwi.org. By: Michael L. Gonzales HandsOn-BI, LLC Quarter 3, 2005

See Technology Update Live! with Michael L. Gonzales at TDWI’s Spring and Fall World Conferences. For more information about TDWI events, visit www.tdwi.org.

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Table of Contents

Introduction.............................................................................................................. 3 HeadsUp ................................................................................................................. 5

Overview.............................................................................................................. 5 HeadsUp.............................................................................................................. 6

Performance Scale.................................................................................................. 7 Business Logic Calculation...................................................................................... 8 Hierarchical Aggregation ......................................................................................... 9 Dimensional Capability .......................................................................................... 10 Appendix A – Glossary.......................................................................................... 11 Appendix B – References...................................................................................... 12

Tables Table 1: HeadsUp ................................................................................................... 6 Table 2: Performance Scale .................................................................................... 7 Table 3: Business Logic Calculation........................................................................ 8 Table 4: Hierarchical Aggregation ........................................................................... 9 Table 5: Dimensional Capability ............................................................................ 10 Table 6: Glossary .................................................................................................. 11

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Introduction This is the third report in a technical series developed by HandsOn-BI, LLC, and published exclusively through TDWI. This report compares the current Hyperion Intelligence 7.x (Hyperion) product suite and the new Microsoft SQL Server 2005 (Microsoft) solution. The two product offerings were surveyed using four product dimensions: Performance: This section focuses on factors specific to the scale and performance capability of either product offering, including: Overall cube size Sparsity performance Processing performance Business Logic Calculation: Measured here are the capabilities offered to define and apply business logic calculations, specifically: Business logic calculation standardization Business logic calculation application in a cube Scheduling of business logic calculations Hierarchical Aggregation: Compared here is the ability to establish and control a variety of hierarchies in a single dimension, including: Multiple hierarchies Multiple aggregation structures Change aggregation logic Schedule aggregation processing Load summary level data Dimensional Capability: This section examines dimensional functionality afforded by each product, specifically: Member and dimension scale Defining dimensions using external data sources or SQL Adding, renaming, and deleting members in a loaded cube Copying dimension definitions Validating dimensions

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Information in this report was gathered using survey results from two large, independent companies who are considered experts and vendor partners in the implementation of each product. These survey results were examined and edited to emphasize a less biased, more factual response for the reader to evaluate. Although HandsOn-BI used care to ensure accurate survey responses, there is always a possibility of error or omission. Readers are urged to use this report to complement their own research when comparing these two competitors. As always, it is my sincere hope that this report complements your product research efforts.

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HeadsUp This section contains a summary view of these two technologies with regard to their position in the overall OLAP market as well as how their product offerings compare.

Overview Microsoft and Hyperion are the two dominant players in the OLAP space, positions one and two respectively. The current market share1 for Microsoft is estimated at 27.4% while Hyperion is at 20.7%. This estimate is significant for several reasons:

• Microsoft was not even on the OLAP radar screen prior to 1998 and yet it is the clear leader in the space today.

• Hyperion’s share has eroded slowly but steadily, dropping from nearly 30% a few years ago to its current level.

• Currently, both vendors are clearly the dominant leaders with the closest competitor (Cognos) being significantly smaller.

• Both vendors provide an integrated, multidimensional database server product. This underlying technical architecture is considerably different than that of most OLAP products offered.

Although Microsoft has enjoyed a fast rise to become the OLAP market leader, it has done so primarily because previous versions of Analysis Services were technologically sufficient for common OLAP applications and, most importantly, it was bundled with SQL Server at no additional charge. Technically, however, Hyperion remained the superior multidimensional database server in terms of hierarchical and calculation complexity, as well as MOLAP scalability. The purpose of this study is to determine if the pending GA release of Microsoft SQL Server 2005 will challenge the technical superiority of Hyperion. 1 Market share estimates taken from “The OLAP Report,” www.olapreport.com, accessed on July 26, 2005.

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HeadsUp Summarized commentary comparing Hyperion and Microsoft against the four dimension criteria is found in Table 1. Readers will find more information for each product reviewed in the following sections of this paper. Table 1 - HeadsUp

Criteria Superior Product

Summary

Performance Neither In terms of MOLAP, both products provide significant scalability. Of the two, Hyperion has a proven history of large, successful production cubes (>100gb). Microsoft must still convince the market of its scalability claims.

Business Logic Calculation

Hyperion Hyperion still offers the most robust means for definition, maintenance, and control of business logic to apply cube calculations, some such as Time Series, with a simple click of the mouse. While Microsoft provides comparable features, it does so with more custom coding required.

Hierarchical Aggregation

Neither Once again we find both products competitive. A fundamental difference is that much of the aggregation definition with Hyperion is found in the outline and graphical interface. Microsoft again mainly relies on MDX coding.

Dimensional Capability

Hyperion Although both products can achieve significant dimensional scalability, Hyperion’s outline interface and the robust capability of adding, changing, and deleting members with loaded cubes remains superior to the Microsoft offering.

While both products are competitive, Hyperion remains the superior MOLAP technology. While Microsoft continues to grow its technological capability, the application of many features and functionality is only achieved with some custom programming. Hyperion, with its mature graphical interface and intuitive cube outline, reduces the application of many complex features to simple mouse clicks. Nevertheless, Hyperion’s technically superior MOLAP technology will not be sufficient to stem the market share gains of Microsoft. Total Cost of Ownership2 will continue to weigh heavily in favor of SQL Server 2005 Analysis Services.

2 Gonzales, Michael. HandsOn-TechUpate, TDWI Q1, 2005.

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Performance Scale Much of the performance for any software solution can be tied to the platform on which it resides. Nevertheless, there are many OLAP-specific features that dictate your expected overall performance and scale. Table 2 attempts to draw comparisons to the upper size limit of a cube, the query performance against highly dense or sparse cubes, and the overall processing performance. Table 2 – Performance Scale

Criteria Hyperion Microsoft

Afford cube size up to 10 terabytes and consistent performance for queries and cube navigation.

Scalability and speed resulting in 247,524 average queries per minute (AQM) while serving 10,000 concurrent users—an average of 2.4 seconds for each user query.

A test demonstrated 1.2 terabytes of source data with 7.7 billion facts could be stored and aggregated in a single cube. Query response times were sub-second. The cube size was 416 GB, one-third the size of the source data set.

Provide consistent performance for queries and cube navigation against data with sparsity ranging from 0.5% to 45%.

AQM of 119,085 and an average response time of 5.04 seconds. This benchmark is a 189% improvement over previous benchmarks and cuts its own record-breaking response times in half.

Microsoft does not allocate storage for empty cells. This counterbalances performance issues caused by sparsity.

Cube processing performance.

For the second consecutive year, Hyperion broke the industry OLAP benchmark, which it set.

Proactive caching affords both real-time updates and MOLAP class performance. This highly compressed data cache is automatically maintained as source data changes, isolating the back-end systems.

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Business Logic Calculation Although all competitive OLAP products provide financial and statistical functions, only the leading products provide a means for users to define business logic around those calculations. Below are a few criteria necessary to define and implement effective business logic in cube technology. Table 3 – Business Logic Calculation

Criteria Hyperion Microsoft

Provide standard set of arithmetic operators to define business logic calculations.

Over 250 pre-defined calculations and a procedural calculation language for controlling calculation execution and business logic in calculations. No limit to number of calculations.

MDX provides standard arithmetic operations as well as Time Series functions, hierarchical functions, “IF” statements, Lead/Lag functions, statistical functions.

Provide capability to apply different sets of business logic calculations to different parts of a cube.

Can calculate full or subset of database.

Yes, using MDX.

Capable of selectively scheduling business logic executions.

Business logic execution can be scheduled as needed.

This is possible with the appropriate cube design and through a custom application code.

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Hierarchical Aggregation OLAP is founded on statistical aggregation (sum, min, max, etc.) Therefore, the more features available for applying aggregation to a cube structure, the more advanced the applications that can be designed and implemented. Table 4 examines a few critical features. Table 4 – Hierarchical Aggregation

Criteria Hyperion Microsoft

Define dimensions with no or multiple hierarchies.

Yes, supported. Yes, supported.

Allow hierarchical, non-hierarchical, and multiple aggregation structures within the same dimension.

Supports, ragged, implicit, and unbalanced hierarchies allowing different branches of hierarchies to have different levels of information.

Supports ragged, unbalanced, and alternate hierarchies. Different types of hierarchies can be combined between different dimensions and different levels of aggregation.

Provide the ability to change aggregation logic after data is loaded.

Users can alter aggregation logic without unloading data.

Recalculations do require reading from the original data source. Custom code would be required to recalculate without requiring connectivity to the original data source.

Ability to schedule aggregation-processing.

Aggregations can be scheduled as needed.

Pushing data into cubes can be scheduled using Integration Services. Use MDX for aggregation of data in cube.

Support loading of data at summary level of hierarchy and use of loaded values in calculations at all lower levels of hierarchy.

Yes, users can use a cross dimensional operator to point to data at a summary level and another command to apply calculation to selected groups.

Write-enabled cubes and write-enabled dimensions are different but complementary features. A write-enabled cube provides ability to update cube cells, while a write-enabled dimension allows member updates. The two features can be used in combination.

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Dimensional Capability The dimensional capability of an OLAP tool will dictate the complexity of analysis made available to users as well as their ability to manipulate the physical cube structure. This section examines a few of the advanced dimensional features you should consider. Table 5 – Dimensional Capability

Criteria Hyperion Microsoft

Maximum number of members allowed per dimension and dimensions per cube.

Supports an unlimited number of dimensions and dimensional members within a dimension.

Has been tested beyond 64 million members.

Capability to define dimensions using formatted flat files, XML or CSV files.

Yes, access to a range of data sources.

With custom coding.

Ability to define a dimension structure through data retrieved from an SQL query.

Can create dimensional hierarchies from raw data, from SQL queries, and wide range of sources, including leading relational databases.

A data member can be added or renamed at the relational level; however, cube processing must occur either at dimensional or cube levels.

Ability to add, rename, or delete dimension members in a loaded cube.

Changes to dimensions can be made without unloading the database.

Yes, with restrictions for a cube where an incremental or full refresh is required depending upon the dimension structure and type of change.

Ability to copy a dimension definition from an existing OLAP cube to define a new cube structure.

Outline structures can be copied from one database to another.

Yes, shared dimensions are available and copies of dimension definitions can be copied from one cube to another.

Ability to validate a dimension prior to cube creation.

The server validates the outline prior to saving.

Schema Generation Wizard validates cubes and dimensions.

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Appendix A – Glossary Table 6 - Glossary

Aggregation The process of consolidating data from subordinate entities up to parent entities. Aggregation and the concept of roll-up are synonymous.

Attribute A type of member stored in a dimension. For example, shoe size and color could be attributes for a Product dimension.

Calculation Aggregation process or running of commands that specify how a cube will be consolidated.

Cube A discrete multidimensional database stored in a proprietary data structure used for online analytic processing (OLAP).

Dimension A cube category of data used to organize data for the use of storage and retrieval.

Hierarchy A set of parent/child, multidimensional relationships, often established in a tree model.

Member A specific and discrete element of a dimension. For example, November is a member of the Date dimension.

Multidimensional Database

A technique for organizing, storing and retrieving data through three or more dimensions.

MDX Multidimensional Expressions

MOLAP See cube.

OLAP Online analytic processing is a generic term used to describe multidimensional data and related analysis. OLAP supports techniques such as drill-down, roll-up, and data pivot. There are three core adaptations of OLAP, including MOLAP, ROLAP, and HOLAP

Sparsity The lack of data at intersection points of dimension members.

SQL Structured Query Language

XML eXtensible Markup Language

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Appendix B – References Gonzales, Michael. HandsOn-TechUpate, TDWI Q2, 2005. ---, HandsOn-TechUpdate, TDWI Q1, 2005. ---. “More Than Pie Charts.” Intelligent Enterprise, November 14, 2004. ---. “The SQL Language of OLAP.” Intelligent Enterprise, September 18, 2004 ---. “The OLAP-Aware Database.” DB2 Magazine Quarter 2, 2003. ---. IBM Data Warehousing. Wiley Publishing Inc., 2003.