SAS® 9.1 OLAP Server Administrator’s Guide The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2004. SAS ® 9.1 OLAP Server: Administrator’s Guide. Cary, NC: SAS Institute Inc. SAS® 9.1 OLAP Server: Administrator’s Guide Copyright © 2004, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. U.S. Government Restricted Rights Notice. Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227–19 Commercial Computer Software-Restricted Rights (June 1987). 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Contents What’s New v Overview v Details v Chapter 1 R OLAP Introduction and Overview 1 What Is OLAP? 1 What Is a Cube? 2 Understanding the Cube Structure 3 What Is SAS OLAP Server? 3 About Cube Metadata Storage 4 Why You Should Use Cubes 4 Analyzing Your Data 5 Chapter 2 R Installing and Administering SAS OLAP Server 7 Installing and Configuring SAS OLAP Server 8 Monitoring OLAP Server Performance 27 Changing an OLAP Server Configuration 28 Optimizing OLAP Server 31 Monitoring and Administering Sessions—SAS OLAP Server Monitor Plug-In 34 Securing Cubes 35 Cubes and the Metadata Server 38 Understanding Change Management in SAS OLAP Cube Studio 39 Accessing OLAP Cubes from SAS: SQL Pass-Through Facility for OLAP 40 Chapter 3 R Building and Updating Cubes 45 Background 45 Building a Cube from a Detail Table 48 Building a Cube from a Summary Table 55 Building a Cube from a Star Schema 61 Updating a Cube 67 Refreshing Cube Metadata 67 Defining Member Properties 68 Defining Multiple Hierarchies for a Dimension 69 Defining Ragged Hierarchies for a Dimension 70 Manually Tuning Cube Aggregates 73 Multiple Language Support and Dimension Table Translations 74 Adding SAS System Options to a Cube 75 Specifying Tuning and Performance Options in Cube Aggregations 76 Chapter 4 R Using SAS OLAP Cubes 79 Using a Cube with ADO MD 79 Using a Cube with OLE DB for OLAP 79 iv Using a Cube with Additional SAS Software 80 Using a Cube with Third-Party Clients 80 Chapter 5 R Transitioning from SAS OLAP Server Release 8.2 to SAS 9.1 85 Conversion and Migration Issues from Release 8.2 to SAS 9.1 85 Comparing OLAP Functionality in SAS 8 and SAS 9.1 86 Comparing PROC MDDB Code and PROC OLAP Code 89 Appendix 1 R The OLAP Procedure 91 The OLAP Procedure 92 Syntax: OLAP Procedure 92 PROC OLAP Statement 92 METASVR Statement 97 DIMENSION Statement 99 LEVEL Statement 102 PROPERTY Statement 103 HIERARCHY Statement 105 MEASURE Statement 107 AGGREGATION Statement 110 DROP_AGGREGATION Statement 112 DEFINE Statement 113 USER_DEFINED_TRANSLATIONS Statement 115 Tables Used to Define Cubes 119 Naming Guidelines for SAS OLAP Server 120 Loading Cubes 121 Maintaining Cubes 125 Specialized Syntax Options for PROC OLAP 127 Appendix 2 R Recommended Reading 129 Recommended Reading 129 Glossary 131 Index 139 v What’s New Overview The SAS OLAP Server enables users to develop and deploy scalable Online Analytical Processing (OLAP) applications. In addition, automated data loading and cube building is available through the use of a new administration interface called the SAS OLAP Cube Studio, which was developed using Java technology. OLAP queries are performed using the Multidimensional Expressions (MDX) query language in client applications that are connected to the OLAP Server by using the SQL Pass-Through Facility for OLAP, which is designed to process MDX queries within the PROC SQL environment. open access technologies such as OLE DB for OLAP, ADO MD, and Java. Note: This section describes the features of the SAS OLAP Server that are new or enhanced since SAS 8.2. R Details There are two new tools for data loading and cube building: The OLAP procedure, in addition to cube building, includes options for handling ragged hierarchies, defining global calculated members and named sets, assigning properties to levels, and optimizing cube creation and query performance. It also supports multiple hierarchies and drill-through tables. The SAS OLAP Cube Studio is an alternative Java interface to the OLAP procedure. This interface is also integrated with SAS ETL Studio. Server performance is recorded and analyzed by using the Application Response Measurement (ARM) system. The new multi-threaded data storage and server functionality provide faster cube performance. The data can be stored in a multidimensional form (MOLAP) or in a form that includes existing aggregations from presummarized data sources. The metadata structure is improved, and metadata is stored with the cube. vi What’s New Caching and logging can be enabled or disabled. Support for ad hoc calculations and time dimensions is improved. An SQL Pass-Through Facility for OLAP is available in SAS for use in querying cubes. Aggregations can be added to or deleted from existing cubes. Note: Version 8 of the SAS OLAP Server can be used with SAS 9. For help, see “V8 SAS OLAP Server” in SAS System Help and Documentation. R 1 CHAPTER 1 OLAP Introduction and Overview What Is OLAP? 1 Data Storage and Access 1 Benefits of OLAP 2 What Is a Cube? 2 Understanding the Cube Structure 3 What Is SAS OLAP Server? 3 About Cube Metadata Storage 4 Why You Should Use Cubes 4 Cube Usage and Storage Space Reduction 4 Multi-Threading Capabilities 5 Easy Setup and Maintenance 5 Data Management: Choosing Your Own Tool 5 Analyzing Your Data 5 Data Preparation and Dimension Design 5 Aggregation Design 6 What Is OLAP? Online Analytical Processnding (OLAP) is a technology that is used to create decision support software. OLAP enables application users to quickly analyze information that has been summarized into multidimensional views and hierarchies. By summarizing predicted queries into multidimensional views prior to run time, OLAP tools provide the benefit of increased performance over traditional database access tools. Most of the resource-intensive calculation that is required to summarize the data is done before a query is submitted. Data Storage and Access Decision makers are asked to make timely and accurate decisions that are based on the past performance and behavior of an organization as well as on future trends and directives. To make effective business decisions, business analysts must have access to the data that their company generates and responds to. This access must include timely queries, summaries, and reviews of numerous levels and combinations of large, recurrent amounts of data. The information that business analysts review determines the quality of their decisions. Organizations usually have databases and data stores that maintain repeated and frequent business transaction data. This provides simple yet detailed storage and retrieval of specific data events. However, these data storage systems are not well suited for analytical summaries and queries that are typically generated by decision 2 Benefits of OLAP R Chapter 1 makers. For decision makers to reveal hidden trends, inconsistencies, and risks in a business, they must be able to maintain a certain degree of momentum when querying the data. An answer to one question usually leads to additional questions and review of the data. Simple data stores do not successfully support this type of querying. A second type of storage, the data warehouse, is better suited for this. Data is maintained and organized so that complicated queries and summaries can be run. OLAP further organizes and summarizes specific categories and subsets of data from the data warehouse. This results in a robust and detailed level of data storage with efficient and fast query returns. SAS OLAP cubes can be built from either partially or completely denormalized data warehouse tables. Stored, precalculated summarizations called aggregations, can be added to the cube to improve cube access performance. Aggregations can either be pre-built relational tables, or you can let the cube create its own optimized aggregates. Benefits of OLAP The ability to have coherent and relevant information is the reason OLAP has gained in popularity. OLAP systems help reveal evasive inconsistencies and trends in data that might not have been seen before. OLAP users can intuitively search data that has been consolidated and summarized within the OLAP structure. In addition, OLAP tools allow for tasks such as sales forecasting, asset analysis, resource planning, budgeting, and risk assessment. OLAP systems also provide the following benefits: fast access, calculations, and summaries of an organization’s data support for multiple user access and multiple queries the ability to handle multiple hierarchies and levels of data the ability to pre-summarize and consolidate data for faster query and reporting functions the ability to expand the number of dimensions and levels of data as a business grows. To fully understand the benefits of OLAP and the details of its effective implementation, it helps to examine the technology from two perspectives—first, from that of the users and second, from that of the information technology (IT) administrators who are responsible for OLAP implementation.