Designing a Data Warehouse Issues in DW design

Data Warehouse A read-only database for decision analysis Subject Oriented Integrated Time variant Nonvolatile consisting of time stamped operational and external data.

Data Warehouse vs Operational Databases Highly tuned Real time Data Detailed records Current values Accesses small amounts of data in a predictable manner Flexible access Consistent timing Summarized as appropriate Historical Access large amounts of data in unexpected ways

Data Warehouse Purpose Identify problems in time to avoid them Locate opportunities you might otherwise miss

“DNA of Data Warehousing”, Techguide.com,

Data Warehouse: New Approach An old idea with a new interest because of: Cheap Computing Power Special Purpose Hardware New Data Structures Intelligent Software

Warehousing Problems Business Issues Data Quantity Data Accuracy Maintenance Ownership Cost

Warehousing Problems Business Issues Database Issues DBMS Software Technology Complexity

Warehousing Problems Business Issues Data Issues Analysis Issues User Interface Intelligent Processing

Three Approaches Classical Enterprise Database Contains operational data from all areas of the organization. Data Mart Extracted and managerial support data designed for departmental or EUC applications Data Package Data required for a specific application

Classical Warehouse

Mart

Package

Three Fundamental Processes Data Acquisition Data Storage Data a Access

Data Acquisition Handles acquisition of data from legacy systems and outside sources. Data is identified, copied, formatted and prepared for loading into the warehouse. Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Acquisition steps Catalog the data Clean and prepare the data. Develop an inventory of where it is and what it means. Clean and prepare the data. Extract from legacy files and reformat to make it usable. Transport data from one location to another. Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Storage The storage component holds the data so that the many different data mining, executive information and decision support systems can make use of it effectively. Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

The Storage Area Managed by Relational databases Specialized hardware like those from Oracle Corp. or Informix Software Inc. Specialized hardware symmetric multiprocessor (SMP) or massively parallel processor (MPP) machines Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Storage The majority of warehouse storage today is being managed by relational databases running on Unix platforms. Oracle, Sybase Inc., IBM Corp. and Informix control 65 percent of the warehouse storage market. Meta Group Inc. (1996) Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Access Different end-user PCs and workstations draw data from the warehouse with the help of multidimensional analysis products, neural networks, data discovery tools or analysis tools. These powerful, "smart" software products are the real driving force behind the viability of data warehousing. Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Access Tools Intelligent Agents and Agencies Query Facilities and Managed Query Environments Statistical Analysis Data Discovery. (decision support, artificial intelligence and expert systems) OLAP Data Visualization Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Hardware Budget A typical startup warehouse project allocates more than 60 percent of its budget for hardware and software to the creation of a powerful storage component, spending just 30 percent on data mining and user access technologies. Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Systems Analysis Budget Budgeting for systems analysis and development, however, follows a very different pattern. More than 50 percent of development dollars are spent on building acquisition capabilities, 30 percent fund the development of user solutions and 20 percent are dedicated to the creation of databases in the storage component. Warehousing Wherewithal, Rob Mattison, CIO, April 1996.

Design Issues Relational and Multidimensional Models Denormalized and indexed relational models more flexible Multidimensional models simpler to use and more efficient

Star Schemas in a RDBMS In most companies doing ROLAP, the DBAs have created countless indexes and summary tables in order to avoid I/O-intensive table scans against large fact tables. As the indexes and summary tables proliferate in order to optimize performance for the known queries and aggregations that the users perform, the build times and disk space needed to create them has grown enormously, often requiring more time than is allotted and more space than the original data!

Building a Data Warehouse from a Normalized Database The steps Develop a normalized entity-relationship business model of the data warehouse. Translate this into a dimensional model. This step reflects the information and analytical characteristics of the data warehouse. Translate this into the physical model. This reflects the changes necessary to reach the stated performance objectives. Designing the Perfect Data Warehouse (the paper formerly known as: Data Modeling for Data Warehouses), Frank McGuff , http://members.aol.com/fmcguff/dwmodel/

The Business Model Identify the data structure, attributes and constraints for the client’s data warehousing environment. Stable Optimized for update Flexible

Business Model As always in life, there are some disadvantages to 3NF: Performance can be truly awful. Most of the work that is performed on denormalizing a data model is an attempt to reach performance objectives. The structure can be overwhelmingly complex. We may wind up creating many small relations which the user might think of as a single relation or group of data.

Structural Dimensions The first step is the development of the structural dimensions. This step corresponds very closely to what we normally do in a relational database. The star architecture that we will develop here depends upon taking the central intersection entities as the fact tables and building the foreign key => primary key relations as dimensions.

Simple DW pattern.

Other Dimensions Categorical dimensions: generated groups (additional key components) Partitioning dimensions: subtypes (planned vs. actual) Informational dimensions: generate different types of data (messy).