- Concisely define the following key terms: data warehouse, operational system, informational system, data mart, independent data mart, dependent data mart, enterprise data warehouse (EDW), operational data store (ODS), logical data mart, @ctive data warehouse, reconciled data, derived data, event, transient data, periodic data, static extract, incremental extract, data scrubbing, refresh mode, update mode, data transformation, selection, joining, aggregation, star schema, grain, market basket analysis, conformed dimension, snowflake schema, OLAP, ROLAP, MOLAP, data mining, and data visualization.
- Give two important reasons why an “information gap” often exists between the information manager’s need and the information generally available.
- List two major reasons why most organizations today need data warehousing.
- Name and briefly describe the three levels in a data warehouse architecture.
- List the four main steps of data reconciliation.
- Describe the two major components of a star schema.
- Estimate the number of rows and total size in bytes of a fact table, given reasonable assumptions concerning the database dimensions.
- Design a data mart using various schemes to normalize and denormalize dimensions and to account for fact history and changing dimension attribute values.
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