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Information Returned CPU workload Memory in use Data paged to disk Modem speed Number of corrupted data packets (possibly indicative of phone line noise) Number of bytes read/written per second

Here is a quick review of the basic principles covered in this chapter Fact tables are dependent on dimension tables Loading a fact requires that associated dimension rows have been previously loaded or have been prepared for loading in the same transaction Loading Dimensions The dimension load process must identify and process new and changed dimension records and handle them accordingly New records contain a natural key that has not been previously loaded into the dimension table A source record can be identified as new by looking up its natural key in the dimension table If not found, it is new A source record can be inspected for type 1 changes by assembling its type 1 dimension attribute values and comparing them to those of a record in the dimension table that shares the same natural key value If they do not match, a type 1 change has occurred.

Table 3-6.

Summary

Type 1 changes must be applied to all rows in the dimension table that share the same natural key value (rather than only to one that is flagged as current) A source record that contains type 1 changes may also contain type 2 changes A source record can be inspected for type 2 changes by assembling its type 2 attribute values and comparing them to those of the current record in the dimension table with the same natural key If they do not match, a type 2 change has occurred Loading Facts Loading a fact table row requires identification of the foreign key values that will refer to dimension tables Source systems provide transactions with natural keys; these must be translated into surrogate keys For each transaction, surrogate keys can be determined by looking up the natural key value in the dimension table.

3:

There may be multiple matches; the lookup must be supplemented in some way to determine the correct version Adding a current_version flag to the dimension table allows surrogate keys to quickly identify the latest dimension entry for a natural key; adding effective and expiration flags allows identification of the version in effect at any point in time Optimizing the Load Limit the amount of data that must be processed by implementing a mechanism that identifies new and changed data in advance Reduce the complexity of processing by simplifying lookups Cleansing Data The data warehouse should not transform or manufacture data if it will result in a different version of the facts from that provided by the operational systems..

This chapter described the development of SQL and its role as a standard language for relational database management: SQL was originally developed by IBM researchers, and IBM's strong support of SQL is a key reason for its success. There are official ANSI/ISO SQL standards and several other SQL standards, each slightly different from the ANSI/ISO standards. Despite the existence of standards, there are many small variations among commercial SQL dialects; no two SQLs are exactly the same. SQL has become the standard database management language across a broad range of computer systems and applications areas, including mainframes, workstations, personal computers, OLTP systems, client/server systems, data warehousing, and the Internet.

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Data cleansing rules can be described using rules or by mapping data elements to one another. Transactions may arrive with natural keys that cannot be validated against dimension tables. These should not be excluded from the fact table since they will alter the magnitude of facts. Accommodate these facts with special case dimension rows and transaction identifiers that will permit their correction later. Housekeeping Columns Housekeeping columns are added to the star schema to support reporting, ETL, or quality assurance.

Figure 3-33.

Database management systems organize and structure data so that it can be retrieved and manipulated by users and application programs. The data structures and access techniques provided by a particular DBMS are called its data model. A data model determines both the "personality" of a DBMS and the applications for which it is particularly well suited. SQL is a database language for relational databases that uses the relational data model. What exactly is a relational database How is data stored in a relational database How do relational databases compare to earlier technologies, such as hierarchical and network databases What are the advantages and disadvantages of the relational model This chapter describes the relational data model supported by SQL and compares it to earlier strategies for database organization.