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been acquired by IBM), features these extensions as part of its Red Brick Intelligent SQL (RISQL) language: I Ranking. Supports queries that ask for the top 10 and similar requests. I Moving totals and averages. Supports queries that smooth raw data for time series analysis. I Running totals and averages. Allows query responses that show results for individual months plus year-to-date totals, and similar requests. I Ratios. Allows queries that very simply express the ratio of individual values to a total or subtotal without the use of complex subqueries. I Decoding. Simplifies the translation of dimension-value codes (like the supplier-id in the example warehouse) into understandable names. I Subtotals. Allow production of query results that combine detailed and summary data values, at various levels of summarization. Other warehousing vendors provide similar extensions in their SQL implementations or provide the same capabilities built into their data analysis products. As with extensions in other areas of the SQL language, although the conceptual capabilities provided by several different DBMS brands may be similar, the specifics of the implementation differ substantially.
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The performance of a data warehouse is one of the keys to its usefulness. If business analysis queries take too long, people tend not to use the warehouse on an ad hoc basis for decision making. If it takes too long to load data into the warehouse, the corporate IS organization will probably resist frequent updates, and stale data may make the warehouse less useful. Achieving a good balance between load performance and runtime performance is one of the keys to successful warehouse deployment.
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The process of loading a warehouse can be very time-consuming. It s common for warehouse data loads to take hours or even days for very large warehouses. Load processing typically involves these operations: I Data extraction. The data to be loaded into the warehouse database typically comes from several different operational data sources. Some may be relational databases that support OLTP applications. I Data cleansing. Operational data tends to be dirty in the sense that it contains significant errors. For example, older transaction-processing systems may not have strong integrity checks, permitting the entry of incorrect
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customer numbers or product numbers. The warehouse-loading process typically includes data integrity and data sanity checks. I Data cross-checking. In many companies, the data processing systems that support various business operations have been developed at different times and are not integrated. Changes that are processed by one system (for example, adding new product numbers to an order-processing application) may not automatically be reflected in other systems (for example, the inventory control system), or there may be delays in propagating changes. When data from these nonintegrated systems arrives at the warehouse, it must be checked for internal consistency. I Data reformatting. Data formats in the operational data stores may differ considerably from the warehouse database. Character data may need transformation from a mainframe s EBCDIC encoding to ASCII. Zoned decimal or packed decimal data may need reformatting. Date and time formats are another source of differences. Beyond these simple data format differences, data from one OLTP data source row may have to be broken apart into multiple warehouse tables, while data from multiple OLTP tables or files may have to be combined to create a warehouse table. I Data insertion/update. After the preprocessing, actual bulk loading of data into a warehouse database tends to be a specialized operation. High-volume data loaders typically operate in a batch-oriented mode, without transaction logic and with specialized recovery. Row loading or update rates of hundreds of megabytes per hour may be required. I Index creation/update. The specialized indexes used by the warehouse must be modified to reflect the revised warehouse contents. As with the actual data insertion and update, specialized handling is typically applied. In some cases, it is more efficient to rapidly re-create an entire index than to modify it incrementally as data rows are inserted or updated. Other index structures permit more incremental updates. These tasks are typically performed by specialized warehouse-building programs on a batch-processing basis. Ad hoc query access to the warehouse is turned off during the update/refresh processing, allowing it to proceed at maximum speed without competition for DBMS cycles. Despite these optimizations, warehouse load times tend to grow as the amount of accumulated data grows, so the load-time versus runtime performance trade-off must be made on an ongoing basis. Warehouses with many indexes or precomputed summary values may offer much better runtime performance but at the expense of unacceptably long load times. Simpler structures with less loading work may increase the time required for ad hoc queries beyond an acceptable level. In practice, the warehouse administrator must find a good balance between loading and runtime query performance.
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