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OLTP vs. Data Warehousing Database Attributes
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SQL and Data Warehousing
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The workload presents a large volume of short, simple database requests that typically read, write, or update individual rows and then commit a transaction. The same type of workload is presented by all of the most frequent types of transactions, such as: Retrieving the price of a product Checking the quantity of product available Deleting an order Updating a customer address Raising a customer s credit limit In contrast, a typical business analysis transaction (generating an order analysis report) might involve these database accesses: Join information from the orders, order items, products, and customers tables Summarize the detail from the orders table by product in a summary query Compute the total order quantities for each product Sort the resulting information by customer This workload presents a single, long-running query that is read-intensive. It processes many rows of the database (in this case, every order item) and involves computing totals and averages and summarizing data. These characteristics are typical of almost all business analysis queries, such as: Which regions had the best performance last quarter How did sales by product last quarter compare with last year What is the trend line for a particular product s sales Which customers are buying the highest-growth products Which characteristics do those customers share The difference between the business intelligence and the OLTP workloads is substantial and makes it difficult or impossible for a single DBMS to serve both types of applications.
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Components of a Data Warehouse
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Figure 21-1 shows the architecture of a data warehousing environment. It has three typical key components: Warehouse loading tools A suite of programs that extract data from corporate transaction-processing systems (relational databases, mainframe and minicomputer files, legacy databases), process the data, and load it into the warehouse. This process typically involves substantial cleanup of the transaction data, filtering it, reformatting it, and loading it on a bulk basis into the warehouse. A common term used for these tools and processes is extract, transform, and load (ETL).
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A warehouse database A relational database optimized for storing vast quantities of data, bulk loading data at high speeds, and supporting complex business analysis queries. Data analysis tools A suite of programs for performing statistical and time series analysis, doing what if analysis, and presenting the results in textual and/or graphical form. Vendors in the data warehousing market have tended to concentrate on one of these component areas. Several vendors build product suites that focus on the warehouse-loading process and challenges. Other vendors have focused on data analysis. Some vendors have consolidated to cover both these areas, but both areas remain the focus for individual independent software companies, including several whose revenues are in the $100 million range. Specialized warehouse databases were also the target of several startup companies early in the data warehousing market. Over time, the major enterprise DBMS vendors also moved to address this area. Some developed their own specialized warehouse databases; others added warehouse databases to their product line by acquiring smaller companies that produced them. Today, the database component shown in Figure 21-1 is almost always a specialized SQL-based warehouse DBMS supplied by one of the major enterprise database vendors.
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The Evolution of Data Warehousing
The initial focus of data warehousing was the creation of huge, enterprisewide collections of all of the enterprise s accumulated data. By creating such a warehouse of data, almost any possible question about historical business practices could be posed. Many companies started down the road to creating warehouses with this approach, but success rates were low. Large, enterprisewide warehouses generally proved too difficult to create, too big, too expensive, and too unwieldy to use.
transform,
load(ETL)
FIGURE 21-1
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