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You could make this relationship explicit by storing brand information in a separate table, linking it to product via a foreign key relationship When the relationships between dimension attributes are made explicit in a dimensional design, the result is known as a snowflake The snowflake gets its name from the appearance it takes when drawn with the fact table in the center Dimension tables emanate from this center like the branches of a snowflake, as shown in the example in Figure 7-5 The snowflake in Figure 7-5 was created by instantiating the hierarchies from Figure 7-3 in separate physical tables This required the addition of surrogate keys to each hierarchy level Category_key and brand_key, for example, were added to category and brand Instantiation of the hierarchy also required the addition of foreign keys that identify the parent of each level.

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If backing up data to tape is slow, is the real problem the device or is it a slow SCSI controller that runs on the ISA bus 3 Make one change at a time Once you ve identified the problems, it s time to try solving them It s important to make only one change at a time Otherwise, the presence of multiple variables might confuse your conclusions 4 Reevaluate the performance After changes have been made, you should always retest your environment First and foremost, make sure that you haven t eliminated any needed services Backups might run very quickly if no one can connect to your database server, but this clearly isn t an acceptable performance optimization technique 5 Repeat, as necessary Whether or not you have improved performance, you might choose to repeat these steps to isolate and move other bottlenecks.

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Each product, for example, includes a brand_key, which identifies the appropriate brand It is easy to imagine this snowflake becoming more intricate Many product attributes, for example, may have codes and descriptions Each of these code/ description pairings may become the basis for additional tables in the snowflake schema You may find a snowflake configuration appealing because it exposes a natural taxonomy in the data For those trained in ER modeling, the snowflake reflects some best practices learned in the service of operational systems; however, it is of little utility for an analytic database, aside from saving some space..

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The AVG() column function computes the average of a column of data values. As with the SUM() function, the data in the column must have a numeric type. Because the AVG() function adds the values in the column and then divides by the number of values, its result may have a different data type than that of the values in the column. For example, if you apply the AVG() function to a column of integers, the result will be either a decimal or a floating point number, depending on the brand of DBMS you are using. Here are some examples of the AVG() column function: Calculate the average price of products from manufacturer ACI. SELECT AVG(PRICE)

IT may sound rather dismal, but performance optimization is usually a constant process There are a few misconceptions that can cause problems in the overall methodology The first step is to realize that the goal is to move the bottleneck If we define a bottleneck as the slowest step in a given process, you ll realize that this can never be removed entirely Second, realize that the goal is usually to reach acceptable performance Although it would be nice to always squeeze every last piece of computing power out of our machines, time and money constraints often prevent this from happening Sometimes, we just have to settle for good enough instead of perfect With this is mind, let s look at some ways to monitor performance..

CATEGORY category_key category_name category_code BRAND brand_key category_key brand_name brand_code brand_manager PRODUCT product_key brand_key product_name sku ORDER_FACTS product_key day_key salesrep_key customer_key order_dollars extended_cost margin_dollars quantity_ordered order_number order_line_number CUSTOMER customer_key customer_name customer_id primary_industry billing_address billing_city billing_state billing_zip phone SALESREP salesrep_key territory_key salesperson_name salesperson_code office_location hire_date TERRITORY territory_key region_key territory_name territory_code

- 138 -

6:

FISCAL_YEAR fiscal_year_key fiscal_year FISCAL_PERIOD fiscal_period_key fiscal_year_key period fy_and_period

Snowflaking a dimension is similar to a process called normalization, which guides the design of operational systems. This technique was developed to ensure referential integrity of data in operational systems, which support a wide variety of simultaneous transactions that are highly granular. An analytic database does not share this usage pattern, and referential integrity can be enforced by the ETL process. Normalization is therefore not necessary. In fact, modeling the relationships between dimension attributes detracts from usability, complicates ETL, and may even disrupt performance. That said, there are some good reasons you may wish to model a snowflake, as you will see later in this chapter.

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Read barcode from an image in JAVA - Stack Overflow
Java Apache Camel Barcode based on the zxing library works great: ... Some example (with rotating if needed) .... If you guys come across any other barcode reading SDKs or APIs or Softwares which can run on command ...

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