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implementation Often, to fully meet the user s reporting requirements, you will need to extract data from several OLTP systems that may not be easily accessible For example, direct access to mainframe systems is usually not an option In this case, data has to be exported on a regular basis from the OLTP system to flat files and then uploaded to the warehouse One of the most important decisions that you have to make when architecting a warehouse is the level of data consolidation Because the main purpose of a warehouse is to show historical patterns for trend analysis, it rarely makes sense to import the OLTP data at its most granular level Instead, the data warehouse measures are typically grouped around coarser time periods, such as days or months Storing data at levels that are too granular may unnecessarily increase the size of the warehouse and impact its performance.

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Embracing nil receivers in Objective-C will change how you write code and design classes. This section will show how to take advantage of nil receivers to simplify your code and will introduce three design patterns that leverage nil. Abstractly, this is about adopting the programming principle of letting the data make the decision first articulated by Charles Moore, computer science pioneer and inventor of the FORTH language. The principle avoids writing conditional statements that branch to handle exceptional cases, in favor of data and expressions that incorporate those cases. Contrast the two code snippets in Listing 7-4. The code is part of a recipe management program. The makeLists method assembles a list of recipes planned for the week, along with a shopping list of ingredients that must fit within the current budget. If the cost of all ingredients exceeds the budget, ingredients for meals later in the week are omitted. The program uses a number of classes, many of which have optional properties that must be considered. There might not be a Budget object, in which case there are no budgetary restrictions on the grocery list. There might not be any meals in the planner, in which case the resulting lists will be empty. A meal might not have a recipe property (some meals might be take-out), in which case it would go in the list of meals but would not add any ingredients to the shopping list.

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Document by ean reader.termDocs( term ); while ( termDocs.next() ) { bitSet.clear( termDocs.doc() ); Filter them out }

Consolidating data too much may not address more detailed queries posed by the warehouse users During the ETL stage, you will also need to take care of resolving the data inconsistencies by scrubbing and transforming the source data For instance, during one of my OLAP projects, I found out that all the OLTP source systems had a different way of storing the employee information As a result, matching employee data from one database to another presented a challenge To handle cases like this, you would probably need a staging database as an intermediate cleansing store where you could perform several passes of data scrubbing and massaging before the data is loaded into the warehouse database For ETL tasks, you can use the Microsoft Data Transformation Services (DTS) DTS sponsors a graphical designer that you can use to define various tasks to extract, transform, and consolidate data from disparate sources.

would be thrown away immediately (Hibernate flattens the tabular result set to build the object graph). You may fetch as many one-to-one or many-toone associations as you like.

Figure 2.8 BrickHouse provides an interface for the ipfw command-line tools, giving you a simple way to set up software-based firewalling.

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