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If you ve had any exposure to operational reporting, you ll already know many of the differences between reporting systems and traditional OLTP systems. Some of the these are shown in table 1.
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Table 1 OLTP versus reporting environment characteristics OLTP Queries Indexes Query volume Updates Few rows (1 50) Few Medium to high Small, frequent, dynamic Reporting Many rows (millions, billions, or more) Many Low Large, infrequent, scheduled
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The difference is even more fundamental. OLTP applications are designed based on a discreet set of specifications. Specific data is to be collected, and there are clear patterns about who will enter the data, at what point in the business process, and using what method. The first step to designing a business intelligence solution is to take several steps back to understand the business at its core: Why does it exist What is its mission How does the business plan to achieve its mission What key performance indicators (KPIs) need to be measured to assess success A business intelligence solution needs to be able to address not just the needs of today, but those of the future, and that can only be accomplished by obtaining a core understanding of the underlying business processes. I remember a past client who had chosen to implement a replicated OLTP data scheme for all of their reporting needs. They were suffering from numerous repercussions of this decision, including tempdb capacity issues, slow query times, and the inability to scale. When asked why they were not open to discussion about a business intelligence solution that provided more efficient analysis via OLAP cubes, they cited a prior attempt at a BI application that only addressed the queries for which it was designed. When the questions (queries) changed, the cube did not contain the information necessary to respond, and the whole project was aborted. This is why it is so critical to model the data warehouse based on the business, not on the specific reporting needs of the day.
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One of the hardest things for a relational DBA to come to grips with is the redundancy involved in data warehousing. It s disk intensive, to be sure. Often, a copy of a subset of the data is made for staging prior to loading the data warehouse, then there is the data warehouse itself, plus the cube store. This redundancy can be mitigated somewhat in the data warehouse design, but it s best to come to terms with the idea of redundancy as soon as possible. One exciting benefit is the potential to archive data from the operational system as it is loaded into the data warehouse, making the OLTP system more lean.
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BI for the relational guy
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The following is a high-level view of how a business intelligence project should be approached. This is intended to provide an overview to contrast with the approach taken in typical OLTP development projects.
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Determine overall strategy The general approach to a business solution is to develop an overall strategy to the data warehouse, determining how departments interact with each other and developing a high-level plan for how each subject area will be built out. In practice, I find that most companies skip this step. Departments in an organization tend to vary in their readiness for data warehousing, and cooperation from all departments is critical for making this step possible. Address a subject area Each subject area should be addressed in great detail, fleshing out the relevant dimensions and developing one or more star schemas to represent the business segment. This is done by conducting interviews with business subject-matter experts. One common pitfall I have found is clients insisting that the IT staff knows all there is to know about the business. It is true that they are intimate with the business rules that underlie the technology solutions that run much of the business, but that should not be confused with a core understanding of the business, including insights into where the business is heading. IT personnel are a valuable resource for knowing where data is housed and how to best get it into the data warehouse. The data model should be based on interviews with stakeholders within the departments represented in the subject area.
Develop the dimensional model Developing a dimensional model that represents the business based on the information gathered in the preceding step is paramount in a successful business intelligence solution. It s important to get this step right. An indication of a well-designed model is its ability to accommodate changes easily. Extract, transform, and load When the dimensional model has been established, it is time to determine data sourcing, or how to best populate the model. ETL processes need to be designed to accommodate the initial loading of the data warehouse, as well as ongoing incremental loads, which will, hopefully, be able to isolate new data in the source system from data that has been previously loaded. Develop the cube Cube design usually closely follows the dimensional design, which is one reason for the focus on the dimensional design. Analysis Services provides easy mechanisms for understanding the dimensional model and building dimensions and measure groups.
The remaining steps involve data validation, the automation of remaining processes, and more. This is a simplified, high-level description of the approach to building a
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