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The short answer is that’s there’s no good reason.
SOA, the Service-Oriented Architecture, is a popular approach for organizing business functionality. From the literature it’s clear that SOA’s emphasis is on programming. That’s ironic given that most SOA services deal with the reading and writing of data. We seldom see SOA developers creating substantive data models. This is a flawed approach that needs correction.
Continue reading Why Doesn’t SOA Use Data Models?
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Developers routinely use data models for defining database structure. This is beneficial, but it uses only part of data modeling’s power. Data models not only capture data structure, but they also express the potential for computation. Traversals of models can provide blueprints for resolving use cases, phrasing SQL queries, and assessing quality.
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A data warehouse is a database dedicated to decision support and business analysis. The inputs to a data warehouse are data from the day-to-day operational systems. A data warehouse integrates the input data and restructures it so that it is amenable to data mining.
With an agile approach developers build a data warehouse rapidly to get it into the hands of business users so they can give feedback. Where possible, SQL code substitutes for programming and ETL scripts.
The course is organized about a threaded case study for the retail industry.
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So you have prepared a data model. You’re proud of your work. Now how do you explain the model to others? You want them to understand your work and you want their feedback. We can think of three techniques for presenting data models.
Continue reading Presenting Data Models
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We define agile analytics as the iterative and rapid processing of data for decision support. There is minimal upfront investment. Developers instead process source data and build queries incrementally as business needs emerge.
Big Data and data warehouses are both technologies for realizing agile analytics. They differ in several ways.
Continue reading Agile Analytics
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Few database projects start with a clean slate. Many operational applications have legacy databases as a source of data and ideas. Analytical applications have the operational databases that are feeding data. Developers often encounter existing databases that are poorly documented and need to figure them out. We use the term database archaeology to refer to the study of database artifacts.
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I’m proficient with ERwin and recently had the opportunity to try ER/Studio. I must say that I was impressed. ER/Studio is the best data modeling tool that I’ve seen to date. My experiments did not cover every aspect. But I did look at enough features to form a clear opinion.
Continue reading Comments on ER/Studio
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Over the years we’ve seen a number of projects where application architects use a generic layer to hide a database. This is a common approach with object-oriented languages accessing a relational database. Application code accesses the layer which in turn accesses the database. The use of a generic layer can be a valuable technique, but it is overused. Some architects seem to be unaware that there are other possibilities.
Continue reading Be Wary of Generic Database Layers
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The UML is a popular notation for modeling software. Even though the UML was mostly developed for programming, it is also relevant for databases. This article takes a critical look at using the UML for databases.
Continue reading When To Use the UML for Databases
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Generally the most pressing problems for software development concern quality, time to market, and cost. If you define referential integrity (RI) in your software you can improve all three of these items. RI improves quality by ensuring that data references truly exist and cannot be dangling. RI also reduces development time and lowers cost as it takes much less effort to define RI than to program the equivalent application code.
Continue reading Do Use Referential Integrity