A suggestive picture for the concept of database grading.

Database Grading, Part 1: Database Designs

picture by Greg via Flickr

While working on database projects, we often find ourselves doing reverse engineering. Reverse engineering is the inverse to normal development. Developers start with an application and work backwards to understand the software and infer its intent. Reverse engineering can apply to a variety of artifacts, such as hardware, programming code, and databases. Our focus here is on relational databases.

There are many reasons for database reverse engineering. One reason is to assess software quality. For example, you may want to assess the quality of a vendor product or an internal legacy application. Information systems revolve about a database, so you can use database quality as an indicator of software quality.

This article is the first in a series of two blogs that present our grading scale for database quality. We assign separate grades for the quality of a database design (this blog) and the underlying model (next blog). The design grade measures the quality of the database syntax. The model grade measures the quality of the semantic concepts underlying the database. Applications can have different design and model grades.

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Runners as a metaphor for agile

Agile Techniques Are Helpful with Databases

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I’ve been practicing agile database techniques for about twenty years now. My use of agile techniques didn’t start as an explicit plan. Rather it evolved over time as I was working on consulting projects. It made sense to look for ways of working faster and better and with greater customer interaction.

I can think of at least three kinds of agile database techniques.

  • For data modeling.
  • For data warehouse development.
  • For database reverse engineering.

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Focus on Quality

Data Warehouse Model Quality

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Data warehouses have a much different architecture and different business motivations than operational applications. For example, operational applications manage the day-to-day data needed to support the business. In contrast, data warehouse restructure operational data and place it in a format amenable to data mining and deep analysis. Operational applications rapidly read and write transactions with small amounts of data. In contrast, users only read data warehouses and can have extensive queries involving large data sets running for multiple minutes.

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Focus on Quality

Operational Model Quality

picture by xianrendujia via Flickr

Quality is an underappreciated aspect of data models. The purpose of a model is not just to capture the business requirements, but also to represent them well. A high quality model lessens the complexity of development, reduces the likelihood of bugs, and enhances the ability of a database to evolve. There are both qualitative and quantitative measures of quality.

This is the first of a two-part series. This blog discusses quality for day-to-day operational applications. Next month’s blog will discuss data warehouses.

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