Django and sub-classing Part 2 (#57 – #58)
In this post we are looking at the basic performance of the different sub-classing options in Django that have been discussed in previous posts. The three options are listed again below. […]
Data Modelling Podcast – Connections Part 1
We now make available the third episode of the DjangoModelling.com Data Modelling Podcast. This session talks about connections in data modelling. A new podcast in the series will be released on the first Monday of each month (God willing).
Data Modelling Podcast – Chickens and Eggs, and Pots
We now make available the second instalment of the DjangoModelling.com Data Modelling Podcast. This session talks about chickens and eggs in data modelling, and then discusses a simple example of data modelling. A new podcast in the series will be released on the first Monday of each month (God willing).
Django and sub-classing (#54 – #56)
When modelling data, we often need to distinguish between different types of objects. For example, a mapping application may need to store information about both roads and lakes, or a library catalogue may need to store data about both books and DVDs. With these examples, the objects can be quite diverse and the details stored [...]
Data Modelling Podcast – Introduction
We now make available the inaugural DjangoModelling.com Data Modelling Podcast. This session talks about data modelling in the past and our reasons and methods for modelling data now. A new podcast in the series will be released on the first Monday of each month (God willing).
Hardware hides performance pitfalls (#52)
Data modelling is not the only limiter of performance. When I first started working with databases, performance in all aspects of computing was much worse than it is now. However well an expert might craft a query, and however simple the query might be, technical restrictions would always limit the performance to just a few [...]
Missing data (#51)
A very common problem with databases is missing data. Missing data makes searches difficult (or impossible) and statistics unreliable. Two common reasons for this are: Records contain optional fields which have been left empty. Records are completely missing because some of the mandatory information was not available. Since the missing data was in mandatory columns, [...]
Which RDBMS? (#50)
In this series of newsletters, we concentrate on data modelling in general and discuss relational databases when we need to consider specific applications. In general, we do not consider the so-called “NoSQL” databases. Relational database management systems lend themselves to the use of fixed schemas and also provide easy type checking. […]
Importing data (#49)
Often the data we are modelling must be imported into a database. Data importing can be a complex and difficult task. Often the data must come from several different sources and the importing will occur in several steps. Sometimes the data for different tables can come from different sources and must be imported at different [...]
Performance (#48)
In a database, good performance is vital. So, how can we be sure we will have good performance in a project? What will make a data model good for performance or bad? And what is good performance anyway? Let’s start by defining good performance. […]