A number of OLTP systems store dimension data in SCD2-like tables in order to retain all the revisions whenever the dimension information changes. In certain situations, you may come across a need to join two or more SCD tables, while keeping all the versions information intact. Sound tricky? Not really.
This is the fourth installment in this series on SQL Server security. Today, we’re going to take a closer look at some key security concepts concerning object ownership and ownership chaining as well as execution context and impersonation in SQL Server. This could get technical.
This is the third installment in a series on assigning SQL Server permissions. In the previous posts, we’ve looked at security principals (the users and groups that carry the permissions) as well as the securables (the objects you want to control access to). Building on this understanding, we’re getting ready to look at the fabric that connects principals with securables: Permissions.
There are a number of layers in the SQL Server security model, giving you a nearly infinite number of ways to set up access control on your server and databases. Security is a huge topic, and there are literally entire books on it, so this series of articles is designed to give you just a quick overview of the SQL Server security model to get you started.
In this first installment, I’ll go through the different types of security principals that are available, as well as how they connect to each other.
In datawarehousing, a common scenario you may have encountered is the phenomenon of late-arriving dimension members. Imagine a situation where you get dimension members and fact data from two entirely different data sources, which may or may not provide data at the same time. Some fact data will probably be ready to load before its corresponding dimension member(s) arrive. How do you tag your fact data, and what consequence does that have for the integrity of your data warehouse?
This installment in the series on efficient data is on versioning changes in a table. The article is a re-post of a post I wrote in september on compressing slowly changing dimensions, although the concept does not only apply to dimensions – it can be used pretty much on any data that changes over time.
The idea is to “compress” a versioned table, so instead of just adding a date column for each version, you can compress multiple, sequential versions into a single row with a “from” date and a “to” date. This can significantly compress the size of the table.
This week’s installment in the series on efficient data is all about data types. Be smart about choosing them; The right type for the right job will not only store your data in less physical space and enforce data integrity better, but smaller indexes and tables will also improve overall query performance.
A quick way to improve I/O performance is to set a good fill factor. This installment of the series on efficient data discusses fill factors and page splits, and how they affect physical I/O performance.