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.
Series
The SQL Server security model, part 2: securables
Continuing on last week’s post on security principals, this week’s installment in the series on SQL Server security takes a look at securables.
The SQL Server security model, part 1: principals
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.
Late-arriving dimension members
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?
Efficient data, part 6: Versioning changes
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.
Efficient data, part 5: Datatypes
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.
Efficient data, part 4: Fill factor
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.
Efficient data, part 3: Compression
This is the third part in a series of articles on optimizing large volumes of data. This part is all about using compression, and since I wrote an article on that a few months ago, I’m not going to re-invent the wheel. So this article is a repost of my july article on compression.
Efficient data, part 2: Partitioning
This is the second part in a series on storing and modelling data efficiently. A great way to add performance to your data is to partition it. Like the name implies, partitioning splits a table or index into multiple partitions, so the data can be stored across multiple physical files and drives. Partitioning is a feature of SQL Server Enterprise Edition, but if you have one, you’re in luck!
Efficient data, part 1: Normalization
We’ve talked a lot about optimizing queries and query performance, but we haven’t really touched that much on the storage and data modelling aspects. In this series of post, I’ll run through some basic tips on how you can more efficiently model and store your data, which may come in particularly handy when you’re working with large databases and large transaction volumes, but a lot of it also makes good design sense in smaller databases.
In this first article, we’ll cover the normalized data model.
