Featured Database Articles
Arshad Ali discusses SQL Server 2016 Query Store and how it greatly simplifies the performance troubleshooting process.
Row Level security is all about restricting database users from being able to view or update rows based on who they are and what roles they are in. But there are some users who you “might” not want to restrict access at the row level at all, like the database owner, or someone with sysadmin permissions. In this article Greg Larsen shows you how to disable RLS for database administrators (users in the sysadmin role) and database owners.
In a recently published article we described the basic characteristics of Azure SQL Database elastic database jobs. As we pointed out, their name is somewhat misleading since it implies that they are intended specifically for Elastic Database pools. However, their scope can include any custom-defined group of Azure SQL Databases, although the implementation in such cases is not yet available via the Azure portal but requires the use of Azure PowerShell (or the equivalent REST API). In this article, we take you through the sequence of steps illustrating this scenario.
When using Oracle 184.108.40.206 in a Grid configuration the srvctl utility can change the group on the Oracle executables, creating problems with locating the spfile and with disk access.
Sometimes it's good to re-think how to write a query; set operations can provide performance benefits over 'straight SQL'. Read on to see an example of this in action.
Deletes may be more 'expensive' than the Oracle optimizer reports; read on to see why.
What is next for big data? Some experts claim that data "volumes, velocity, variety and veracity" will only increase over time, requiring more data storage, faster machines and more sophisticated analysis tools. However, this is short-sighted, and does not take into account how data degrades over time. Analysis of historical data will always be with us, but generation of the most useful analyses will be done with data we already have. To adapt, most organizations must grow and mature their analytical environments. Here are the steps they must take to prepare for the transition.
Business Intelligence (BI) has matured over the past two decades. The next few years will be critical for the information technology staff, as they attempt to integrate and manage multiple, diverse hardware and software platforms. This article addresses how to meet this need, as users demand greater ability to analyze ever-growing mountains of data, and IT attempts to keep costs down.
Some companies have been slow to acquire big data applications. They discovered that modern hardware platforms and database management systems were more than adequate for most of their business analytics needs. Such needs share several common attributes, including analytics run against operational systems, where the analytics logic and engine were close to the object data. This meant that companies could avoid complex and high-volume data movement and extract-transform-load (ETL) strategies while executing queries against already existing, well-tuned databases. In this article we introduce the concepts of strategic and tactical analytics, and how best to support these methods with your current IT infrastructure.