Featured Database Articles
The difference between the CONCAT function and the STUFF function lies in the fact that CONCAT allows you to append a string value at the end of another string value whereas STUFF allows you insert or replace a string value into or in between another string value. Read on to learn more about these functions and their real life usage.
There are several different approaches to automating deployment of SQL Server Infrastructure-as-a-Service (IaaS) virtual machines in Microsoft Azure. Marcin Policht examines an approach that involves uploading or creating a custom operating system image, including a pre-staged SQL Server installation, which is well suited for scenarios where you want to deploy a custom-configured SQL Server instance to multiple virtual machines with minimal effort and maximum consistency.
There are times when you don’t what to return a complete set of records. When you have this kind of requirement to only select the TOP X number of items Transact SQL (TSQL) has the TOP clause to meet your needs. In this article Greg Larsen explores how to select a subset of data from a record set.
In Oracle 12c an interesting problem arises with ANSI joins, star expansion and tables with 500 columns or more. Read on to find out what happens and how you can work around the problem.
SGA and PGA management can be confusing. Oracle can make it easier with two views that provide advice on how to set the associated parameters. Read on to see what those views are and how to read the data.
In Oracle 11.2 using analytic functions can generate 'interesting' cardinality values that are far larger than they should be. Read on to see just how far off those values can be.
Big data applications are not usually considered mission-critical: while they support sales and marketing decisions, they do not significantly affect core operations such as customer accounts, orders, inventory, and shipping. Why, then, are major IT organizations moving quickly to incorporating big data in their disaster recovery plans?
As big data application success stories (and failures) have appeared in the news and technical publications, several myths have emerged about big data. This article explores a few of the more significant myths, and how they may negatively affect your own big data implementation.
Most large organizations have implemented one or more big data applications. As more data accumulates internal users and analysts execute more reports and forecasts, which leads to additional queries and analysis, and more reporting. The cycle continues: data growth leads to better analysis, which generates more reporting. Eventually the big data application swells with so much data and querying that performance suffers. How to avoid this?