Featured Database Articles
Arshad Ali demonstrates how you can use the command line interface to tune SQL queries and how you can use SQL Server Profiler to capture the workload for tuning with Database Engine Tuning Advisor.
In general, Microsoft Azure provides two types of SQL Server based relational data services, Azure SQL Database Platform-as-a-Service (PaaS) and deploying a SQL Server instance into an Azure Infrastructure-as-a-Service (IaaS) virtual machine. There are obvious implications in regards to the management overhead associated with IaaS-based solutions. Marcin Policht reviews methods that allow you to minimize this overhead, focusing in particular on deployment tasks.
Greg Larsen discusses the ROLLUP, CUBE and GROUPING SETS operators. These operators are used with the GROUP BY clause and allow you to create subtotals, grand totals and superset of subtotals. Read on to find out more about these additional GROUP BY operators.
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.
Overflow data in an index-organized table is usually put in its own tablespace. Sometimes it isn't; read on to see what overflow data is and how to change where it's located.
Odd errors can result in odd 'solutions', and Oracle has one for the MERGE statement. Read on to see what it is and what it fixes.
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?
Big data is everywhere, and most large IT enterprises have installed one or more big data applications. These applications provide fast access to large stores of data, usually customer or sales data. Your technical staff that supports these applications and the systems that analyze and consume the data didn't exist ten years ago. Who are these new IT professionals, and how should you manage them?