Featured Database Articles
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.
There are different techniques to optimize the performance of SQL Server queries but wouldn’t it be great if we had some recommendations before we started planning or optimizing queries so that we didn’t have to start from the scratch every time? This is where you can use the Database Engine Tuning Advisor utility to get recommendations based on your workload.
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.
Oracle provides several levels of compression, with BASIC compression requiring no additional license cost. Read on to find out how this level of compression works and what you may expect when using it.
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?
Your big data repository won't simply add another twelve months of data over the next year. More data is coming, more categories of data will be created, and your analytical environment must expand to fit future needs. But size alone won't be your only problem. In the rush to accumulate a sufficient amount of valuable data and implement a business analytics environment that can produce usable results, several items may have been ignored, postponed, or simply forgotten. These missing details can make or break your company in the future.