Featured Database Articles
Traditional deployments of Azure SQL Database involve identifying projected resource requirements and selecting individual Azure SQL Database instances. For fluctuating workloads, this frequently results in over- or under-provisioning. To address this challenge, Microsoft offers another approach to sizing Azure SQL Database that relies on Elastic Database Pools. Read on to learn more.
Greg Larsen explores the different ways that you can encrypt your existing confidential data using Always Encrypted Columns in SQL Server 2016.
In this third article of the exploring SQL Server 2016 Always Encrypted series, Greg Larsen looks at the differences between an Always Encrypted column that uses an encryption type of Deterministic and those that use encryption type of Randomized.
There are several ways to ensure unique records in an Oracle table, but are some better than others? Read on to see how each method compares.
Oracle can still have issues with ANSI join syntax, producing sub-optimal translations and possibly wrong results. Read on to see how Oracle processes ANSI join syntax and how that translation process can create some problems.
In Oracle 220.127.116.11 shared cursor memory in some subpools can increase dramatically over time, resulting in having to restart the database. Read on to see how this was discovered and how to check the subpool memory allocations.
Many big data applications are designed, built and installed without a formal load test. This is unfortunate, as load testing gives the database administrator quite a lot of valuable information. It may make the difference between poor and acceptable big data performance. This article reviews big data application implementation practices with some proactive tips on load testing to make your production implementation a success.
Big data applications are now in place to support business analytics processing. However, many standard performance tuning options (such as indexes) may no longer apply. Here we investigate the differences between operational systems and big data systems, and present common options for big data performance tuning.
Many IT enterprises have created big data applications to store and analyze massive amounts of historical data. Should these applications be installed in their own exclusive environment, or tightly integrated with current operational systems?