SQL for Data Warehousing and Analytics

This is the next part in my on-going series of posts on the topic of how to successfully manage sandboxes within an Oracle data warehouse environment. In Part 1 I provided an overview of sandboxing (key characteristics, deployment models) and introduced the concept of a lifecycle called BOX’D ( B uild, O bserver, X -Charge and D rop). In Part 2 I briefly explored the key differences between data marts and sandboxes. Part 3 explored the B uild-phase of our lifecycle. Now, in this post I am going to focus on the O bserve-phase. At this stage in the lifecycle we are concerned with managing our sandboxes. Most modern data warehouse environments will be running hundreds of data discovery projects so it is vital that the DBA can monitor and control the resources that each sandbox consumes by establishing rules to control the resources available to each project both in general terms and specifically for each project. In most cases, DBAs will setup a sandbox with dedicated resources. Howe...

Data Warehousing and Big Data were at the heart of this year’s OpenWorld conference being across in a number of keynotes and a huge number of general sessions. Our hands-on labs were all completely full as people got valuable hands-on time with our most important new features. The key areas at this year’s conference were: Big Data SQL - One Fast SQL Query for All Your Data Database In-Memory - Powering the Real-Time Enterprise Mutitenant - Plug your data warehousing Into the Cloud All these topics appeared in the main keynote sessions including live on-stage demonstrations of how each feature can be used to increased the performance and analytical capability of your data warehouse. If you want to revisit the most important sessions, or if simply missed this year’s conference and want to catch up on all the most important topics, then I have put together a book of the highlights from this year’s conference. The booklet is divided into the following sectio...