When building a data warehouse, you often need to flatten the data when you move it from the Raw tables to the Stage tables. Flattening data can be a difficult concept for new warehouse designers to grasp, so here are two detailed examples.
If you're inexperienced with using SSIS, creating a data warehouse might seem like an impossible task—but it's not if you use some different strategies. For example, you can use a wizard to initially create an SSIS package that will move data from the source system into the data warehouse.
The quest for the Golden Record to achieve a single, accurate and complete version of a customer record is worth the pursuit to attain survivorship. Record matching and consolidation are only the beginning. Melissa Data takes a new approach. Learn how to apply intelligent rules based on reference data to make smarter and better decisions for data cleansing....More
On SQL Servers where Availability Groups (or Mirroring) isn’t in play, I typically recommend keeping a combination of on-box backups along with copying said backups off-box as well. Obviously, keeping databases AND backups on the SAME server is the metaphorical equivalent of putting all of your eggs in one basket – and therefore something you should avoid like the plague....More
One of the biggest strengths of AlwaysOn Availability Groups is that they allow DBAs to address both high availability and disaster recovery concerns from a single set of tooling or interfaces. But, this doesn’t mean that you won’t still need backups....More