Today, we’re going to be looking at a kind of poor-mans’s-partitioning, using a view to union records from multiple tables. We’ll also take a look at when you would want this type of solution, some benefits and drawbacks, as well as ways to make things go faster.
partitioning
Reloading fact tables with zero downtime
If you’re working with data warehousing or reporting, you’ll recognize this problem as a recurring headache whenever you’re designing an ETL process for fact tables: If you want to completely reload all the rows of a fact table, you would typically start by emptying (or truncating) the fact table, and then load new data into it. But during the loading process, depending on what your job does, there won’t be any data in the table, or worse, it will be half-filled and incorrect. Worst-case: If your ETL job crashes, the table will remain empty. Now, if your ETL job takes an hour to run, that’s a problem.
Efficient data, part 2: Partitioning
This is the second part in a series on storing and modelling data efficiently. A great way to add performance to your data is to partition it. Like the name implies, partitioning splits a table or index into multiple partitions, so the data can be stored across multiple physical files and drives. Partitioning is a feature of SQL Server Enterprise Edition, but if you have one, you’re in luck!
sqlsunday.com 