Using a view, we were able to create a “partitioned” Polybase experience, similar to what we had in SQL Server 2000. This form of poor man’s partitioning allows us to segment out data sets and query them independently, something which can be helpful when storing very large amounts of data off-site and only occasionally needing to query it. The thing to remember, though, is that if you store this in Azure Blob Storage, you will need to pull down the entire table’s worth of data to do any processing.
This leads to a concept I first heard from Ginger Grant: pseudo-StretchDB. Instead of paying for what Stretch offers, you get an important subset of the functionality at a much, much lower price. If you do store the data in Azure Blob Storage, you’re paying pennies per gigabyte per month. For cold storage, like a scenario in which you need to keep data around to keep the auditors happy but your main application doesn’t use that information, it can work fine. But if you need to query this data frequently, performance might be a killer.
For Polybase tables without the ability to perform external pushdown, coming up with a good partitioning strategy is probably one of the two best ways to improve performance, with creating a Polybase scale-out cluster the other method.