Rob Farley digs into Compression Delay as part of real-time operational analytics:
The thing with Operational Analytics is that the analytical data, reporting data, warehouse-style data, is essentially the same data as the transactional data. Now, it doesn’t look quite the same, because it’s not been turned into a star-schema, or have slowly changing dimension considerations, but for the purposes of seeing what’s going on, it’s data that’s capable of handling aggregations over large amounts of data. It’s columnstore.
Now, columnstore data isn’t particularly suited to transactional data. Finding an individual row within columnstore data can be tricky, and it’s much more suited to rowstore. So when data is being manipulated quite a lot, it’s not necessarily that good to be using columnstore. Rowstore is simply better for this.
But with SQL 2016, we get updateable non-clustered columnstore indexes. Data which is a copy of the underlying table (non-clustered data is a copy – clustered data or heap data is the underlying table). This alone presents a useful opportunity, as we can be maintaining a columnstore copy of the data for analytics, while handling individual row updates in the rowstore.
Read the whole thing.