We ran into several of those. Notably, it's difficult to achieve the 'real-time' promise of redshift because of the huge performance hit while loading data into the DB so you have to do it off-hours. You can update a replica and then 'hot-swap' it in but this gets expensive. For operational analytics it's better to go with one of the purpose-built timeseries databases and dual write to that and your data warehouse.
The biggest gotcha listed there is how Redshift gets bogged down if you're loading a lot of tables, frequently. You can't run a production Redshift with lots of tables at <15m latency. But in most cases, Redshift is still an overall better choice than a timeseries database because:
* It has all of SQL, including JOINs
* You can use it for both timeseries data and all your other data.
We ran into several of those. Notably, it's difficult to achieve the 'real-time' promise of redshift because of the huge performance hit while loading data into the DB so you have to do it off-hours. You can update a replica and then 'hot-swap' it in but this gets expensive. For operational analytics it's better to go with one of the purpose-built timeseries databases and dual write to that and your data warehouse.