The possibilities of tuning in Snowflake are limited in contrast to Teradata. However, the numerous options Teradata offers are unnecessary due to the architecture. There is no workload management comparable to Teradata (TASM) in Snowflake. Workload tuning is done by defining virtual warehouses. In principle, nothing more than independent clusters of computers accessing the same data. By forming clusters, performance can be optimized at the highest level. Snowflake uses intensive disk-level caching. The Disk Cache is a combination of SSD and memory. When creating virtual warehouses, it is advisable to create users that execute the same queries in the same virtual warehouse. Then the disk cache can be used optimally. Snowflake also offers a cache of the result set, which is available to every user independent of the virtual warehouse. This cache holds the result sets of all queries of the last 24 hours. Each time the cache is used, the counter starts from zero again. This is far more extensive than caching the query plan in Teradata. Teradata offers a block-level cache using Teradata Virtual Memory. The cache is available to all data warehouse users but is purged frequently and is temperature-based. But it's not a cache of the result set. It's caching of data blocks only. It may be tempting to shut down a Snowflake virtual warehouse as soon as possible to save costs, but the disk cache is also lost when the warehouse is closed. Here, costs must be weighed up against performance. This is not an issue at Teradata. The system is available 24/7. Snowflake does not use indexes or partitioning that the user can determine. However, it is possible to define a cluster key to locate specific column values within one or a few micro partitions: ALTER TABLE Customer CLUSTER BY (CustomerId);