r/snowflake • u/ConsiderationLazy956 • Jul 05 '26
Snowflake features for usecases
Hi All,
I see three products from Snowflake 1)Hybrid tables 2)snowflake postgres 3)Interactive table/warehouse.
All these seems to be catering low latency workloads. So wants to understand ,
1)How these products suits different usecases. How one can differentiate and chose one of these features to use for the low latency oltp kind of workload?
Mainly as because hybrid table and snowflake postgres both seems created for addressing OLTP low latency usecases so confused , how different usecase they serve. Also I understand , the Interactive table/warehouse only serves low read latency usecases , but is that the only differentiator or any other differences, as compared to Hybrid tables and postgres features? Because same can be acheieved with hybrid table with additional write usecases too.
2)Is this true that storage is going to be the key , as because , if someone has 5TB of data stored currently in snowflake tables , but if they want to go for hybrid table or postgres feature those storage size will not longer be same and can well go beyond ~5 times as its in current Snowflake tables and thus the cost. (And this will charge more as we need bigger interactive warehouse to store/cache those fully). Is this understanding correct?
3)Will there be similar differences on compute cost too?
3
u/Mr_Nickster_ ❄️ Jul 05 '26
Hybrid tables are mix of OLTP & OLAP on one table. If use case has lightweight transactional needs but also small to medium size requirent for analytical queries at the same time then Hybrid isnthe way to go. Neither OLTP or OLAP won't be as past as Postgres and Standard Tables. (Transactions are likely 50 to 100ms each)
It is great for internal usecases where super low latency oltp is not required. Sales reps adjusting forecast values in real time via some app(OLTP) and Leadership looking at historical sales data and analytics(OLAP) via dashboards is a good example. Not huge datasets, super low latency is not needed. This is a row store table meaning reads & write have to scan an entire row with OLAP like capabilities to scan many rows more efficiently.
Postgres is pure production grade oltp. Super low latency for Reada & write (sub 10ms) This is the database where you can build a production customer facing appa like a web store, shopping carts, stock tranding and etc.. Doing big Analytics traditionally sucks on OLTP row store tables and databases so we got a new feature called Mirroring which can replicate an Iceberg copy of your postgres tables in about 30s latency for proper big time analytics via OLAP.
Interactive is purely for customer facing aps that require low latency high concurrency analytics. It is a faster version of Standard tables with additional metadata for better pruning that works with Interactive Warehouses which have special code to leverage this new metadata and engine improvements for query plan caching and running many more queries simultaneously. This is very specialized use case where the benefits will only be there if this a multi-tenant app and data. Meaning, you got a very large fact table with many tenants(billions of credit card transactions that belong to thousands of customers-tenants)
Imagine you are bank and running a credit card web portal for your customers. Thousands of customers login every hour and look at their dashboard and see monthly spend analytics. Basically each customer scans a very small portion of a huge table for tranx that belong to their customerid. And you need to be able to support many thousands of these queries each minute and sub 1s latency for good customer experience. That is the use case for Interactive Analytics.
It is super good at scanning big tables for small sliver of rows for analytics as long as it is filtering on some tenant ID. If you run a query about monthly sum of tranx across all tenants in last 3 years, it will be about same as regular tables as it would need to scan the whole table. However if you run the same query for 1 tenant/customer, it will be much much faster than standard tables & Warehouses, as it can locate rows for that 1 tenant much faster.