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The forthcoming Unistore workload joining transactional and analytical data via
hybrid tables may change this perception.
hybrid tables are not yet generally available.
You can find further details on Unistore at
https://www.snowflake.com/en/data-
cloud/workloads/unistore/
.
Rapid data ingest options via data streaming requiring low latency for low-data
volume is another common use case. I recommend the “Tour of Ingest” at
https://
quickstarts.snowflake.com/guide/tour_of_ingest/index.html
.
For those looking to understand a much wider suite of Snowflake use cases, please
investigate all the various quick starts at
https://quickstarts.snowflake.com/
.
Provision or Consumption Model
Performance tuning in a provision-based model has fixed constraints; you cannot simply
pop down to the data center and plug in more memory or replace your hardware with
faster devices. Without preplanned system downtime for upgrades along with the service
disruption caused, you are limited to eking out every small performance increment
from your existing hardware using any and all levers provided by your operating system
vendor, RDBMS vendor, network tooling, and storage vendor. And all of these require
deep subject-matter experts (SMEs) in each topic to interact and define optimal patterns
for repeatability. Well, that’s the intent, but as you all know, reality does not always
match expectations.
In sharp contrast, a consumption-based model such as Snowflake removes
many historically familiar tuning options and levers; no longer are you able to tune
the operating system and change the RDBMS kernel settings. Instead, Snowflake
implements a managed service where you pay for what you consume, and this brings
about totally different challenges. Gone are the provision-based constraints, but leaving
aside the shift to a security focus, which a consumption-based model requires, you
replace the provision-based hardware constraints with two new major challenges: cost
and performance optimizations.
ChaptEr 1
tuning thE SnowFlakE Data ClouD