page188
274
You may choose a variety of data distribution models and do not prescribe any
particular approach. For local Snowflake account consumption, as a rule of thumb
and discussed at length in Chapter 6, you should plan for Small warehouses for data
consumption.
Distribution Venues
Your focus within this book so far has been to develop an understanding of how
Snowflake works internally to minimize costs and maximize performance. It might not
be obvious why you are investigating data product distribution across disparate venues,
CSP locations, and software platforms.
Using Snowflake to master data product curation is an excellent strategy for success
though mastering typically occurs in a single location, preferably close to the inbound
data sources. But your clients probably operate globally and want to consume your
data products according to their individual needs, which may involve specific file
formats, data subsets, geographical locations, and alternate consumption products and
platforms.
Any data product provider must consider their offering as one or more inputs to their
client’s infrastructure, perhaps a single box within a complex environment. Adopting
a client-centric approach provides insight and informs your approach to data product
distribution.
Data replication can be both more complex and costly than first thought. Efficient
processing of the inbound data will also positively impact how you distribute the data
products too.
To provide broader context for the later investigation, let’s briefly examine some
distribution venues.
Snowflake Marketplaces
Snowflake operates both Private Listings and Marketplace, and each is maintained
through Snowsight, the default Snowflake-supplied user interface. Snowsight enables
client access to predefined objects within a Snowflake account.
Figure 8-5 illustrates the Snowflake-provisioned one-to-many models for data
interchange between a single provider and one or more consumers.
Chapter 8
parallelization