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of writing, this is a direct pass-through cost from your cloud service provider (CSP). You
also incur costs when you replicate data across regions and when you egress data from
one CSP to another external location.
Unlike legacy products, Snowflake provides few levers and switches to influence
system behavior and application performance, instead preferring to hide complexity
to enable developers to focus on delivering business benefit. You might be lulled into
a false sense of security by the ease with which you can port your applications into
Snowflake, but this can be an expensive mistake.
Tuning the Snowflake Data Cloud is a project-oriented book with a hands-on
approach to identifying migration and performance issues with experience drawn
from real-world examples. As you work through the examples, you will develop the
skill, knowledge, and deep understanding of Snowflake tuning options and capabilities
while preparing for later Snowflake features as they become available. Your Snowflake
platform will cost less to run and will improve your customer experience.
It is important to note that Snowflake is a constantly evolving product, and therefore
best practices will change over time. You should not expect the advice, hints, and tips
in this book to be static; this book offers what I know right now, with both eyes on
the future.
Regardless of your relational database management system (RDBMS) experience, it’s
safe to say some of your performance tuning skill, knowledge, and expertise is directly
transferable. Equally, some prior learning is not transferable; a degree of unlearning will
be required, and for those working on both legacy RDBMS and Snowflake, the operating
paradigms are distinctly different.
I next discuss some common themes.
Use Cases for Snowflake
Fundamentally, the underlying CSP storage (whether S3, Azure Blob, or Google Cloud
storage) and Snowflake’s immutable storage policy dictate the supported transaction
style, with data warehousing preferred over online transaction processing (OLTP).
As a general rule of thumb, Snowflake prefers high-volume bulk-load operations
supporting analytics workloads. Low-latency, high-volume transactions are not yet
common workloads for Snowflake.
ChaptEr 1
tuning thE SnowFlakE Data ClouD