Cost Efficient Snowflake CI

Businesses are now able to produce more valuable data insights than ever thanks to an explosion of tools that support the analytic engineering function. Cloud data warehouses like Snowflake, Amazon Redshift or Google BigQuery have improved their ability to collect, store and run complex analyses in a single location. It can be easy to be caught off guard by runaway cloud service costs, and e-commerce teams often find themselves unequipped to manage these new expenses.

Data teams can help avoid these situations by embracing e-commerce operations as a shared responsibility. Our data team here at Backchannel applies the same operational rigor to our own processes that we do to our customers. In this article, we’ll discuss how Backchannel’s data team implemented targeted cloning strategies in Snowflake to keep up with the pace of innovation while not breaking the bank.

How does Backhannel handle data CI

Backchannel leverages an extract, load, transform (ELT) paradigm. At the core of our data operations is Snowflake, which acts both as a refinery for raw data (in conjunction with dbt) and as a warehouse for cleaned data to be consumed by a variety of functions across marketing automation, sales operations, underwriting, and capital markets, to name a few. Snowflake is one of our newest products, having been added to our data toolbelt in October 2024. We chose Snowflake for its ability to scale alongside us, both in terms of handling our growing workloads and providing features for our developers to continue to iterate quickly.