Zip’s Sr. Product Manager Moss Pauly worked with the Data Engineering team to modernize their data platform and build a fit-for-purpose modern data stack. As Zip’s team was evaluating data solutions, their top priorities were seamless integration, cost scalability, and real business use cases.
Zip built out a best-of-breed modern data stack with Snowflake, dbt, Snowplow, Fivetran and Census. One of the biggest benefits for Zip was that each of these tools were the best-of-breed in their domain, yet they had tight integrations with the other components.
Snowflake Data Cloud
As compute and storage are the core of the modern data stack, choosing a data warehouse was Zip’s most critical decision. They evaluated multiple solutions extensively and ultimately decided on the Snowflake Data Cloud. Over the past 18 months of using Snowflake, Zip’s data team has been very satisfied with its ease of use, performance, and seamless integration with dbt.
Data Transformation: dbt
Zip needed to store business logic and transforms to build data models in a scalable, future-proof way. Dependency management and documentation were both significant pain points of their previous transformation stack. They chose dbt cloud and haven’t looked back, with 1000+ models in production after 18 months. The cloud based IDE has been a game changer, and they’re also diving deep into the power of macros and incremental models.
Event Collection: Snowplow
With millions of customers, Zip’s previous stack was unable to deal with their sheer volume of raw events. Snowplow appealed to the data team because it was open source, flexible, and didn’t have a SaaS cost tied to Events/Month. Zip’s data team was explicit that they did not want a solution where cost concerns would limit what they could track, and they wanted to retain first-party ownership of their events.
With their first-party event collection solution solved, Zip knew they needed a solution for third-party data ingestion. They didn’t want their engineers spending time wrangling third-party data APIs and wanted to capitalize on standard models in dbt for third-party data sets where possible. They evaluated a few options in this space, but Fivetran clearly came out ahead. They had coverage for all their third-party integrations, thorough documentation of data structures and pre-packaged dbt transform availability.