Demystifying dbt models at scale
Written by The Count Team
Streamline data pipelines with Count: Enhance transparency, collaboration, and speed in managing 400+ dbt models and 30+ data sources, delivering outputs 3x faster.
Justin Freels, Senior Manager of Data Engineering at The Stable, part of Accenture Song, recently sat down with Count’s Mico Yuk to talk about his experiences and challenges managing a large-scale data stack that sources data from over 30 platforms, is transformed across over 400 dbt models, and serves hundreds of customers. He and Mico break down how the Count canvas has helped his team build a truly transparent data pipeline, work more closely with stakeholders, and deliver final outputs 3x faster.
The Stable, part of Accenture Song, helps brands build digital commerce channels and understand their performance across various retailers. For Justin’s team, this requires maintaining a complex pipeline of data. Data is sourced from a variety of retail (e.g. Walmart, Target, Amazon, Shopify) and media (e.g. Facebook, Google Analytics) sources, then merged, transformed, and presented as internal reports, and shared with clients via an embedded analytics platform.