How Paytronix Implemented Data Mesh with Coalesce Projects


Moving Last-Mile Data Transformations from Looker to Coalesce Optimized Snowflake Consumption, Reduced Cost by 50%

Since adopting Coalesce, the data team at Paytronix has unlocked rapid insights, predictive modeling, and gen AI-powered tooling within its platform, which is used by more than 1,800 restaurants and convenience stores in the U.S. Next, the team turned to a new challenge: the business analysts were still doing last-mile data transformations in Looker, which were difficult to manage and generated high Snowflake consumption. The solution: implementing Data Mesh with Coalesce Projects.

Watch this on-demand webinar, to hear Jesse Marshall, Director of Data Science, and Susan Kolesnikov, Data Engineer, at Paytronix, share how they successfully enabled the analytics team to run transformations in Coalesce, allowing end-to-end visibility into their data pipelines and reducing costs by 50%.

Hosted by Armon Petrossian, CEO and Co-founder of Coalesce, this session covers:

  • Challenges resulting from doing data transformations in Looker, including high costs and data quality issues
  • Rolling out Projects in Coalesce: learnings and best practices
  • Successfully implementing Data Mesh: how-to and results


Susan Kolesnikov
Jesse Marshall
Director of Data Science
Armon Petrossian

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