SpotOn Accelerates Reporting with Fivetran Transformations for dbt Core
Technology Category
- Infrastructure as a Service (IaaS) - Cloud Databases
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Electrical Grids
- Equipment & Machinery
Applicable Functions
- Product Research & Development
Use Cases
- Leasing Finance Automation
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
About The Customer
SpotOn is one of the fastest-growing software and payment companies, offering comprehensive, cloud-based technology for businesses of all types and sizes. With the acquisition of Appetize, SpotOn’s client base grew to include enterprise venues like Madison Square Garden, Fenway Park, and MetLife Stadium, all with unique needs and complex data. The company's product team is tasked with efficiently turning captured customer transaction data into fast, reliable, informative reporting for their clients, while their internal data team harnesses data to optimize internal operations.
The Challenge
SpotOn, a rapidly growing software and payment company, faced significant challenges in efficiently transforming their captured customer transaction data into fast, reliable, and informative reporting for their clients. As the company scaled, the complexity of turning data into reporting for customers and internal stakeholders increased, with client data scattered across 30 unconnected MySQL databases. The engineering team lacked a central repository for efficient reporting generation. The existing data transformation process using stored procedures in Snowflake became increasingly complex and resource-intensive, with over 2,000 lines of code behind a single table. Changes were not automatically monitored or logged without version control, making quality assurance time-consuming and scaling required writing code from scratch for each new use case. This resulted in high costs, resource-intensive processes, and suboptimal results, impacting the company's ability to scale quickly to meet growing customer needs.
The Solution
To address these challenges, SpotOn turned to Fivetran, Snowflake, and dbt. Fivetran was used to seamlessly move all of their clients’ customer data from 30 disparate MySQL databases into Snowflake, providing a secure foundation of data that could then be transformed at scale to meet their various analytical needs. With Fivetran Transformations for dbt Core, SpotOn was able to automate faster, more reliable reporting for their clients, while saving time and cost. dbt Core modularized the 2,000 lines of transformation code, making data models more readable, easier to debug, and scalable with jinja templates. By integrating their dbt project into Fivetran, Fivetran Transformations for dbt Core could orchestrate those model runs automatically post-Fivetran connector load, eliminating the need for custom scripting, third-party tools, or DevOps. SpotOn's data engineering team also used dbt Cloud to turn large volumes of data from multiple databases into powerful internal analytics and BI, enabling collaborative, data-driven growth.
Operational Impact
Quantitative Benefit
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