Case Study: iFood cuts data pipeline costs and boosts real-time insights with Databricks

iFood uses DLT to cut costs and reduce pipeline maintenance

iFood, a leading food delivery platform in Brazil, was processing billions of events daily across apps, logistics, and partner systems, but its complex data architecture and siloed pipelines made it hard to deliver reliable real-time insights at scale. The company needed a more scalable, efficient, and governed platform, and turned to Databricks and Spark Declarative Pipelines to support its growing analytics and modeling demands.

Databricks helped iFood unify its data architecture with Spark Declarative Pipelines, enabling real-time ingestion, quality checks, and a medallion-based design for massive datasets. The results were significant: ingestion latency dropped from hours to seconds, pipeline maintenance efforts fell by about 70%, and data processing and storage costs were reduced by 67%, cutting monthly expenses from tens of thousands to just thousands of dollars.

iFood

Gabriel Campos

Head of Data and AI