Case Study: Apna achieves near real-time data freshness with Onehouse

Scaling Core Data Infrastructure to Support a Rapidly Growing Careers App

Apna, the largest professional networking and job site in India, faced significant data infrastructure challenges with its existing Google BigQuery architecture. Their ingestion via Hevo Data was expensive, slow, and unreliable, with limited time travel and a lack of schema alerts. This hindered their ability to support their 33 million users and AI-powered job-matching engine effectively. They partnered with Onehouse to overcome these obstacles.

By implementing Onehouse's managed data lakehouse platform, Apna adopted a medallion architecture with Apache Hudi on Google Cloud Storage. This solution replaced their batch ETL pipelines with Onehouse's low-code, incremental pipelines. The results were substantial: data freshness improved from hours to minutes, time travel extended from 7 days to years, and compute costs were reduced. Onehouse also provided real-time schema alerts and created a single, central source of truth for all their use cases.

Apna

Ronak Shah

Head of Data