How Apna, Glance, & Meesho Are Innovating with Data Streaming For Consumers in India

Apna, Glance, and Meesho describe how data streaming with Apache Kafka and Confluent Cloud supports their rapid growth, real-time services, and customer experiences in India. Apna replaced monolithic systems with an event-driven microservices architecture that uses Kafka for communication, scheduling, pipelines, and services such as job matching and application tracking, while Confluent Cloud provides managed scalability and high availability. Glance processes large volumes of lock-screen interaction data to tailor content to user interests, using Kafka to decouple data producers and consumers while relying on managed tooling for schema governance, lineage, observability, and secure data access. Meesho uses Kafka as the first destination for ingested data supporting recommendations, seller and consumer experiences, and business decisions, with Confluent Cloud helping it accommodate demand spikes, elastically scale infrastructure, and reduce operational overhead. The examples present data streaming as a foundation for scalable architectures, data democratization, and faster development, while also noting governance and infrastructure-management challenges as streaming use expands.

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