This is a guest post by Oleh Khoruzhenko, Senior Staff DevOps Engineer at Bazaarvoice, in partnership with AWS.
Bazaarvoice is an Austin-based company powering a world-leading reviews and ratings platform. Our system processes billions of consumer interactions through ratings, reviews, images, and videos, helping brands and retailers build shopper confidence and drive sales by using authentic user-generated content (UGC) across the customer journey. The Bazaarvoice Trust Mark is the gold standard in authenticity.
Apache Kafka is one of the core components of our infrastructure, enabling real-time data streaming for the global review platform. Although Kafka’s distributed architecture met our needs for high-throughput, fault-tolerant streaming, self-managing this complex system diverted critical engineering resources away from our core product development. Each component of our Kafka infrastructure required specialized expertise, ranging from configuring low-level parameters to maintaining the complex distributed systems that our customers rely on. The dynamic nature of our environment demanded continuous care and investment in automation. We found ourselves constantly managing upgrades, applying security patches, implementing fixes, and addressing scaling needs as our data volumes grew.
In this post, we show you the steps we took to migrate our workloads from self-hosted Kafka to Amazon Managed Streaming for Apache Kafka (Amazon MSK). We walk you through our migration process and highlight the improvements we achieved after this transition. We show how we minimized operational overhead, enhanced our security and compliance posture, automated key processes, and built a more resilient platform while maintaining the high performance our global customer base expects.
The need for modernization
As our platform grew to process billions of daily consumer interactions, we needed to find a way to scale our Kafka clusters efficiently while maintaining a small team to manage the infrastructure. The limitations of self-managed Kafka clusters manifested in several key areas: