In the era of big data analytics and streaming platforms, Apache Kafka and Schema Registry based data pipelines have made rapid progress. This has enabled implementation of large scale, high throughput data pipelines for enterprises. This paper presents a robust approach to managing and validating high throughput streaming data using Apache Kafka in conjunction with Apache Avro and Confluent Schema Registry. We delve into strategies for effective schema design, serialization, and enforcement of compatibility rules, focusing on how Avro’s compact binary format and Schema Registry’s versioning capabilities work together to safeguard against data corruption and serialization mismatches. We then discuss the challenges with schema registry in terms of performance for high throughput systems and governance for distributed teams. We propose two novel solutions in this paper to address the challenges. One solution is to implement a gRPC based schema registry validation to address latency in high throughput systems. The other solution is to implement a multi-layer governance model to maintain change control in schema registry for distributed teams implementing Kafka, Avro and schema registry based solutions.