Kafka for Developers - Data Contracts Using Schema Registry - Evolving the Schema - Consumer Fails to Read the New Schem

The lecture discusses the challenges of evolving schemas in data systems, particularly focusing on the issues that arise when the consumer and producer do not share the same schema. It demonstrates an experiment where a schema change is made without updating the consumer, leading to errors. The lecture concludes by introducing the concept of a Schema Registry, which helps manage schema evolution and compatibility, ensuring efficient data processing and storage.

What is the initial approach to handling schema changes to avoid consumer issues?

• Do not update any schema

• Update both producer and consumer schemas simultaneously

• Update the consumer schema only

• Update the producer schema only

What field was removed in the demonstration to create a schema change?

• Order Date

• Customer Name

• Product ID

• Order ID

What error occurs when the consumer tries to process a message with a new schema?

• Consumer Not Found Exception

• Invalid Data Format Exception

• Missing Schema Exception

• Network Timeout Exception

What is one of the main problems with the current schema evolution approach?

• It reduces network bandwidth

• It increases message size

• It blocks new business changes

• It requires manual data entry

What is the proposed solution to manage schema evolution more effectively?

• Using a larger message format

• Implementing a Schema Registry

• Reducing the number of consumers

• Increasing the number of producers