Kafka for Developers - Data Contracts Using Schema Registry - Data Evolution and Schema Evolution

The lecture covers the data life cycle and schema evolution, emphasizing the importance of adapting to changing business requirements. It explains how schemas evolve with data changes and the role of Schema Registry in managing these changes. Various compatibility types are discussed, including backward, forward, and full transitive, highlighting their impact on producers and consumers. The lecture aims to provide practical knowledge on applying these compatibility types to ensure seamless schema evolution without affecting applications.

What is the initial step in the data life cycle?

• Data analysis

• Data creation

• Data deletion

• Data storage

What happens to the schema when data evolves?

• It remains unchanged

• It evolves along with the data

• It is archived

• It is deleted

What is the role of a subject in schema evolution?

• To define the scope of schema evolution

• To analyze data

• To delete obsolete data

• To store data

What issue can arise if the producer is updated before the consumer?

• Schema mismatch

• Data loss

• Increased latency

• Data duplication

Which compatibility type allows you to delete fields and add optional fields?

• Backward

• Forward

• Full

• None

Which application needs to be upgraded first when using forward compatibility?

• Consumer

• Producer

• Neither

• Both simultaneously

What does the 'none' compatibility type imply?

• Only backward changes are allowed

• No changes are allowed

• Only forward changes are allowed

• All changes are accepted without compatibility checks