AWS Certified Data Analytics Specialty 2021 - Hands-On! - Kinesis - Handling Duplicate Records

The video tutorial explains how duplicates can occur in Kinesis Data Streams on both the producer and consumer sides. On the producer side, network timeouts can lead to duplicate records being created. The solution is to embed a unique record ID to deduplicate data. On the consumer side, retries can cause the same data to be read twice, especially during worker restarts or shard changes. Making consumer applications idempotent is recommended to handle duplicates. The video also provides exam tips related to these scenarios.

What can cause duplicate records on the producer side in Kinesis data streams?

• Producer application errors

• Network timeouts

• Incorrect data formatting

• High data volume

How can producer-side duplicates be mitigated in Kinesis data streams?

• By increasing network bandwidth

• By embedding a unique record ID

• By reducing data size

• By using a different API

Which of the following is NOT a cause of consumer-side duplicates?

• Shard merging

• Worker termination

• Data encryption

• Application deployment

What is a recommended approach to handle consumer-side duplicates?

• Increase the number of shards

• Make the consumer application idempotent

• Use a different data stream

• Reduce the number of workers

How can a database help in managing duplicates in Kinesis data streams?

• By encrypting the data

• By rejecting duplicate unique record IDs

• By allowing multiple entries of the same record ID

• By storing data in different tables