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

The video tutorial explains the occurrence of duplicate records in Kinesis Data Streams due to network timeouts on the producer side and retries on the consumer side. It highlights the importance of embedding unique record IDs to deduplicate data and making consumer applications idempotent to handle duplicates. The tutorial also outlines four scenarios where consumer retries can occur and suggests handling duplicates at the final destination, such as a database. Key points are emphasized for exam preparation.

What can cause duplicate records on the producer side in Kinesis Data Streams?

• Producer application crashes

• Network timeouts leading to retries

• Data corruption during transmission

• Incorrect data formatting

How can producer-side duplicates be mitigated in Kinesis Data Streams?

• By using a unique record ID

• By increasing network bandwidth

• By reducing the number of producers

• By compressing the data

Which of the following is NOT a scenario that can lead to consumer-side duplicates?

• Worker instances being added or removed

• Shards being merged or split

• Unexpected worker termination

• Data being sent to multiple streams

What does it mean for a consumer application to be idempotent?

• It can operate offline

• It requires less memory

• It can handle duplicate data without side effects

• It processes data faster

Where is it recommended to handle duplicates if possible?

• At the producer level

• In the network layer

• In the data stream itself

• At the final destination, such as a database