Kinesis data loss.

Data loss in Amazon Kinesis can occur for various reasons, and it's crucial to address these issues promptly to ensure data integrity. Here are some common causes and steps to address Kinesis data loss:

1. Provisioned Throughput Exceeded:
   • Cause: If your application exceeds the provisioned throughput capacity of the stream, it may result in data loss.
   • Solution:
     • Monitor the stream's shard metrics to ensure it has sufficient capacity. Consider increasing the number of shards if needed.
2. Consumer Lag:
   • Cause: Consumers may fall behind in processing records, leading to potential data loss if records expire before they can be processed.
   • Solution:
     • Monitor consumer lag and consider scaling your consumer applications or optimizing processing logic to keep up with the ingestion rate.
3. Retries and Error Handling:
   • Cause: Incomplete processing of records due to application or network errors can lead to data loss.
   • Solution:
     • Implement proper retries and error handling in your consumer applications to ensure that failed records are reprocessed.
4. Record TTL (Time-To-Live) Expiry:
   • Cause: If records are not processed within their TTL, they will be automatically removed from the stream, resulting in data loss.
   • Solution:
     • Ensure that your consumers can process records within the TTL configured for the stream.
5. Using the Wrong Shard Iterator Type:
   • Cause: Choosing the wrong shard iterator type (e.g., TRIM_HORIZON) may result in missing records that were not included in the iterator.
   • Solution:
     • Select the appropriate shard iterator type based on your application's requirements.
6. Application Bugs or Logic Errors:
   • Cause: Bugs or logic errors in your consumer applications can lead to improper handling of records and potential data loss.
   • Solution:
     • Thoroughly test your consumer applications and review your processing logic to ensure it is handling records correctly.
7. Faulty Producer Applications:
   • Cause: Issues with your producer applications can lead to incomplete or missing data being sent to the stream.
   • Solution:
     • Monitor and review the logs of your producer applications to identify and address any issues with data ingestion.
8. Scaling Issues:
   • Cause: Insufficient or improper scaling of your Kinesis applications can lead to data loss due to overload or underutilization.
   • Solution:
     • Continuously monitor your application's performance and scale it appropriately based on the incoming data volume.
9. AWS Service Issues:
   • Cause: There may be temporary issues with the Kinesis service itself that lead to data loss.
   • Solution:
     • Monitor the AWS Service Health Dashboard for any reported issues with the Kinesis service.
10. Monitoring and Alerting:
    • Implement thorough monitoring and alerting to detect and respond to any anomalies or issues related to data ingestion and processing in your Kinesis stream.

Remember to implement best practices for data processing, error handling, and monitoring to minimize the risk of data loss in your Kinesis applications. If you encounter persistent issues, consider reaching out to AWS Support for further assistance.