How to Handle Duplicate Data Ingested into Time Series Serverless Collections

Handling duplicate data ingested into time series serverless collections can be done by implementing appropriate strategies at different stages of the data pipeline. Here are some approaches you can consider:

1. Change Data Capture: Most of the time we implement change data capture mechanisms to capture and process only the changed or updated data. This will ensure that duplicate data is not ingested into the time series serverless collections. You can use Kafka CDC Connectors for this like Confluent CDC Connectors.

2. Deduplication at Ingestion: Easiest way to implement is to do the check at the source itself. This can be achieved by maintaining a record of previously ingested data and comparing incoming data with this record before storing it. If a duplicate is detected, it can be skipped or replaced with the latest version.

3. Data Cleaning and Transformation: Apply data cleaning and transformation techniques to preprocess the incoming data. This may involve removing duplicates within the data itself or performing aggregations to merge duplicate records.

4. Event Time Deduplication: If the time series data includes an event time or timestamp, deduplication can be performed based on the event time. Duplicate events that occur within a specific time window can be filtered or consolidated.

5. Data Validation: Perform data validation checks before ingestion to identify and reject duplicate data. This can be done by comparing incoming data with existing data based on a unique identifier or timestamp. If a duplicate is detected, it can be rejected or handled accordingly.