How can the issue of data duplication in Synapse serverless queries for partitioned data in Delta Lake be resolved?

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To resolve the issue of data duplication in Synapse serverless queries for partitioned data in Delta Lake, the following steps can be taken:

1. Use the Delta Lake MERGE operation: The MERGE operation can be used to update data in Delta Lake tables using a set of predetermined rules. This can help to avoid data duplication when new data is added to the table.

2. Use partitioning and clustering: Partitioning and clustering data in Delta Lake tables can help to improve query performance and reduce data duplication. By partitioning data based on different criteria, such as date or region, queries can be optimized to only access the relevant data.

3. Enable delta cache: Delta cache can be used to accelerate queries and reduce data duplication by caching frequently accessed data in memory. This can help to improve query performance and reduce the amount of data that needs to be scanned.

4. Use unique identifiers: Using unique identifiers in Delta Lake tables can help to avoid data duplication. By ensuring that each record has a unique identifier, queries can be optimized to identify and remove duplicate records.