How can you avoid duplicate records in an Amazon Redshift table when an AWS Glue job is rerun?

The best way to avoid duplicate records in an Amazon Redshift table when an AWS Glue job is rerun is to modify the AWS Glue job to copy rows into a staging table and replace existing rows using SQL commands. This approach effectively handles the issue of duplicates by following a systematic process:

1. Staging Table Usage: When you copy data into a staging table first, you can perform deduplication and transformation operations on that data without affecting the existing records in the main table. A staging table acts as a temporary holding area where you can clean and validate the new data before merging it into your production table.

2. Replacing Existing Rows: By executing SQL commands after the data has been moved to the staging table, you're able to identify which records in the main table need to be updated or replaced. This can involve using commands like DELETE for old rows and INSERT or UPDATE for new or changed rows, ensuring that only valid, non-duplicate records are retained in the final table.

This strategy provides a robust solution as it leverages Redshift's SQL capabilities to manage data integrity, making it an effective choice for data processing workflows.

In contrast, employing a versioning system can introduce complexity and overhead in