During a high-volume data load, an ETL process is experiencing slow performance. What strategies could be employed to handle this scenario effectively?
- Adding more memory to the server
- Implementing parallel processing
- Increasing batch sizes
- Reducing the number of transformations
To handle slow performance during high-volume data loads, implementing parallel processing is an effective strategy. This involves dividing the workload into smaller tasks that can be processed concurrently, maximizing resource utilization and reducing overall processing time.
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