When an organization experiences fluctuating data loads in their cloud-based ETL processes, what approaches should be taken to maintain performance and cost-effectiveness?
- Auto-scaling, Data Partitioning, Data Caching, Data Pre-loading
- Data Backup, Data Archiving, Data Purging, Data Snapshotting
- Data Deduplication, Data Compression, Data Tiering, Data Virtualization
- Data Load Balancing, Data Throttling, Data Fragmentation, Data Replication
To address fluctuating data loads in cloud-based ETL processes, approaches like auto-scaling, data partitioning, data caching, and pre-loading should be considered. Auto-scaling allows dynamic resource allocation based on demand, ensuring both performance and cost-effectiveness.
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