Auto-scaling triggers: (1) CPU utilization—e.g., >70% scale up. (2) Memory—high usage. (3) Queue depth—Kafka consumer lag, SQS length. (4) Request rate—requests per second. (5) Custom—data pipeline backlog, partition count. (6) Time-based—scale up before peak. Best practice: Set cooldown periods; use predictive scaling; define min/max bounds; alarm on scaling failures. For data pipelines: scale on backlog or latency SLOs....
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