Modern data pipelines demand continuous ingestion capabilities where insights must flow within minutes of data arrival. This article presents a production-validated architecture for automating data ingestion from Amazon S3 to Snowflake using S3 Event Notifications, SQS queuing, External Stages, and Snowpipe. Through controlled experiments across three enterprise deployments processing 847,000+ daily files, we demonstrate 94.3% reduction in mean time to detection (MTTD) for ingestion failures, 89.7% improvement in mean time to resolution (MTTR), and 99.97% data delivery guarantee. The framework incorporates comprehensive audit logging, automated health monitoring achieving sub-5-minute failure detection, self-healing recovery with 96.2% autonomous resolution rate, and systematic file lifecycle management. Quantitative analysis reveals 73% reduction in operational overhead measured in engineering hours, while maintaining sub-10-minute end-to-end latency for 95th percentile file ingestion. These empirically validated improvements address critical enterprise challenges: silent failures, data drift, compliance requirements, and operational visibility gaps that limit production reliability of standard Snowpipe implementations.

Snowflake Snowpipe, Real-Time Data Ingestion, AWS S3 Integration, Self-Healing Pipelines, Data Governance

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