Compare and implement Snowflake Snowpipe Streaming versus classic Snowpipe for low-latency ingestion

Steps

1. Classic Snowpipe: stage files to an internal or external stage, then call the REST API ingest endpoint or use auto-ingest via a cloud storage event notification to trigger a COPY INTO; suitable for micro-batch file-based workflows with latency in the seconds-to-minutes range.
2. Snowpipe Streaming: use the Snowflake Ingest SDK (Java client) to open a channel per table partition and call insertRows() with row maps; rows appear in the target table in near-real-time (sub-second to low seconds) without staging files.
3. For Snowpipe Streaming, configure the channel with an offset token that your application manages; on restart, resume from the last committed offset token to avoid data loss or duplication.
4. Classic Snowpipe charges per file load based on compute credits; Snowpipe Streaming charges based on rows ingested and compute; compare cost at your expected row rate before choosing.
5. Use Snowpipe Streaming with Kafka Connect (Snowflake Kafka connector) by setting ingestion.method=SNOWPIPE_STREAMING in the connector config to route Kafka records directly into Snowflake without staging.
6. For both modes, monitor credit consumption and error queues (REST status endpoint for classic Snowpipe; channel status via SDK for Streaming) to detect stalled or failed ingestion.