A data engineer shares how they built a self-healing ingestion pipeline in Snowflake to handle structurally broken pipe-delimited files from a legacy system. Standard tools like Snowpipe and Fivetran failed because embedded newlines split logical records across multiple physical lines. The solution uses a Python Stored Procedure via Snowpark to buffer and stitch broken rows, then hands off a repaired file to Snowflake's bulk COPY engine — reducing processing time from hours to under two minutes. The architecture includes a Stage Stream for incremental processing, an audit log tracking raw vs. healed record counts, real-time email alerting via SYSTEM$SEND_EMAIL, and a Task DAG that orchestrates a weekly stage refresh followed by conditional processing only when new data exists.
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