Requirements (clarify): ingest ~100k rows/day CSVs from multiple vendors, validate and transform to a canonical schema, allow optional new columns (schema drift), idempotent loads, easy failure recovery, monitoring/alerting, use Snowflake.High-level flow:1. Ingest files to an object store (S3/GCS) or upload to Snowflake internal stage.2. COPY INTO a raw staging table (raw_csv_stage) storing each file as VARIANT/JSON or all columns as STRING plus metadata (file_name, load_id, file_hash, received_at).3. Validate & transform: run validation SQL / Great Expectations checks against raw data; normalize to canonical schema using SQL/Stored Proc; capture unknown columns into a JSON column to support schema drift.4. Merge into production analytic table using MERGE with business key + checksum for idempotency.5. Monitoring: load audit table, TASKs, alerts via cloud functions -> Slack/email.Staging + COPY pattern:- Create FILE FORMAT matching CSV options.- COPY INTO raw_table from @stage FILE_FORMAT=(...) ON_ERROR='CONTINUE' to capture rejects in load_errors table.Example COPY + MERGE snippets:sql
-- load raw CSV into staging table (all cols as VARIANT or flattened)
COPY INTO raw_stage(file_variant, file_name, file_hash, load_id)
FROM @vendor_stage/prefix/
FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1 FIELD_OPTIONALLY_ENCLOSED_BY='"');
-- merge into canonical table using business key and checksum for idempotency
MERGE INTO analytics.canonical t
USING (
SELECT
data:vendor_id::STRING as vendor_id,
data:order_id::STRING as order_id,
MD5(TO_VARCHAR(data)) as row_hash,
...
FROM transformed_stage
) s
ON t.vendor_id = s.vendor_id AND t.order_id = s.order_id
WHEN MATCHED AND t.row_hash <> s.row_hash THEN
UPDATE SET ..., row_hash = s.row_hash, updated_at = CURRENT_TIMESTAMP()
WHEN NOT MATCHED THEN
INSERT (..., row_hash, created_at) VALUES (..., s.row_hash, CURRENT_TIMESTAMP());
