DATA OPS
Validate S3 CSV drops and load clean rows into Snowflake
When a CSV lands in an S3 bucket, validate its schema and row quality, load only conforming rows into Snowflake, and quarantine the rest. Posts a load summary to Slack.
How it runs
The automated pipeline, trigger to output.
- TriggerNew CSV uploaded to S3 inbound prefixAWS S3
- ActionRead file and validate schema + row qualityAWS S3
- LogicSplit rows into passing vs failing sets
- ActionCOPY passing rows into Snowflake stagingSnowflake
- ActionWrite failing rows to S3 quarantine prefixAWS S3
- OutputPost load summary to SlackSlack
What it does
Watches an S3 prefix for new CSV uploads, runs each file through schema and data-quality checks, then loads only the rows that pass into a Snowflake staging table. Rows that fail are written to a quarantine prefix so nothing silently disappears, and a load summary lands in Slack.
When to use it
Use it when upstream partners or internal teams drop CSV exports into a bucket on no fixed schedule and you need a hands-off pipeline that never loads malformed data into the warehouse. Ideal for keeping a Snowflake landing zone trustworthy without a human reviewing every file.
How it works
- 1An S3 object-created event for the inbound prefix triggers the run.
- 2The pipeline reads the file and validates headers, column count, types, and required-field presence against the expected contract.
- 3A logic step splits rows into a passing set and a failing set based on the validation results.
- 4Passing rows are bulk-loaded into the Snowflake staging table via COPY.
- 5Failing rows plus a reason column are written back to an S3 quarantine prefix.
- 6A summary (file name, rows loaded, rows quarantined) is posted to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect AWS S3Buckets, objects, signed URLs.
- 2Connect SnowflakeWarehouses, queries, shares.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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