# Destinations Where the data lands. The same flow can be pointed at any of these. ## A SQL table Created for you, or matched to a table you already have, in any relational connection. You see the exact `CREATE TABLE` and `INSERT` statements before anything runs. Writing modes are **append** (add the rows) and **replace** (empty the table first). ## A MongoDB collection Nesting is kept rather than flattened. This is the inverse of what the SQL destination does with a nested record, and it is usually the right answer when the source was already document-shaped. ## A file CSV, JSON, JSONL, or a Markdown report you can hand to somebody. Useful when the point of the flow is to produce an artifact rather than to load a database. ## A fine-tuning set Validated JSONL, in the shape a fine-tuning job expects. Together with [generation as a source](https://docs.snoutdata.com/flows/sources), this makes "produce a dataset and train something smaller on it" one path in one app, rather than two tools and a script in between. ## A vector index Embedded and upserted into Pinecone, so you can retrieve over what you just brought in. ## Production destinations are guarded If the destination connection is flagged as production, the same guardrails that protect a query you type by hand apply here: destructive operations are held until you confirm them, and the run is recorded.