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erdo datasets write appends or updates rows in an existing writable dataset. The examples below use an ordinary file dataset. Pass --org and --project when you need to select the workspace and project explicitly. A populated canonical lead dataset refuses generic row writes. Use its capture pipeline or the supported lead identity operations.

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Append rows

Pass a JSON array inline or load it from a file with @:
Each array element must be a non-null JSON object. The CLI sends its JSON values unchanged; storage behavior depends on the dataset. File datasets store cell values as text, with JSON null becoming an empty cell. Objects and arrays do not round-trip as JSON: for example, an object may become map[plan:pro]. If a file dataset column needs JSON that can be parsed later, supply an encoded JSON string, such as "meta": "{\"plan\":\"pro\"}". An empty array is a successful zero-row no-op.

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Replace matching rows

Add a key column to upsert by that value:
Omit both --key-column and --write-mode to append. With a key, new keys insert and matching keys update. File datasets replace the matching row, clearing fields omitted from the incoming row. PostgreSQL tables update the non-key columns present in the batch. Columns absent from every row stay unchanged; omitting a column supplied by another row writes NULL for that cell. --write-mode replace selects this default explicitly and requires a non-blank --key-column, just like merge mode. A supplied key must name at least one non-blank column, even when the write mode is omitted. A comma-separated value supplies a composite key:
Replacement changes matching rows and preserves unrelated rows in the dataset. It does not replace the whole dataset. File datasets and writable PostgreSQL tables support this keyed replacement. ClickHouse-backed writes append even when --key-column is supplied with the default or explicit --write-mode replace; they do not update the matching row.

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Merge matching rows

Merge mode requires a non-blank key. It fills blank fields and preserves existing non-blank values:
Use --overwrite-columns when selected non-blank incoming values should also replace existing values:
--overwrite-columns is valid only with merge mode and cannot contain empty column names. The server also refuses attempts to overwrite a key column. Storage support varies: file datasets and writable PostgreSQL tables support merge; ClickHouse-backed datasets refuse merge mode.

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Response and errors

The command prints the server’s JSON count response:
Writes require edit access to the dataset. The dataset’s declared columns and required fields still apply; the server reports contract and storage errors. For a file merge, required fields are checked on the resulting row, so a partial update can retain required values already stored. The CLI does not automatically retry a row write after a network failure: the request may have reached the server, so inspect the affected dataset before retrying manually.

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Repair dataset user access

An organization operator can inspect and repair explicit dataset user grants without bypassing RBAC:
These endpoints require an organization member session or full organization API key, and an admin or owner grant on the exact dataset. The list and set responses contain only user_id and level; they do not expose email addresses. Removal verifies that the direct grant is absent on readback; it does not assert that all effective access was revoked. A user may still have access through an organization or another inherited grant after a direct grant is removed.