CSV File to DuckDB Integration
AI-first ETL from CSV File into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads CSV File into DuckDB
Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.
Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.
Ideal for local and notebook analytics without standing up a server.
Endpoints
CSV File: Delimited flat files from exports and drops.
DuckDB: In-process analytics database for fast local OLAP.
How CSV File entities map to DuckDB
| CSV File entity | DuckDB object | Notes |
|---|---|---|
| rows | csv_file_rows | id PK · custom fields → JSON or STRUCT columns |
| columns | csv_file_columns | id PK · linked to csv_file_rows |
| headers | csv_file_headers | id PK · linked to csv_file_rows |
| delimiters | csv_file_delimiters | id PK · linked to csv_file_rows |
FAQ
How does Datrise handle CSV File's custom fields in DuckDB?
Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.
How does the CSV File to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
Related pipelines
More destinations for CSV File
Early access
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