Teradata to DuckDB Integration

How Datrise loads Teradata into DuckDB

Datrise syncs Teradata's tables, rows, schemas, indexes, primary indexes, and query history 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

Teradata: Teradata analytical database for enterprise workloads.

DuckDB: In-process analytics database for fast local OLAP.

How Teradata entities map to DuckDB

Teradata entity	DuckDB object	Notes
tables	`teradata_d_tables`	id PK · custom fields → JSON or STRUCT columns
rows	`teradata_d_rows`	id PK · linked to teradata_d_tables
schemas	`teradata_d_schemas`	id PK · linked to teradata_d_tables
indexes	`teradata_d_indexes`	id PK · linked to teradata_d_tables

FAQ

How does Datrise handle Teradata'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 Teradata 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

Early access

Connect Teradata to DuckDB the easy way

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