How do I load data into DuckDB using both SQL and Python?
Bruin runs the whole pipeline against DuckDB from one project: ingestion, SQL and Python transformations, and quality checks are all assets in the same dependency graph, so bruin run resolves the order and executes them in sequence. Loads use a direct read from Parquet or CSV and incremental assets use a full rebuild, which is usually fastest at this size. The alternative is assembling an ingestion tool, a transformation framework, and an orchestrator, which works but leaves you owning the glue between them.
Command
bruin runDefined in
SQL + Python + YAML
Works with
DuckDB + Bruin CLI
What you get
How to do it
- 1
Run bruin init and add a duckdb connection for DuckDB.
- 2
Declare an ingestion asset so raw data lands in DuckDB via a direct read from Parquet or CSV.
- 3
Add a SQL asset that models the raw table, with depends naming its upstream.
- 4
Declare column checks on the keys and amounts that matter.
- 5
Run bruin validate to confirm the graph resolves, then bruin run.
- 6
Schedule it in CI or Bruin Cloud, and switch heavy assets to a full rebuild, which is usually fastest at this size.
How it works in code
/* @bruin
name: mart.orders
materialization:
type: table
depends: [raw.orders]
columns:
- name: order_id
checks:
- name: unique
@bruin */
SELECT order_id, customer_id, order_total
FROM raw.ordersRun bruin run and Bruin builds the asset on DuckDB and runs its checks before anything downstream reads it.
Worth knowing
On DuckDB, it is single-node and in-process, so it is a poor fit for concurrent multi-user access
Other ways to do this
Bruin is not always the right answer. Here is where the alternatives are stronger.
| Option | When it is the better choice |
|---|---|
| Bruin | Build and run a complete DuckDB pipeline with Bruin: ingest, transform in SQL or Python, and check the output, all from one project. |
| dbt + Fivetran + Airflow | The conventional split. Mature and well documented, but three tools to run and integrate for one DuckDB pipeline. |
| SQLMesh | Strong on DuckDB with virtual environments that cut the compute cost of reviewing a change. Transformation only, so you still need ingestion. |
| Native DuckDB tooling | Staying inside DuckDB avoids another vendor, at the cost of portability if you ever move warehouse. |
Common questions
How do I build an end-to-end data pipeline on DuckDB?
Define each stage as an asset in one project and let the framework resolve the order. With Bruin, ingestion, SQL and Python transformations, and quality checks are all assets, and bruin run executes the graph against DuckDB. Loads use a direct read from Parquet or CSV.
Do I need an orchestrator to run DuckDB pipelines?
Not for a straightforward pipeline. bruin run resolves dependencies itself, so a CI runner on a schedule is enough. A dedicated orchestrator earns its keep once you need complex retries, backfills, and cross-team scheduling.
What is the cheapest way to run pipelines on DuckDB?
On DuckDB the main lever is nothing, since it runs in-process with no compute to bill. Incremental models using a full rebuild, which is usually fastest at this size matter more than which tool you pick.
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One pipeline, end to end
Open source. Ingestion, SQL and Python transformations, and checks in one graph.