How do I build an ELT pipeline from an API into Postgres?
Bruin runs the whole pipeline against Postgres 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 COPY from a staged file and incremental assets use INSERT ON CONFLICT DO UPDATE. 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
Postgres + Bruin CLI
What you get
How to do it
- 1
Run bruin init and add a postgres connection for Postgres.
- 2
Declare an ingestion asset so raw data lands in Postgres via COPY from a staged file.
- 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 INSERT ON CONFLICT DO UPDATE.
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 Postgres and runs its checks before anything downstream reads it.
Worth knowing
On Postgres, analytics queries on the same instance as your application will eventually degrade the application
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 Postgres 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 Postgres pipeline. |
| SQLMesh | Strong on Postgres with virtual environments that cut the compute cost of reviewing a change. Transformation only, so you still need ingestion. |
| Native Postgres tooling | Staying inside Postgres 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 Postgres?
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 Postgres. Loads use COPY from a staged file.
Do I need an orchestrator to run Postgres 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 Postgres?
On Postgres the main lever is indexes and query shape, since compute is your instance rather than a metered pool. Incremental models using INSERT ON CONFLICT DO UPDATE 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.