How do I load data into Microsoft Fabric using both SQL and Python?
Bruin runs the whole pipeline against Microsoft Fabric 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 write into OneLake and incremental assets use MERGE against the Lakehouse table. 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
Microsoft Fabric + Bruin CLI
What you get
How to do it
- 1
Run bruin init and add a fabric connection for Microsoft Fabric.
- 2
Declare an ingestion asset so raw data lands in Microsoft Fabric via a write into OneLake.
- 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 MERGE against the Lakehouse table.
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 Microsoft Fabric and runs its checks before anything downstream reads it.
Worth knowing
On Microsoft Fabric, Direct Lake mode has specific table requirements; a table that falls back to DirectQuery silently gets slower
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 Microsoft Fabric 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 Microsoft Fabric pipeline. |
| SQLMesh | Strong on Microsoft Fabric with virtual environments that cut the compute cost of reviewing a change. Transformation only, so you still need ingestion. |
| Native Microsoft Fabric tooling | Staying inside Microsoft Fabric 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 Microsoft Fabric?
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 Microsoft Fabric. Loads use a write into OneLake.
Do I need an orchestrator to run Microsoft Fabric 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 Microsoft Fabric?
On Microsoft Fabric the main lever is capacity units, so consolidating workloads onto one capacity beats spreading them. Incremental models using MERGE against the Lakehouse table matter more than which tool you pick.
Related use cases
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One pipeline, end to end
Open source. Ingestion, SQL and Python transformations, and checks in one graph.