How do I set up a modern data warehouse on Synapse quickly?
Bruin runs the whole pipeline against Synapse 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 INTO from Azure storage and incremental assets use a staging table plus MERGE. 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
Synapse + Bruin CLI
What you get
How to do it
- 1
Run bruin init and add a synapse connection for Synapse.
- 2
Declare an ingestion asset so raw data lands in Synapse via COPY INTO from Azure storage.
- 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 staging table plus MERGE.
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 Synapse and runs its checks before anything downstream reads it.
Worth knowing
On Synapse, a dedicated SQL pool bills while paused-but-not-actually-paused; confirm the pool state, not the job state
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 Synapse 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 Synapse pipeline. |
| SQLMesh | Strong on Synapse with virtual environments that cut the compute cost of reviewing a change. Transformation only, so you still need ingestion. |
| Native Synapse tooling | Staying inside Synapse 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 Synapse?
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 Synapse. Loads use COPY INTO from Azure storage.
Do I need an orchestrator to run Synapse 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 Synapse?
On Synapse the main lever is pausing the dedicated SQL pool when it is idle. Incremental models using a staging table plus MERGE matter more than which tool you pick.
Related use cases
Set up analytics on Snowflake from scratch
How do I set up a modern data warehouse on Snowflake quickly?
Getting startedSet up analytics on BigQuery from scratch
How do I set up a modern data warehouse on BigQuery quickly?
Getting startedSet up analytics on Databricks from scratch
How do I set up a modern data warehouse on Databricks quickly?
One pipeline, end to end
Open source. Ingestion, SQL and Python transformations, and checks in one graph.