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Build Reliable Data Pipelines That Your Business Can Trust

Transform brittle ETL jobs into resilient data pipelines. Integrate seamlessly with dbt while ensuring data quality and timely insights delivery.

Why Modern Analytics Teams Choose Prefect

  • Native integration with dbt and data warehouses
  • Automated pipeline recovery and retries
  • Self-service deployment capabilities
flow.py
flow.py
1from prefect import flow, task
2from prefect_dbt.cloud import DbtCloudCredentials, DbtCloudJob
3
4@task
5def load_data():
6    # Your existing data loading code
7    pass
8
9@flow
10def analytics_pipeline():
11    # Load raw data
12    raw_data = load_data()
13
14    # Transform with dbt
15    dbt_job = DbtCloudJob(
16        dbt_cloud_credentials=DbtCloudCredentials.load("dbt-creds"),
17        job_id="daily-transformations"
18    )
19    dbt_job.run()
Testimonial
With Prefect we can define our workflows precisely, using code that's under version control. Features like tasks, task dependencies & retries, and mapping make it easy to write robust data imports and data pipelines.
Lee Mendelowitz
Lee Mendelowitz
Lead Data Engineer, Washington Nationals

Trusted by Enterprise Analytics Teams

Accelerate Time to Production

Deploy and refresh analytics pipelines quickly with self-service capabilities that minimize maintenance overhead.

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Maintain Data Quality

Automate data quality checks and dependency management across data pipelines with custom alerts and comprehensive failure notifications for end-to-end observability.

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Build Trust in Your Data

Monitor analytics pipelines comprehensively with automated recovery, clear audit trails, and SLA tracking.

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Native Integrations

Connect to the whole analytics stack seamlessly across dbt, data warehouses, and BI tools to streamline ETL workflows.

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flow.py
1from prefect import flow
2from prefect_dbt.cli.tasks import from prefect import flow
3from prefect_dbt.cli.commands import trigger_dbt_cli_command, dbt_build_task
4
5
6@flow
7def dbt_build_flow():
8    trigger_dbt_cli_command(
9        command="dbt deps", project_dir="/Users/test/my_dbt_project_dir",
10    )
11    dbt_build_task(
12        project_dir = "/Users/test/my_dbt_project_dir",
13        create_summary_artifact = True,
14        summary_artifact_key = "dbt-build-task-summary",
15        extra_command_args=["--select", "foo_model"]
16    )

Team Enablement

Scale across the whole team securely with collaborative debugging and fine-grained object-level access control (RBAC & SCIM).

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Real Analytics Outcomes

  • Reliability: Eliminate 3AM pages with self-healing workflows
  • Speed: Deploy changes without waiting for infrastructure
  • Visibility: Know exactly what broke and why
  • Efficiency: Reduce time spent on pipeline maintenance
test
test

Hear From Our Users

Alex Welch, Head of Data, dbt Labs

We use Prefect to orchestrate dbt Cloud jobs right alongside other data tools. It brings visibility to our entire pipeline and streamlines our deployments. By combining Prefect and dbt Cloud, you get the best of both worlds without sacrificing functionality, governance, or velocity.

Analytics Engineering Lead

What used to take days of pipeline debugging now takes minutes. Prefect's observability lets us find and fix issues before they impact business decisions.

Emerson Franks, Principal Engineering Lead, Rec Room

Analytics engineering can iterate freely without affecting Prefect related work with our dbt ETL cover code. We've managed to dry up our code for Databticks, dbt, and Fivetran flows.

Ready to Make Your Data Pipelines Bulletproof?

  • ✓ Native dbt integration
  • ✓ Automated recovery
  • ✓ Complete visibility
  • ✓ Self-service deployment

Learn More About Prefect

Cox Automotive Meets Dynamic Demands with Workforce Analytics Solutions Powered by Prefect
Modern Orchestration: Endpoint’s evolution from Airflow to Prefect
Using Prefect to Orchestrate dbt For Full Observability at dbt Labs

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