How to Orchestrate dbt Core Jobs

Modern data teams rely on dbt Core to transform raw data into actionable insights. But while dbt Core excels at SQL-based transformations, it doesn’t handle orchestration—the process of scheduling, monitoring, and managing data workflows.

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This blog dives into the three-step process for orchestrating dbt Core jobs

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What Is dbt core?

dbt Core (Data Build Tool) is an open-source tool designed to help data teams transform raw data into analytics-ready models. Its SQL-first approach and integration with modern data warehouses make it indispensable for building reliable data workflows.

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But here’s the catch: dbt Core doesn’t manage:

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• Scheduling: When your jobs should run.
• Dependencies: Ensuring data arrives before transformations begin.
• Monitoring: Detecting failures and anomalies in real-time.
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Without orchestration, you risk pipeline inefficiencies, delays, and errors. This is where orchestration tools, and platforms like 5X, come into play.

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3 ways to orchestrate dbt Core jobs

When managing dbt (data build tool) jobs, orchestration plays a crucial role in automating, scheduling, and monitoring complex data transformations. If you're looking to streamline your dbt workflows, understanding the best orchestration approaches is essential. In this post, we'll explore three effective ways to orchestrate dbt Core jobs:

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1. Local System Orchestration
2. GitHub Actions for dbt Orchestration
3. Using 5X for dbt Core Orchestration
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Each method comes with its own set of advantages and use cases, allowing teams to choose the best approach based on their needs and infrastructure.

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1. Orchestrating dbt jobs with local systems

Overview

One of the most straightforward methods of orchestrating dbt Core jobs is by setting up local systems or servers to host and schedule jobs. This approach allows complete control over the orchestration process, but it also requires managing various aspects of the job execution pipeline.

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How it works:

• Setup: Install dbt Core on a local machine or a dedicated server.
• Scheduling: Use cron jobs or task schedulers (e.g., Apache Airflow or Prefect) to trigger dbt runs on a schedule.
• Execution: The local server executes dbt run commands, typically within a virtual environment to ensure compatibility.

Pros and cons:

• Pros:
  • Complete control over the orchestration process.
  • Customizability with existing tools.
• Cons:
  • Requires manual setup and maintenance of infrastructure.
  • Can be resource-intensive, particularly for large datasets or teams.

2. Orchestrating dbt Jobs with GitHub actions

Overview

GitHub Actions allows teams to automate workflows directly from a GitHub repository, including triggering dbt Core jobs. This approach is especially useful when your dbt Core project is hosted on GitHub, enabling seamless integration between code changes and job execution.
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How It works:

• Setup: Create a GitHub repository for your dbt project.
• GitHub Actions Workflow: Define a YAML file to specify actions like dbt run, dbt test, and other dbt commands.
• Trigger: GitHub Actions can trigger dbt jobs on push events, pull requests, or on-demand using the workflow_dispatch trigger.
• Execution: When the specified event occurs, GitHub Actions runs the job and provides logs on the status of the execution.

Pros and cons:

• Pros:
  • Fully integrated with GitHub, making it a great choice for GitHub-hosted projects.
  • Simple to set up with predefined actions and workflows.
• Cons:
  • Limited to GitHub infrastructure.
  • Might be less flexible for complex, multi-step orchestration needs.

3. Orchestrating dbt jobs using 5X

Overview

The 5X platform offers an advanced, managed solution for orchestrating dbt Core jobs. By using 5X’s built-in orchestration capabilities, teams can simplify data transformations while maintaining seamless integration across their data stack. Here’s how you can orchestrate dbt Core jobs using 5X.
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How It works:

1. Setup:
   • Integrate your dbt Core repository into the 5X platform.
   • No external orchestration tools like GitHub Actions or Apache Airflow are required.
2. Job Scheduling:
   • 5X provides a native job orchestration feature where dbt jobs can be scheduled and monitored directly from the 5X interface.
   • The platform allows you to define schedules, trigger jobs based on specific events, and automate data pipelines.
3. Execution:
   • Jobs are executed within the 5X environment, ensuring that all model dependencies are respected.
   • The platform provides robust error logging, making it easier to monitor and debug issues.

Key features of 5X Job Orchestration:

• Ease of Use: Unlike other orchestration tools, 5X provides a simplified user interface to manage and monitor dbt jobs without requiring complex configurations.
• Comprehensive Orchestration: 5X combines dbt job orchestration with other data operations like ingestion, transformation, and visualization, ensuring a unified data workflow.
• Scheduling: Teams can set up regular job schedules (daily, weekly, or custom) and trigger dbt runs based on various business needs.
• Ad-hoc Runs: Execute dbt jobs on-demand for immediate data transformations or testing.
• Integrated Monitoring: 5X offers real-time tracking of job statuses, performance metrics, and logs to ensure smooth operations across the board.

Why 5X Orchestration stands out:

• Streamlined Operations: 5X integrates all steps of the data pipeline, reducing the complexity of using external tools.
• Scalability: The platform is designed to handle growing data needs without the overhead of maintaining separate orchestration tools.
• Fully Managed: 5X manages the infrastructure, allowing teams to focus on creating data models rather than worrying about the execution environment.
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5X orchestration not only automates dbt Core jobs but also ensures that job dependencies are respected, providing a more seamless orchestration experience compared to local solutions or GitHub Actions.

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