Schedules as Code
What are Paradime YAML-Based Schedules?
Paradime YAML schedules are configuration-as-code definitions, allowing you to define, version, and manage your data pipeline schedules directly within your dbt project repository. These schedules are configured in a single file named paradime_schedules.yml
located in the root directory of your dbt project (alongside dbt_project.yml
).
Prerequisites
In order to run yaml-based schedules, connect your data warehouse to the Scheduler Environment.
File Location and Structure
Why Use YAML-Based Schedules?
Version Control
Schedule configurations are tracked alongside your dbt models
Review schedule modifications through Pull Requests
Enforce team review processes for schedule changes
Infrastructure as Code
Schedules are treated as code, not just UI configurations
Easy replication across environments
Enables automated deployment pipelines
Team Collaboration
Simplified schedule review process
Standard formatting and validation
Documentation lives with the code
How YAML-bases schedules are deployed?
Schedules are always read from the paradime_schedules.yml
file on your default branch (usually main
or master
).
Automatic Refresh: Paradime checks for changes every 10 minutes.
Manual Refresh: For immediate updates, navigate to the Bolt interface and click Parse Schedules.
💡 Note: To update your schedules, make sure to merge your changes to the default branch first.
Configuration Reference
This section describes the YAML configuration format for scheduling and managing automated tasks. The configuration supports various execution modes including scheduled runs, trigger-based execution, deferred scheduling, CI/CD integration and API.
💡 Looking for complete examples? Jump to the Example Configurations section below.
Base Configuration
Every scheduler configuration must include these basic fields:
Execution Modes
1. Schedule-Triggered Execution
Basic scheduled execution using cron expression:
2. Run Completion Trigger
Triggers execution based on completion of another job:
3. Merge Trigger
Triggers execution on merge events:
Requires GitHub integration.
4. Deferred Scheduling
Allows schedules to used dbt defer to artifacts comparison:
5. Turbo CI Configuration
Configuration for CI pipelines:
6. API Configuration
Basic configuration when triggering Bolt via API:
For more details on Paradime APIs check our Developers guide.
7. Suspended State
Configuration for suspended jobs:
Notifications Configuration
Notifications can be configured for various events through multiple channels:
For Slack and MS Teams notifications, check our integrations guide:
Example: Complete Configuration
Best Practices
Schedule Format
Use standard cron expressions for scheduling
✅ Standard cron to define days 0-6
10 * * * 0-6
: At minute 10 on every day-of-week from Sunday through Saturday.
❌ Non-standard cron to define days 1-7
10 * * * 1-7
: At minute 10 on every day-of-week from Monday through Sunday
Use
'OFF'
to disable scheduled executionUse crontab.guru to validate your cron expressions
SLA Configuration
sla_minutes
should be set based on job complexityConsider dependencies when setting SLA
Recommended minimum: 30 minutes
Notification Configuration
Configure at least one notification channel
Include critical events (failed, SLA) in notifications
Use team channels for collaborative workflows
Make sure to set the Slack / MS Teams Channel or Email for System notifications. Check our guide here for Notifications Settingss
Paradime schedules terminal commands
Before running any of the following commands, navigate to your dbt™️ project directory where paradime_schedules.yml
is located.
CLI command | Description |
---|---|
Validate File Format - This command checks the | |
Run Schedule Locally - To run all defined schedules based on your local context: (ie. based on your development environment and your current branch). | |
Run Selected Schedule Locally - To run the named schedule based on your local context: (ie. based on your development environment and your current branch). | |
Dry run - To simulate all schedule executions without running dbt™️ models. | |
Dry run - To simulate the named schedule executions without running dbt™️ models. |
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