Templates

Paradime provides a set of pre-configured templates and webhooks to help you quickly set up, optimize, and automate your Bolt Schedules. These templates cover various aspects of dbt™ model management, source data monitoring, CI/CD workflows, and integrations with external tools for seamless pipeline monitoring and alerting.

Available Templates

🏭 Run and Test all your dbt™ Models

A foundational schedule that executes dbt™ runs and tests across your entire project, ensuring comprehensive model execution and validation.

🔍 Snapshot Source Data Freshness

A schedule that monitors source data freshness using dbt™ source freshness checks, ensuring your critical data meets defined SLAs.

Build and Test Models with New Data Sources

This template monitors source freshness to selectively rebuild only the necessary dbt™ models, reducing unnecessary compute.

🔍 Test Code Changes on Pull Requests

Automatically validate dbt™ model changes in a pull request by building and testing only the affected models in a temporary schema.

🚀 Deploy Code Changes On Merge

Automatically execute dbt™ model builds and deploys them to production when a new pull request is merged into the main branch.

🔔 Create Jira Tickets

Automatically create Jira tickets when pipeline runs fail, enabling immediate visibility and tracking of data pipeline issues

🔄 Trigger Census Syncs

Trigger Census syncs automatically after successful pipeline runs, ensuring your operational tools always have the latest data.

📊 Trigger Hex Projectss

Keep your Hex dashboards current by automatically refreshing projects when pipeline runs complete successfully.

🎯 Create Linear Issues

Create Linear issues automatically when pipelines fail, streamlining incident management and response.

🚨 Create New Relic Incidents

Generate New Relic incidents automatically for failed pipeline runs, enabling immediate incident response.

📋 Create Azure DevOps Work Items

Automatically create Azure DevOps work items when pipelines fail, keeping your development team informed of data pipeline issues.