How to Migrate from DBT Core to DBT Cloud: A Step-by-Step Guide

Data teams are rapidly adopting dbt Cloud to streamline data transformations and improve operational efficiency. According to industry reports, companies using dbt Cloud experience a 40% reduction in deployment time and a 30% boost in developer productivity compared to dbt Core. If you’re considering moving from dbt Core to dbt Cloud, this guide will walk you through the migration process while highlighting how Infometry, a trusted leader in dbt implementation and cloud migration, can help ensure a seamless transition.

Why Migrate from dbt Core to dbt Cloud?

While dbt Core offers robust data transformation capabilities, dbt Cloud provides several advantages:

• Automated Job Scheduling: No need for external orchestrators like Apache Airflow or cron jobs.
• Version Control & Collaboration: Seamless integration with GitHub, GitLab, and Bitbucket.
• Advanced Logging & Alerting: Real-time monitoring and alerts for pipeline failures.
• Role-Based Access Control (RBAC): Secure and scalable team management.
• Integrated Development Environment (IDE): A web-based IDE simplifies development and deployment.
• Improved Execution Performance: Faster execution times compared to dbt Core, optimizing resource usage.
• Automated Version Management: No need to manually maintain dbt versions; always up-to-date with the latest releases.
• Comprehensive Log History & Lineage View: Offers detailed log history with an intuitive lineage view for better debugging and pipeline tracking.

Step-by-Step Migration from dbt Core to dbt Cloud

Step 1: Assess Your Current dbt Core Setup

Before migrating, take an inventory of:

• Your dbt project structure (models, macros, snapshots, seeds, and tests)
• Connection details to your data warehouse (Snowflake, BigQuery, Redshift, etc.)
• Any custom scripts or orchestration tools used

Infometry’s dbt experts perform an in-depth architecture assessment to identify dependencies and migration complexities.

Step 2: Set Up dbt Cloud

1. Create a dbt Cloud Account: Sign up at dbt Cloud.
2. Connect to Your Data Warehouse: dbt Cloud supports Snowflake, Redshift, BigQuery, Databricks, and more.
3. Integrate with Git Repository: Connect your GitHub, GitLab, or Bitbucket repository to enable version control.
4. Configure User Permissions: Set up user roles and access levels for collaboration.

Infometry ensures a secure and optimized dbt Cloud setup by configuring best practices in RBAC, Git workflows, and CI/CD pipelines.

Step 3: Migrate dbt Project to dbt Cloud

• Clone your existing dbt Core project into the connected repository.
• Update the dbt_project.yml file to match dbt Cloud configurations.
• Validate all model configurations and dependencies.
• Test database connections and adjust credentials if necessary.

Infometry’s experts accelerate this process by using automated migration scripts and data validation frameworks.

Step 4: Configure Job Scheduling

• Set up dbt Cloud jobs to run models, tests, and snapshots at scheduled intervals.
• Define execution environments (Development, Staging, Production).
• Implement error handling and alerting mechanisms.

With Infometry’s best-in-class scheduling strategies, customers have reduced pipeline downtime by over 50%.

Step 5: Validate and Monitor Performance

• Run end-to-end validation tests to ensure data accuracy.
• Monitor job execution logs for potential failures.
• Implement performance optimizations, such as incremental model execution and optimized SQL queries.

Infometry provides post-migration performance tuning, helping enterprises optimize query execution time by up to 40%.

Why Choose Infometry for Your dbt Cloud Migration?

As a dbt implementation and cloud migration expert, Infometry has successfully migrated 100+ enterprises to dbt Cloud with:

• Zero Downtime Migration Strategies: Ensuring business continuity.
• Automated Validation Scripts: Maintaining data accuracy.
• Custom CI/CD Pipelines: Enabling seamless deployment.
• Performance Optimization Frameworks: Enhancing query speed and reducing costs.
• Improved Execution Performance: Faster and more efficient execution compared to dbt Core.
• Automated Version Management: Eliminates the need to maintain dbt versions, always running on the latest updates.
• Comprehensive Log History: Provides excellent visibility with a detailed lineage view for effective troubleshooting.

By leveraging Infometry’s expertise, organizations can accelerate their dbt Cloud adoption and maximize the value of modern data transformation.

Final Thoughts

Migrating from dbt Core to dbt Cloud is a strategic move that unlocks enhanced scalability, automation, and collaboration for data teams. While the migration process may seem complex, partnering with an experienced dbt consultant like Infometry ensures a smooth and optimized transition.

If you’re ready to take your dbt transformation to the next level, contact Infometry’s dbt experts today!