dbt Core vs. dbt Cloud

April 4, 2024

What is dbt?

The main task of an Analytics Engineer is to efficiently transform raw data into organized and ready-to-be-used data. dbt (data build tool) has become a common tool for data teams to streamline this data transformation process. dbt enables data professionals to execute, test and document their data workflows.

However, dbt is not a single tool, it comes in two flavors : dbt Core and dbt Cloud. Each version brings its unique set of features, usability, and deployment methods to the table.

This article aims to shed light on dbt Core and dbt Cloud, comparing their functionalities, user experiences, and ideal use cases, thus (hopefully 🤞) guiding you to make an informed decision that aligns with your data engineering and analytics goals.

TL;DR: Key Differences Between dbt Core and dbt Cloud

• Deployment: dbt Core is designed for local deployment, allowing users to run and manage data transformations directly on their machines, providing a hands-on, customizable setup. In contrast, dbt Cloud offers a cloud-based platform, eliminating the need for manual environment setup and maintenance.
• User Interface: dbt Core operates through a command-line interface (CLI), catering to those who prefer text-based commands and scripts. dbt Cloud, however, introduces a web-based integrated development environment (IDE), making it accessible and collaborative, with features that streamline the development process.
• Features: While dbt Core focuses on the core functionalities of data transformation with an emphasis on version control and testing, dbt Cloud expands upon this with advanced features like job scheduling, orchestration, and access control, enhancing automation and collaboration across data teams.
• Price: dbt Core is free and open-source, appealing to individuals and teams looking for a powerful, no-cost solution to data transformation. dbt Cloud offers a subscription-based model, providing additional managed services, support, and advanced features for teams requiring more than what the open-source version can offer.

Understanding dbt Core

At its heart, dbt Core is the foundational open-source version of dbt. It’s primarily operated through a command-line interface (CLI), which makes it great for those who are comfortable with text-based commands and scripts. dbt Core having no graphical user interface makes it more appropriate for technical data professionals.

Key Features of dbt Core:

• Open-Source and Community-Driven: dbt Core benefits from a vibrant community of analytics engineers. Its open-source nature means that it’s continually being refined and extended with contributions from the community.
• Version Control Integration: It integrates seamlessly with version control systems like Git, allowing changes to data models and scripts to be tracked, reviewed, and collaborated on by teams, thus ensuring integrity and continuity in data projects.
• Local Development: dbt Core is designed for local development, meaning you can develop and test your data transformations on your machine before deploying them. This approach favors those who prefer a local setup and a more hands-on, detailed control over their development environment.

dbt Core is best suited for data professionals who are not only comfortable with SQL but also prefer a code-first approach to their data transformations. It’s for users who appreciate the control and flexibility that comes with manual setup, version control, and local development. These users typically have a solid foundation in software engineering best practices and are looking for a powerful, customizable tool to integrate into their data workflows.

Target Audience for dbt Core:

Choosing dbt Core is a decision often driven by a desire for maximum flexibility and control over the data transformation process. It’s ideal for teams that have the technical skills to manage their infrastructure and workflows and who value the open-source ethos of sharing knowledge and contributing back to the community. Also, for large teams, dbt Cloud can get quite expensive and it may make sense for larger teams to manage their own infrastructure.

In summary, dbt Core represents the essence of dbt’s approach to data transformation, offering a robust, command-line-driven tool for those who seek to build sophisticated, reliable, and scalable data pipelines. An important point to note that dbt Core is basically a command line interface and does not provide features such as scheduling and orchestration.

Exploring dbt Cloud

dbt Cloud elevates the dbt experience by offering a fully managed service that extends the foundational capabilities of dbt Core. It’s designed for teams and organizations seeking to streamline their data transformation workflows without the overhead of manual setup and maintenance. dbt Cloud not only simplifies the operational aspects of using dbt but also introduces a suite of advanced features aimed at enhancing productivity and collaboration.

Additional Features of dbt Cloud:

• Web-based Integrated Development Environment (IDE): dbt Cloud features a user-friendly, web-based IDE, making it easier for users to write, test, and manage their dbt projects. This IDE lowers the entry barrier for new users and facilitates real-time collaboration among team members.
• Job Scheduling and Orchestration: With dbt Cloud, scheduling and orchestrating data transformation jobs becomes a breeze. It allows you to automate your data workflows, ensuring that your data transformations are performed reliably and on schedule, without the need for manual intervention.
• Enhanced Collaboration Tools: dbt Cloud promotes teamwork through features like project sharing, access control, and integrated commenting. These tools help teams work together more effectively, streamline the review process, and ensure that everyone is aligned on project goals and progress.
• Managed Service: As a fully managed service, dbt Cloud takes the hassle out of infrastructure management. It handles the setup, maintenance, and scaling of your dbt environment, allowing you to focus on deriving insights from your data.

Target Audience for dbt Cloud:

dbt Cloud is ideal for teams and organizations that prioritize convenience, efficiency and collaboration in their data projects.

It’s especially beneficial for those with lower levels of technical expertise, as the web-based IDE and managed services make dbt more accessible.

dbt Cloud is tailored for businesses looking to reduce the operational complexity of their data workflows and for teams that benefit from the enhanced collaboration features to streamline their data transformation processes.

Conclusion

Choosing between dbt Core and dbt Cloud comes down to your project needs, team expertise, and desired level of infrastructure management.

• dbt Core, being free and open-source, offers flexibility and control for those comfortable with a CLI and a hands-on, code-first approach. It’s ideal for teams valuing open-source collaboration, customization, and managing their budget effectively.
• dbt Cloud, meanwhile, caters to teams seeking streamlined operations with its managed service, advanced scheduling, and user-friendly IDE, perfect for enhancing team collaboration and efficiency.

In essence, whether you opt for the granular control of dbt Core or the streamlined convenience of dbt Cloud, both tools are solid choices for data transformations.