dbt Core vs dbt Cloud– Understanding 5 Easy Differences

As organizations accumulate more data, analysts face challenges in effectively utilizing the data collected by companies. Since big data comes in different forms and sizes, companies fail to create robust data pipelines to move data as soon as it arrives. This makes it difficult for businesses to enhance the quality of their data for analytical purposes. As a result, it creates data silos—data that is residing in a data storage system but are not used for analysis.

To address such problems, organizations use dbt—a data transformation tool. With dbt, organizations can quickly transform raw data to improve the quality for further use. dbt comes in two different forms dbt core and dbt cloud. Let’s understand the similarities and differences between dbt core vs dbt cloud.

dbt core is an open-source command line tool that is used to run your dbt projects. In other words, when using dbt core, you will have to use IDE to edit files locally and run projects using the command line tool. To work with dbt core, you need to be familiar with terminal commands like cd, pwd, ls or dir, and others to navigate through the project directories.

dbt Cloud — An Overview

dbt cloud is a browser-based platform that allows you to transform data and manage all the components in one place. With dbt cloud, you can simplify dbt project deployments and get access to enhanced features like scheduled runs of commands. While the dbt core is a free tool, dbt cloud works on a subscription model. It has 3 plans: developer, team, and enterprise. The developer is a free plan, the team plan costs $100, and the enterprise plan has bespoke pricing.

dbt Core vs dbt Cloud

Let’s understand dbt core vs dbt cloud based on different parameters.

dbt Core vs dbt Cloud: Cloud IDE

dbt Cloud offers an in-built IDE for building, testing, version-controlling, and deploying dbt projects. Within the cloud IDE, you can view Python models in DAG, which is a lineage graph. It is used to visualize the workflow and connections of dbt models. Although DAG is also a part of dbt core, you can only see the DAG in the documentation.

Even documenting a project becomes straightforward with cloud IDE as you can view the generated documentation in real-time. As a result, you can change the documentation before committing the changes to production. The best part is that documents are hosted directly in the cloud, which is not the case for dbt core. In dbt core, the documentation will reside in the local project directory. You must find a host to ensure other team members can access the documentation.

dbt cloud IDE also offers features like autocomplete, version control, and log for debugging. dbt autocomplete helps you quickly write code that is unique to dbt. For example, you can quickly autocomplete code for referencing, source, macro, and more. And the in-built version control feature allows you to start building incrementally with just a few clicks. 

Overall, the integrated IDE eliminates the need for moving between different tools or applications while working on a project.

dbt Core vs dbt Cloud: Scheduling Jobs

With dbt core, you can run scheduled commands to automate your workflows. It is a crucial feature in dbt cloud that is missing in dbt core. Scheduling removes the need for manual work to run jobs. You can schedule bespoke or recurring tasks in advance to ensure jobs are completed in time. This is a significant benefit if you extensively use dbt for your data transformation. 

For scheduling with dbt core, you will have to use solutions like GitHub Actions, Gitlab CI, and Airflow. This will allow you to trigger dbt jobs and run them as per the schedule. 

dbt Core vs dbt Cloud: Continuous Integration

Continuous Integration (CI) ensures that new code changes do not break the existing codebase. With dbt cloud’ CI, you can test every code change before deployment. Connect your project with GitHub, GitLab, or Azure DevOps to obtain CI capabilities. 

dbt cloud responds to pull requests for new changes and tests the affected models in the staging environment. This allows you to see the result and then confidently deploy the new code. On the other hand, CI with dbt core is not directly supported. If you want CI with dbt core, you must embrace third-party tools like GitHub Actions.

dbt Core vs dbt Cloud: API

dbt cloud offers two APIs—Administrative API and Metadata API—for its team and enterprise plans. Administrative API is used to manage dbt cloud accounts, start jobs from orchestration tools, and more. On the other hand, Metadata API provides you with information about your projects. The data from Metadata API can be used for analysis to optimize the dbt project workflows. 

Some core use cases of Metadata API are to enhance quality and gain operational efficiency. For improved quality in analytics with transformed data, you can monitor test failures, source freshness, run status, and other dependencies. And to obtain operational efficiency, Metadata API enables you to analyze model build time and run counts. This can help teams understand the computational requirements and reduce costs.

APIs are not directly available for dbt core. However, you can use third-party tools like Elementary to collect metadata of your project runs. But, there are no alternatives for the Administration API of the dbt cloud.

dbt Core vs dbt Cloud: Semantic Layer

dbt cloud offers a semantic layer in public preview to define essential business metrics like revenue, churn, and customer. With dbt semantic, you can eliminate duplication of important metrics across different use cases to create a single source of truth. Consistency ensures BI analysts get accurate insights to make data-driven decisions.

dbt semantic also allows you to import metrics with Metadata API, query metrics from the dbt proxy server, and more. To obtain features of dbt cloud semantic in dbt core, you can use Looker’s LookML. However, it will only be feasible for business intelligence use cases; it doesn’t support other downstream processes.  

Why Upgrade to dbt Cloud over dbt Core?

dbt cloud and dbt core have their own advantages and disadvantages. However, the most significant benefit of dbt cloud is unifying the entire workflow. With dbt cloud, you can manage all your tasks like model building, version-controlling, testing, documenting, and scheduling from a single platform.

If you had to do such tasks with dbt core, you would have to switch between different applications. This can become challenging as the complexity of your project increase over time. Managing numerous tasks simultaneously with dbt core can reduce the turnaround time for dbt projects.

Therefore, you can consider upgrading from dbt core to dbt cloud. Migrating from dbt core to dbt cloud is straightforward because dbt core projects can run on dbt cloud seamlessly. You can even build simultaneously on dbt core and dbt cloud. However, embracing the dbt cloud to enhance collaboration in data transformation tasks is more beneficial.

Since its release in January 2019, dbt cloud has become a one-stop solution for all leveraging dbt transformation capabilities. It eliminates the need to manage infrastructure and integration with third-party tools for monitoring, scheduling, and developing. As an organization, dbt is now focusing on further enhancement of dbt cloud. You can expect additional features in the future to simplify the entire data transformation workflow. 

Conclusion

As dbt cloud is a SaaS solution, it has several advantages over dbt core. You can enhance your productivity by using automation features of dbt cloud like scheduling model runs, code autocompletes, and document hosting. dbt cloud also enables you to implement best practices like CI in project development. Such features of dbt cloud keep you up-to-date with modern project development requirements while working with big data. 

You can simplify your task with a cloud-based ELT solutions like Hevo Data which automates the data integration process for you and runs your dbt projects to transform data present in your data warehouse. At this time, the dbt Core™ on Hevo is in BETA. Please reach out to Hevo Support or your account executive to enable it for your team.

Visit our Website to Explore Hevo

Offering 150+ plug-and-play integrations and saving countless hours of manual data cleaning & standardizing, Hevo Data also offers in-built pre-load data transformations that get it done in minutes via a simple drag-and-drop interface or your custom python scripts. 

Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

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