Have you ever struggled with creating robust data pipelines to move data as soon as it arrives? 

Or have you encountered data silos where data is stored but not used for analysis? 

To address these challenges, organizations turn to dbt (data build tool)---a powerful data transformation tool that helps streamline the building, testing, and deploying of data pipelines. dbt enables businesses to enhance the quality of raw data for analytics and other downstream applications. dbt comes in two different forms: dbt Cloud and dbt Core. Understanding the differences is critical for choosing the right tool to meet your specific data transformation needs. Before comparing dbt Core vs dbt Cloud, let's define each tool.

dbt Core

dbt Core is a command line tool that allows you to edit your dbt projects locally using an IDE and then execute those projects using basic terminal commands. dbt Core is compatible with many popular data warehouses, including Snowflake, BigQuery, and Redshift

To use dbt Core, you need to install it on the command line. You can do this by downloading a package like dbt-snowflake that includes all the necessary code for dbt to work with your data warehouse.

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dbt Cloud

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dbt Cloud is a powerful platform that provides a wide range of features to simplify and streamline data transformation projects. It provides a web-based interface that allows you to develop, test, schedule, document, and investigate data models in one centralized location.

dbt Cloud employs PostgreSQL as its backend database, and it utilizes S3-compatible Object Storage systems for logs and artifacts. To ensure the security of data that is not in motion, dbt Cloud encrypts all data at rest on its servers utilizing AES-256 encryption.

dbt Cloud vs dbt Core: Differences 

Understanding the differences between dbt Cloud and dbt Core is essential when choosing the right tool for your data management needs. Let's dive into the key differences between dbt Cloud and dbt Core!

dbt Cloud vs dbt Core: Job Scheduling Capabilities

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dbt Cloud offers native scheduling capabilities. You can schedule jobs directly in the dbt Cloud UI without setting up external scheduling tools. The scheduling UI in dbt Cloud allows you to specify the job frequency, start time, and time zone. You can also set up alerts to receive notifications if a scheduled job fails or runs longer than expected. Additionally, dbt Cloud allows more advanced scheduling features, such as job dependencies, job timeouts, and retry logic.

On the other hand, dbt Core does not provide native scheduling capabilities. In dbt Core, scheduling jobs can be managed through external tools like GitHub Actions, Gitlab CI, and Airflow. You have to set up the scheduling tool, configure the job schedule, and then call the dbt command-line tool as a scheduled task.

dbt Cloud vs dbt Core: API Support

dbt Cloud offers two APIs: the dbt Cloud Administrative API and the dbt Metadata API for its team and enterprise plans. The Administrative API allows you to start jobs, download artifacts, and manage your dbt accounts. The Metadata API provides information about your project, which can help you improve its quality and efficiency.

dbt Core doesn't have APIs available, but you can use external tools such as Elementary to collect metadata from your project runs. However, there are no alternatives to replace the Administration API provided by dbt Cloud.

dbt Cloud vs dbt Core: Cloud Integrated Development Environment (IDE)

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dbt Core is a command-line tool, which means it does not have a cloud-based IDE for building, testing, and deploying dbt projects. You must rely on local IDEs like VS Code to edit and manage their dbt projects.

On the other hand, dbt Cloud offers an in-built cloud IDE for building, testing, version-controlling, and deploying dbt projects. Within the cloud IDE, you can view Python models in a DAG (Directed Acyclic Graph) to visualize the workflow and connections of dbt models. The DAG feature is also available in dbt Core but can only be viewed in a given model’s documentation.

In addition to DAG visualization, dbt Cloud offers features like real-time documentation, autocomplete, version control, and debugging logs. You can edit and view the documentation in real time within the cloud IDE. Whereas, in dbt Core, documentation resides in the local project directory, and you must find a host to access it. 

dbt Cloud vs dbt Core: Documentation Capabilities

dbt Cloud vs dbt Core - documentation

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dbt Cloud makes creating and displaying documentation for your dbt project simple by combining it with the job scheduler. You can select an option to update documentation automatically with each run. As a result, the documentation stays current and reflects the latest changes in your dbt project. The documentation is helpful for other developers, business stakeholders, and future reference, as it provides a clear understanding of model relationships and logic. You can access the documentation through the documentation tab in dbt Cloud or directly from the IDE.

On the other hand, dbt Core can use Amazon S3 or Netlify to host dbt docs. However, this option requires more effort and knowledge about infrastructure to ensure the security of your documentation.

dbt Cloud vs dbt Core: Support for Semantic Layer