dbt Core vs. dbt Cloud

Take a look at the dbt Core vs. dbt Cloud comparison to gauge which tool is better suited for your business requirements.

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updatedJanuary 2, 2024

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DBT

dbt is available in two forms–dbt Core and dbt Cloud. There are a few commands like dbt run, dbt build, and dbt test that are common to both. While these tools have similarities, understanding the dbt Cloud vs. dbt Core distinction is crucial, too. It greatly helps you select the right tool suited to your needs.

dbt Core Overview

Image Source: dbt

dbt Core is an open-source project where you can develop and execute your dbt projects directly through a command line interface. There are a few ways to install dbt Core on a command line:

Install using pip: If you have Windows or Linux operating systems, you can use namespace pip modules through virtual environments to install dbt Core.
Homebrew: If you use MacOS, or if you want to use dbt with Postgres, Snowflake, BigQuery, or Redshift, it is recommended to use Homebrew formulae.
Pre-built Docker Image: You can access dbt Core and all its adapter plugins that dbt Labs maintain through Docker images. The prerequisites are that you must have Docker installed and have a general understanding of dbt Core versions, adapters, and workflow.

When using dbt Core from the command line, you will require a profiles.yml file. This file encapsulates all the necessary information for dbt to establish a connection with your chosen data platform. Some data platform providers that connect with dbt Core include Apache Spark, Google BigQuery, Amazon Redshift, PostgreSQL, and more.

Strengths of dbt Core

Flexibility: As an open-source solution, dbt Core gives data engineers complete control over their development environment. You can customize your IDE (like VSCode), integrate with any CI/CD tools, and create custom scripts for deployment. This flexibility is particularly valuable when you need to adapt the tool to unique infrastructure requirements or specific organizational workflows.
Suitable for Small Teams: Being open-source, dbt Core is free to use. For small data teams with strong engineering capabilities, this can be a significant advantage. The only costs involved are related to maintenance and the time spent on setup and configuration. Teams can leverage existing tools like Airflow for orchestration without incurring additional licensing fees.
Open Source Community: Being open-source, dbt Core benefits from a vibrant community of contributors. This means access to a wide range of community-built packages, regular updates, and extensive community support through forums.

Weaknesses of dbt Core

Complex Setup and Maintenance: The initial setup of dbt Core requires significant engineering effort. You need to handle local environment configuration, package management, and integration with other tools. For each new team member, you'll need to repeat this setup process and maintain documentation for your specific implementation. This can become time-consuming as teams grow.
Limited Collaboration Features: While dbt Core can be used with version control systems, it lacks built-in collaboration features. Teams often need to implement additional tools and processes for code review, documentation sharing, and project management. This can lead to friction in the development process, especially when working with non-technical stakeholders.
Manual Orchestration Requirements: dbt Core doesn't come with built-in scheduling capabilities. You'll need to set up and maintain separate orchestration tools (like Airflow or Prefect) to schedule and monitor your dbt jobs. This adds another layer of complexity to your data stack and requires additional expertise to manage effectively.

dbt Cloud Overview

Image Source: dbt Cloud

dbt Cloud is a robust browser-based platform that offers a comprehensive suite of features to streamline and simplify data transformation projects. It serves as a web-based interface that centralizes data model development, testing, scheduling, modification, and documentation.

Let’s understand the platform’s internal infrastructure as well as the security measures taken by dbt Cloud.

Architecture

The dbt Cloud application consists of two main components:

Static: This part operates continuously to support essential dbt Cloud functions, such as dbt Cloud web-based applications.
Dynamic: These parts are generated per demand to manage specific tasks, such as requests involving the Integrated Development Environment (IDE).

In its infrastructure, dbt Cloud utilizes PostgreSQL as its backend database. And it leverages S3-compatible object storage systems for storing logs and artifacts; the metadata that is generated while running a dbt project. dbt Cloud also employs Kubernetes storage solutions to handle large volumes of data dynamically.

Security Solutions

To ensure the security of your data, dbt Cloud is HIPAA, SOC2 Type II, ISO 27001:2013, and PCI compliant. It also implements AES-256 encryption on its servers.

dbt Cloud also provides integrations with authentication services and data protection through single sign-on (SSO) features. This minimizes the number of credentials you need to manage while accessing the platform.

With Role-Based Access Control (RBAC) feature, you can also grant or restrict access to dbt projects by defining user roles.

Subscriptions

Unlike dbt Core, which is a free tool, dbt Cloud operates on a subscription-based model. There are three distinct plans that will be touched upon further in this article. However, this is not the sole difference between the two tools.

Strengths of dbt Cloud

Browser-based IDE: dbt Cloud provides a browser-based IDE that eliminates the need for local setup. This significantly reduces onboarding time for new team members and ensures consistency across the development environment. The built-in Git integration makes version control more accessible to less technical users.
Built-in Orchestration and Monitoring: The platform includes native job scheduling, monitoring, and alerting capabilities. This eliminates the need for separate orchestration tools and provides a unified interface for managing your data transformations. The visual job scheduling interface is particularly user-friendly compared to traditional orchestration tools.
Enterprise-Grade Security and Governance: dbt Cloud offers robust security features like SSO integration, role-based access control, and audit logging. These features are essential for enterprises and regulated industries. The platform also provides hosted documentation and lineage visualization, making it easier to maintain data governance.

Weaknesses of dbt Cloud

Cost: The pricing model can become expensive as teams grow. Organizations with heavy transformation workloads might need to upgrade to Enterprise pricing.
Limited Customization Options: Compared to dbt Core, Cloud offers less flexibility in terms of customizing the development environment and deployment processes. You're bound by the platform's features and limitations, which might not align perfectly with specific organizational needs or existing workflows.
Platform Dependency: Adopting dbt Cloud means relying on a third-party service for critical data infrastructure. This includes potential downtime during service maintenance, feature changes that might affect your workflows, and being tied to the platform's upgrade schedule. While dbt Cloud offers high availability (99.95% uptime guarantee), some organizations might prefer having complete control over their infrastructure.

Let’s look at the dbt Core vs. dbt Cloud in greater detail.

dbt Cloud vs. dbt Core: Semantic Layer

Image Source: dbt

The main difference between dbt Core and dbt Cloud is that dbt Core is a free, open-source command-line tool that requires manual setup and infrastructure management, while dbt Cloud is a hosted platform offering automated scheduling, monitoring, and collaborative features with a web-based interface.

If you have large databases spread across multiple locations, it is a challenging task to ensure standardized formulae and values throughout the organization. Here’s where dbt Cloud’s semantic layer helps you process crucial business benchmarks like revenue, or return on investments. Powered by MetricFlow, a tool designed to generate SQL queries, this layer stores the standardized metrics you have created.

Once defined, the metrics will be used across platforms, downstream data tools, and applications in your business. It will not only eliminate metric duplication but also give reliable information across the organization. Thus, you get unified insights while making critical decisions for the business.

While dbt Core also possesses a MetricFlow-powered semantic layer, it does not come with all the features like dbt Cloud. You can access the dbt Semantics interfaces using Apache 2.0. However, two components of the dbt semantic layer are only available under the Cloud’s paid plans.

The first is the Service Layer, which coordinates query requests and directs metric queries to the engine for execution. The next is Semantic Layer APIs, where you can submit metric queries via GraphQL and JDBC APIs. These help in building integrations with various other tools.

dbt Cloud vs. dbt Core: APIs

dbt Cloud offers three distinct APIs, which are:

dbt Cloud Administrative API: As the name suggests, the Administrative API helps you manage your dbt Cloud accounts. This API is enabled by default if you purchase Teams and Enterprise Plans. It also allows you to initiate a job run from an orchestration tool and download artifacts once the job is completed.
dbt Cloud Discovery API: This API offers insights into project-related metadata, which includes the models, sources, nodes, and execution results. Utilizing metadata will help you establish systems for monitoring data, ensuring quality, and streamlining data pipelines.
You can access the Cloud Discovery API in the following ways:
~Ad hoc queries
~Custom applications
~Partner ecosystem integrations
Using functionalities like model timing and dashboard status tile
dbt Semantic Layer API: Since the semantic layer stores the standardized metrics for your business, you can leverage this API to integrate the benchmarks with several applications. These include machine learning, business intelligence, reporting, and cataloging tools, which play a crucial role in further analyzing your data.

While comparing APIs for dbt Core vs. dbt Cloud, the former does not have direct API access. To gather metadata from projects running in dbt Core, you can make use of third-party tools. However, the Administrative API is exclusive to the Cloud, and you will still not find alternatives in Core.

dbt Cloud vs. dbt Core: IDE

Image Source: dbt

The dbt Cloud Integrated Development Environment (IDE) is a unified web-based interface that facilitates the creation, testing, execution, and version control of dbt projects. All the dbt code is compiled into SQL and directly executed into your database.

There are a few key features of Cloud’s IDE that make it a robust editing environment:

SQL Syntax Highlighting: This feature simplifies code interpretation and readability while minimizing syntax errors.
Navigation Tools: These tools facilitate line jumping, text finding, and replacing, thus helping you swiftly move between multiple project files.
Auto-completion: Auto-completion is a commonly used feature where suggestions for table names, columns, and arguments are automatically suggested when you type. This not only reduces errors but also saves you time.

When launching the dbt Cloud IDE, there are three primary start-up states:

Creation Start: This phase occurs only when you initialize IDE for the first time. It has a slightly longer run time because the IDE is in the process of cloning your git repository.
Cold Start: The process occurs when you commence with a new development session. However, the environment is only accessible for three hours and shuts down if inactivity is prolonged.
Hot Start: This state refers to the continuation of existing or active development sessions.

dbt Cloud also provides a graphical representation of Python models through a Directed Acyclic Graph (DAG). In a DAG, the nodes are connected in a directional manner, but there are no closed loops or cycles. This feature is handy when you want to visualize workflows and relationships between your data models.

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Image Source: dbt

In the dbt Core vs. dbt Cloud comparison for IDEs, it is interesting to note that the DAG and its hallmark feature, Lineage Graph, also exist in dbt Core. You can explore these features in the documentation of your project’s directory. To get started with DAG in dbt Core, refer to the dbt docs. However, it is important to note that dbt core lacks comprehensive Cloud features for editing and managing your dbt projects.

dbt Cloud vs. dbt Core: Jobs Scheduling Capabilities

The job scheduler is pivotal in the dbt Core vs. dbt Cloud differentiation. In dbt Cloud, this feature serves as the foundation for executing jobs in the data pipeline. You are relieved from the responsibility of constructing and managing the data transformation infrastructure through the built-in native scheduling capabilities.

Some of the key tasks handled by the scheduler include:

Queuing Jobs: This organizes and prioritizes the tasks for execution.
Creating Temporary Environment: Here, the scheduler sets up the necessary environment to run the dbt commands.
Storing dbt Artifacts: The scheduler stores dbt artifacts for direct utilization or ingestion through the Cloud Discovery API.
Provides Debugging Logs: This feature offers you logs for debugging and resolves issues in the pipeline faster.

dbt Core does not offer you a native feature of scheduling commands to automate your workflows. To achieve this, you need to rely on external solutions that punctually execute dbt jobs based on the schedules you define. Here, you must know how to set up and configure scheduling tools to execute tasks within the dbt command-line tool.

The alert setting is another differentiating point in the comparison of dbt Core vs. dbt Cloud. With dbt Cloud, you can set up alerts to receive notifications on your email about various reasons, such as the success, failure, or cancellation of jobs. This feature keeps you updated with the status of your workflow at all times.

dbt Cloud vs. dbt Core: Continuous Integration (CI)

Image Source: dbt

To establish a Continuous Integration (CI) workflow within dbt Cloud, you can automate the process of testing code alterations and then integrate them into the production environment. While executing a CI job, only the modified data assets in your pull requests are built and tested in the staging schema. You also have the flexibility to configure settings within your Git provider to allow pull requests and pass CI checks for merging data.

Since dbt Cloud offers built-in CI functionality, it eliminates the need for third-party tools. Conversely, dbt Core does not inherently support CI. However, you can implement CI by relying on third-party CI tools to run tests or deploy models whenever codebase changes occur.

dbt Cloud vs. dbt Core: Pricing Models

One of the significant points of difference between dbt Core vs. dbt Cloud is the pricing model. dbt Core is an open-source tool designed to assist you in transforming your data through best practices in analytics engineering. It is accessible through a command line interface to develop, test, and version control projects freely within a hosted environment.

On the other hand, dbt Cloud operates as a subscription-based service, offering three distinct plans:

Image Source: dbt

Developer Plan: This is a free plan designed for individual data engineers. It features a browser-based IDE, job scheduling, CI, and unlimited data runs, but the plan is limited to one project only.
Team Plan: Priced at $100 per developer seat per month, this plan allows API access, semantic layer, and outbound webhooks. You can run up to two jobs concurrently, too.
Enterprise Plan: This is a custom-priced plan tailored for large organizations that need enhanced security and access controls. You get native support for GitHub, GitLab, and Azure DevOps while running unlimited projects.

Choose dbt Core if you have a small, technically skilled team that values complete control over infrastructure and doesn't mind managing their own tooling. Opt for dbt Cloud if you want a managed solution with easier onboarding, built-in scheduling, and collaboration features - especially if you're a growing team or have fewer technical users. The decision often comes down to your team's size, technical expertise, and whether you prefer the flexibility of open-source (Core) or the convenience of a managed service (Cloud).

Final Takeaways

At the end of the dbt Core vs. dbt Cloud comparison, you must have realized that both tools have their strengths and weaknesses. The standout advantage of dbt Cloud is consolidating all your workflow by leveraging dbt in-built transformation capabilities. If you currently use dbt Core, consider upgrading to Cloud. You will get access to an expansive array of features, APIs, and benefits to manage your data pipelines.

However, before transforming your data with dbt Cloud, you must consolidate all your data. This can be easily done by simple, no-coding platforms like Airbyte.

Airbyte provides a great way to set up a unified data pipeline from several sources to a cloud data warehouse of your choice. This data integration and replication platform has 350+ pre-built connectors as well as a Connector Development Kit to build custom connectors within minutes. Sign up today to bring your data together and then transform it for better understanding. As data volumes grow, challenges such as issues in the quality of data, inconsistent metrics, or inaccurate information arise. dbt (data build tool) is a highly effective solution for addressing these problems. It is an SQL-first data transformation tool that enables you to structure your projects and deploy code for further analysis. You can build models, ensure quality testing, and obtain comprehensive data documentation with dbt.

Can I transition from dbt Cloud to dbt Core?

Yes, you can transition from dbt Cloud to Core since both use the same underlying transformation engine and SQL files - you'll just need to set up local development environments and orchestration tools.

Is dbt Cloud suitable for non-technical users?

Yes, dbt Cloud is more suitable for non-technical users due to its browser-based IDE, visual interface for scheduling, and lower barrier to entry without command-line requirements.

How does the user experience differ between dbt Core and dbt Cloud?

dbt Core requires command-line expertise and local setup, while dbt Cloud offers a web-based IDE with point-and-click interfaces for development, scheduling, and documentation - making it more user-friendly and collaborative.

Are there any notable features in dbt Cloud that make it worth the investment?

Yes - the built-in job scheduler, hosted documentation, browser-based IDE, and enterprise features like SSO and audit logging make it worth the investment, especially for growing teams that want to focus on analytics rather than infrastructure maintenance.

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