Compare Domo vs. dbt in 2026

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Average Ratings 49 Ratings

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ease

features

design

support

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Average Ratings 4 Ratings

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ease

features

design

support

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Description

Domo has become a part of Progress Software, integrating its AI and data platform into Progress' suite of offerings. This cloud-native AI data readiness platform not only enhances but also expands Progress' existing data solutions, fostering significant synergies that facilitate the development of innovative, secure, and scalable AI data readiness solutions on a global scale. These combined strengths will assist clients in transforming fragmented enterprise data and insights into governed, AI-ready intelligence, thus elevating the security, governance, and cost-effectiveness of AI-driven projects. Positioned as the agentic platform for the intelligent enterprise, Domo empowers organizations to connect, govern, activate, and disseminate both data and AI effectively. Collaborating with cloud data platforms such as Snowflake, BigQuery, and Databricks, Domo enables the conversion of governed data into various AI agents, applications, workflows, dashboards, and analytics. Additionally, its robust data foundation, activation, and distribution layers empower teams to create and implement intelligence precisely where their work occurs, all while ensuring governance, security, and access controls are firmly in place across both data and AI initiatives. This comprehensive approach not only streamlines operations but also enhances the overall effectiveness of data-driven decision-making within organizations.

Description

dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use. With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.

API Access

Has API Yes

API Access

Has API No

Screenshots View All

Connect your entire data ecosystem. Use pre-built connectors and cloud integrations to securely centralize cloud, on-premises, and proprietary systems in one place.

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Integrations

Blotout Yes

Collate Yes

Meltano Yes

OpenMetadata Yes

Openbridge Yes

Snowflake Yes

Azquo Yes

Budgyt Yes

Cargo No

Exposebox Yes

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Explore All 137 Integrations

Integrations

Blotout Yes

Collate Yes

Meltano Yes

OpenMetadata Yes

Openbridge Yes

Snowflake Yes

Azquo No

Budgyt No

Cargo Yes

Exposebox No

Show More Integrations

Explore All 59 Integrations

Pricing Details

Domo uses a flexible credit-based pricing model designed to align costs with actual usage. Credits are consumed when you perform specific actions—such as storing data, updating tables, running workflows, or leveraging advanced capabilities like ML inference in the data pipeline. This transparent approach gives you predictable, scalable costs that grow with your business needs.

Free Trial Yes

Free Version No

Pricing Details

$100 per user/ month

Free Trial Yes

Free Version Yes

Deployment

Web-Based Yes

On-Premises No

iPhone App Yes

iPad App Yes

Android App Yes

Windows Yes

Mac Yes

Linux No

Chromebook Yes

Deployment

Web-Based Yes

On-Premises No

iPhone App No

iPad App No

Android App No

Windows No

Mac No

Linux No

Chromebook No

Customer Support

Business Hours Yes

Live Rep (24/7) Yes

Online Support Yes

Customer Support

Business Hours No

Live Rep (24/7) Yes

Online Support Yes

Types of Training

Training Docs Yes

Webinars Yes

Live Training (Online) Yes

In Person Yes

Types of Training

Training Docs Yes

Webinars Yes

Live Training (Online) Yes

In Person Yes

Vendor Details

Company Name

Domo

Founded

2011

Country

United States

Website

www.domo.com

Vendor Details

Company Name

dbt Labs

Founded

2016

Country

United States

Website

www.getdbt.com

Product Features

Agentic Data Management

AI Finance

Audience Intelligence

AI / Machine Learning Yes

Audience Segmentation Yes

Competitive Intelligence No

Content Tracking No

Data Visualization Yes

Data-Driven Insights Yes

Filters / Search Yes

Image Recognition No

Sentiment Analysis Yes

Social Listening No

Social Media Analytics No

Trend Tracking No

Big Data

Collaboration Yes

Data Blends Yes

Data Cleansing Yes

Data Mining Yes

Data Visualization Yes

Data Warehousing Yes

High Volume Processing Yes

No-Code Sandbox Yes

Predictive Analytics Yes

Templates Yes

Business Intelligence

Ad Hoc Reports Yes

Benchmarking Yes

Budgeting & Forecasting Yes

Dashboard Yes

Data Analysis Yes

Key Performance Indicators Yes

Natural Language Generation (NLG) No

Performance Metrics Yes

Predictive Analytics Yes

Profitability Analysis Yes

Strategic Planning Yes

Trend / Problem Indicators Yes

Visual Analytics Yes

Dashboard

Annotations Yes

Data Source Integrations Yes

Functions / Calculations Yes

Interactive Yes

KPIs Yes

OLAP Yes

Private Dashboards Yes

Public Dashboards Yes

Scorecards Yes

Themes Yes

Visual Analytics Yes

Widgets Yes

Data Analysis

Data Discovery No

Data Visualization Yes

High Volume Processing No

Predictive Analytics Yes

Regression Analysis No

Sentiment Analysis Yes

Statistical Modeling Yes

Text Analytics No

Data Engineering

Data Fabric

Data Access Management Yes

Data Analytics Yes

Data Collaboration Yes

Data Lineage Tools Yes

Data Networking / Connecting Yes

Metadata Functionality Yes

No Data Redundancy Yes

Persistent Data Management Yes

Data Governance

Access Control No

Data Discovery No

Data Mapping No

Data Profiling No

Deletion Management No

Email Management No

Policy Management No

Process Management No

Roles Management No

Storage Management No

Data Management

Customer Data Yes

Data Analysis Yes

Data Capture Yes

Data Integration Yes

Data Migration Yes

Data Quality Control Yes

Data Security Yes

Information Governance Yes

Master Data Management Yes

Match & Merge Yes

Data Modeling

Data Preparation

Collaboration Tools Yes

Data Access No

Data Blending No

Data Cleansing Yes

Data Governance Yes

Data Mashup No

Data Modeling Yes

Data Transformation Yes

Machine Learning No

Visual User Interface Yes

Data Science

Access Control No

Advanced Modeling Yes

Audit Logs No

Data Discovery No

Data Ingestion Yes

Data Preparation Yes

Data Visualization Yes

Model Deployment No

Reports Yes

Data Visualization

Analytics Yes

Content Management No

Dashboard Creation Yes

Filtered Views Yes

OLAP No

Relational Display Yes

Simulation Models No

Visual Discovery Yes

Data Warehouse

Ad hoc Query Yes

Analytics Yes

Data Integration Yes

Data Migration Yes

Data Quality Control Yes

ETL - Extract / Transfer / Load Yes

In-Memory Processing No

Match & Merge No

Decision Intelligence

Digital Analytics

Embedded Analytics

Ad hoc Query Yes

Application Development Yes

Benchmarking Yes

Dashboard Yes

Interactive Reports Yes

Mobile Reporting Yes

Multi-User Collaboration Yes

Self Service Analytics Yes

Streaming Analytics Yes

Visual Workflow Management Yes

ETL

Data Analysis Yes

Data Filtering Yes

Data Quality Control No

Job Scheduling No

Match & Merge No

Metadata Management No

Non-Relational Transformations No

Version Control No

iPaaS

AI / Machine Learning Yes

Cloud Data Integration Yes

Dashboard Yes

Data Quality Control Yes

Data Security Yes

Drag & Drop Yes

Embedded iPaaS Yes

Integration Management Yes

Pre-Built Connectors Yes

White Label No

Workflow Management Yes

Reporting

Customizable Dashboard Yes

Data Source Connectors Yes

Drag & Drop Yes

Drill Down Yes

Email Reports No

Financial Reports Yes

Forecasting Yes

Marketing Reports Yes

OLAP No

Report Export Yes

Sales Reports Yes

Scheduled / Automated Reports Yes

Statistical Analysis

Analytics Yes

Association Discovery Yes

Compliance Tracking Yes

File Management No

File Storage Yes

Forecasting Yes

Multivariate Analysis Yes

Regression Analysis Yes

Statistical Process Control No

Statistical Simulation Yes

Survival Analysis No

Time Series Yes

Visualization Yes

Product Features

Big Data

Your knowledge is based on information available until October 2023.

Collaboration Yes

Data Blends No

Data Cleansing Yes

Data Mining No

Data Visualization No

Data Warehousing No

High Volume Processing No

No-Code Sandbox No

Predictive Analytics No

Templates No

Data Catalog

Data Engineering

Data Integration

Data Lineage

Database Change Impact Analysis Yes

Filter Lineage Links Yes

Implicit Connection Discovery No

Lineage Object Filtering No

Object Lineage Tracing No

Point-in-Time Visibility No

User/Client/Target Connection Visibility No

Visual & Text Lineage View No

Data Modeling

Data Pipeline

dbt serves as the backbone for the transformation segment of contemporary data pipelines. After data is brought into a warehouse or lakehouse, dbt empowers teams to refine, structure, and document it, making it suitable for analytics and artificial intelligence applications. With dbt, teams can: - Scale the transformation of unrefined data using SQL and Jinja. - Manage workflows with integrated dependency tracking and scheduling capabilities. - Build trust through automated testing and ongoing integration processes. - Map data lineage across models and columns for quicker impact assessments. By incorporating software engineering methodologies into pipeline development, dbt assists data teams in creating dependable, production-ready pipelines that expedite the journey to insights and provide data primed for AI utilization.

Data Preparation

dbt enhances data preparation by providing a structured and scalable approach for teams to clean, transform, and organize raw data within the warehouse environment. Rather than relying on isolated spreadsheets or manual processes, dbt leverages SQL alongside established software engineering practices to ensure that data preparation is consistent, dependable, and collaborative. Utilizing dbt allows teams to: - Clean and standardize their data through reusable models that are version-controlled. - Implement business logic uniformly across all data sets. - Conduct automated tests to validate outputs prior to making data available to analysts. - Document findings and share relevant context, ensuring that every prepared dataset includes lineage and definitions. By treating data preparation as a coding process, dbt guarantees that the datasets created are not merely temporary solutions but are reliable, governed assets that are ready for production and can grow alongside the business.

Collaboration Tools Yes

Data Access No

Data Blending Yes

Data Cleansing Yes

Data Governance No

Data Mashup No

Data Modeling No

Data Transformation No

Machine Learning No

Visual User Interface No

Data Quality

Your knowledge is based on information available until October 2023.

Address Validation No

Data Deduplication No

Data Discovery No

Data Profililng No

Master Data Management No

Match & Merge No

Metadata Management No

DataOps

ETL

dbt revolutionizes the transformation aspect of ETL processes. By moving away from outdated pipelines and opaque transformations, dbt enables data teams to create, validate, and document their transformations directly within their data warehouse or lakehouse. With dbt, teams are equipped to: - Convert raw data into analytics-ready models utilizing SQL and Jinja. - Maintain data integrity through integrated testing, version control, and continuous integration/continuous deployment (CI/CD). - Streamline workflows across teams by using reusable models and centralized documentation. - Utilize contemporary platforms such as Snowflake, Databricks, BigQuery, and Redshift for efficient and scalable transformations. By prioritizing the transformation layer, dbt allows organizations to accelerate the development of data pipelines, minimize data liabilities, and provide reliable insights more swiftly—complementing the ingestion and loading components of a modern ELT architecture.

Data Analysis No

Data Filtering Yes

Data Quality Control Yes

Job Scheduling No

Match & Merge No

Metadata Management No

Non-Relational Transformations No

Version Control No