---
title: dbt Reviews
meta_title: 'dbt Reviews 2026: Details, Pricing, & Features | G2'
meta_description: Filter 209 reviews by the users' company size, role or industry
  to find out how dbt works for a business like yours.
aggregate_rating:
  rating_value: 4.7
  review_count: 209
  scale: '5'
date_modified: '2026-08-10'
parent_category:
  name: IT Infrastructure
  url: https://www.g2.com/categories/it-infrastructure
---


# dbt Reviews
**Vendor:** Fivetran  
**Category:** [DataOps Platforms](https://www.g2.com/categories/dataops-platforms)  
**Average Rating:** 4.7/5.0  
**Total Reviews:** 209
## About dbt
dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.



## dbt Pros & Cons
**What users like:**

- Users love the **ease of use** of dbt, thanks to its clear structure, intuitive documentation, and seamless integration. (34 reviews)
- Users value dbt for its **integration of software engineering best practices** , enhancing maintainability and collaboration in SQL transformations. (21 reviews)
- Users value the **automation** features of dbt, significantly enhancing SQL code maintainability and transforming data workflows. (17 reviews)
- Users value the **transformative power** of dbt, efficiently organizing and modeling data for actionable insights. (16 reviews)
- Users value dbt for its **high data quality** , ensuring integrity and enhancing analytics workflows through modularization and documentation. (14 reviews)
- Productivity Improvement (14 reviews)
- Solution Efficiency (13 reviews)
- Analytics (12 reviews)
- Efficiency Improvement (12 reviews)
- Users value the **seamless integrations** of dbt with various platforms, enhancing their modeling and transformation processes. (12 reviews)

**What users dislike:**

- Users face challenges with **limited functionality** in dbt due to rigid models and debugging difficulties, affecting project progress. (13 reviews)
- Users often face **dependency issues** with dbt, leading to time-consuming troubleshooting and disruption in workflows. (12 reviews)
- Users find the **steep learning curve** of mastering concepts like Jinja and Git to be quite challenging. (10 reviews)
- Users struggle with **unhelpful error messages** in dbt, making troubleshooting difficult and frustrating. (9 reviews)
- Users face **confusing error reporting** that complicates troubleshooting and hinders quick identification of issues. (9 reviews)
- Users find the **complex setup** of dbt challenging, especially with confusing error messages and a steep learning curve. (8 reviews)
- Debugging Issues (8 reviews)
- Users find the **learning curve quite challenging** , particularly with advanced features and debugging complexities in dbt. (8 reviews)
- Query Issues (8 reviews)
- Technical Issues (8 reviews)

## dbt Reviews
  ### 1. A Developer Friendly Transformation Tool

**Rating:** 4.0/5.0 stars

**Reviewed by:** Syed A. | Data Engineer, Information Technology and Services, Enterprise (> 1000 emp.)

**Reviewed Date:** February 03, 2026

**What do you like best about dbt?**

I like best about dbt is how it brings a clean, developer‑friendly structure to analytics work. It makes modeling and transforming data feel organized and predictable, thanks to its simple SQL‑first approach and clear project layout. I also really appreciate how dbt encourages good engineering practices such as version control, testing, documentation. So the entire workflow becomes more reliable and collaborative.

**What do you dislike about dbt?**

I dislike about dbt is that some parts of the workflow can feel a bit inflexible, especially when you're trying to customize how tests or models behave in more complex projects. It also relies heavily on command‑line and configuration files, which can become demanding as the project grows. On top of that, dbt doesn’t handle ingestion or real‑time needs, so user often need additional tools to complete the pipeline, which makes the setup feel less seamless.

**What problems is dbt solving and how is that benefiting you?**

dbt solves the problem of scattered, inconsistent transformation logic by giving user a clean, structured way to manage SQL models, tests, and documentation in one place. I no longer needs to deal with random queries or unclear business rules, everything becomes version‑controlled and easy to trace. Which helps me in my workflows to become productive.

  ### 2. Speedy but however it is quite pricey and resource hungry

**Rating:** 4.5/5.0 stars

**Reviewed by:** Joseph S. | Software Developer, Small-Business (50 or fewer emp.)

**Reviewed Date:** January 20, 2026

**What do you like best about dbt?**

The way it handles large amounts of data, as well as how it integrates into AWS (S3/Glue) is great. This allows me to avoid building custom pipelines which would have been very time consuming and caused additional headaches and due to its columnar database design, all of my complex query requests are processed in a timely manner which means I do not fall asleep while waiting for results.

**What do you dislike about dbt?**

Vacuuming Tables… seriously, I have to manually vacuum and analyze tables to keep this thing running smoothly? It looks like 2005. Managing the clusters and nodes is also a pain – it’s not true serverless. If you’re not paying close attention to the costs, they will jump up way too high.

**What problems is dbt solving and how is that benefiting you?**

This has allowed us to move away from a pandas-based reporting solution, which crashed consistently. We now can process billions of records from our retail business and have a working dashboard. Its architecture provides separate storage and compute, allowing us to scale our compute resources as much as needed based on the demand for reports by management. Most importantly, it has reduced the amount of yelling from our data team regarding slow query performance.

  ### 3. Reliable transformation practices at scale

**Rating:** 4.0/5.0 stars

**Reviewed by:** Scott J. | Manager, data engineering and analytics, Logistics and Supply Chain, Small-Business (50 or fewer emp.)

**Reviewed Date:** January 26, 2026

**What do you like best about dbt?**

One thing that I find impressive about dbt is that it promotes discipline in writing of transformations. It transformed my approach towards the way I deal with my work, as I now think twice before imposing changes. I use it on a regular basis, and it has enhanced teamwork since logic has less difficulty in reviewing and discussion. This has saved time on quick fixes and has assisted us in developing more confidence on outputs that may be shared.

**What do you dislike about dbt?**

What I do not like about dbt is that there is a huge effect of little errors in the models. Some of them may break down under the pressure of having a few downstream pieces broken when there is a slight change. It is time consuming and can even bring several individuals into the same problem when it comes to debugging those chains. In my case, this retards progress and results in context switching which can be annoying when time lines are near.

**What problems is dbt solving and how is that benefiting you?**

Dbt eliminates the issue of vague ownership and reasoning. It provides organization where responsibilities are clearly seen which enhances cooperation. In my case, it implies a reduced number of handoff problems and a streamlined collaboration. Co-workers become bolder in changes and tasks are less responsive on a daily basis. It has simplified our working process and made it more predictable in general.

  ### 4. dbt keeps our data models clean, consistent and version controlled

**Rating:** 4.0/5.0 stars

**Reviewed by:** Bhupendra S. | Senior Team lead, Mid-Market (51-1000 emp.)

**Reviewed Date:** November 03, 2025

**What do you like best about dbt?**

I use dbt every day to transform raw data in our warehouse into clean, analytics ready tables and my workflow typically begins in VS Code, where I write sql models, then push them to Git for version control and run them through dbt Cloud. And overall it has also made collaboration between our team members much easier because dbt makes the whole process much more simpler.

**What do you dislike about dbt?**

It's challenging when one change throws an entire run off track and the error messages are at best, vague. I also feel the need to defend is the handiwork of my contributor to dbt cloud. I have also encountered the overly relaxed strucure and the resulting chaotic command and environment specific configurations.

**What problems is dbt solving and how is that benefiting you?**

I can onboard people more easily, which has dramatically increased the usage of my warehouse and decreased my reliance on fragile, one off sql scripts and we have a whole team of analysts, engineers, and product working to have the same versioned models just building and ready for use.

  ### 5. Makes Transforming and Managing Data Models Way More Manageable

**Rating:** 4.0/5.0 stars

**Reviewed by:** Alexander V. | DevOps Engineer, Mid-Market (51-1000 emp.)

**Reviewed Date:** October 12, 2025

**What do you like best about dbt?**

Thanks to dbt, I no longer have to depend on the engineering team to manage and transform the SQL data within our warehouse. It is the first step for me in organizing, testing, and documenting the entirety of our data models. I appreciate that all of this information is in one place in version control. I can track all changes made and the details surrounding each one.

**What do you dislike about dbt?**

Troubleshooting complex dependencies and build errors can be a daunting task. There are occasions when a model fails and it is unclear which upstream change might be the cause. While the documentation is really good, I have found digging into a Stack Overflow or Slack thread to be the answer for some of the more obscure problems. I also find the visualization of lineage in dbt Cloud to be cumbersome.

**What problems is dbt solving and how is that benefiting you?**

Data transformations are far more efficient now with dbt. I no longer need to create custom scripts or deal with disorganized SQL in dashboards, as I can now have a single layer that is testable and maintained for all my transformations. It is quick and dependable to run models in dbt Cloud, which assures me that the data is consistent and current for our business teams.

  ### 6. Structured data workflows made effortless with dbt

**Rating:** 4.5/5.0 stars

**Reviewed by:** Josh K. | Analytics engineering lead, Architecture & Planning, Small-Business (50 or fewer emp.)

**Reviewed Date:** December 21, 2025

**What do you like best about dbt?**

The largest benefit of dbt to me is that it provides structure to data work. I use it regularly with the BigQuery and version control tools. The integration is comfortable and teamwork is facilitated. It did not add any delay during implementation and the feature set enables one to reuse logic rather than rewriting it. It has minimized the number of errors and saved me time on the review and updates.

**What do you dislike about dbt?**

The negative side about dbt is that it becomes rigid when projects expand. Minor modifications in some cases need more readjustments than anticipated, and this makes me slow down. The problems of debugging failures are not always evident, particularly to more novice team members and this has an impact on the speed of delivery. Clean source data is also used in implementation and hence when inputs are messy, it only adds more workload rather than making it easy.

**What problems is dbt solving and how is that benefiting you?**

Before using dbt, our changes were far between and difficult to handle. At this point, all things go in the same way, which is advantageous to the entire team. The coordination between systems was eliminated through integration and implementation provided a sense of ownership. I can perceive fewer errors, more harmonious work, and a higher level of trust in products. It has made daily work less stressful and less value building oriented.

  ### 7. We finally found a solution for easier management of data models

**Rating:** 4.5/5.0 stars

**Reviewed by:** James M. | Business Intelligence Developer, Information Technology and Services, Small-Business (50 or fewer emp.)

**Reviewed Date:** December 08, 2025

**What do you like best about dbt?**

The interesting fact about dbt is that it simplifies the process of managing data pipelines. It was implemented successfully and I depend on it on a daily basis and hence my frequency of use is high. The amount of features such as model testing, documentation, and version control is especially appreciated by me. It has minimized errors in our conversion processes and has simplified the process of teamwork a lot and has helped the team maintain pipelines which are uniform and structured across projects.

**What do you dislike about dbt?**

The thing I dislike with dbt is that it may be difficult to troubleshoot model errors. The features are good, and error messages are not always helpful in disclosing the problem. High frequency of use implies that such moments have the capacity of derailing workflows since I use it frequently. There is responsive customer support but edge-case fixes are not always immediately available, so the team occasionally has to check outputs before proceeding.

**What problems is dbt solving and how is that benefiting you?**

Dbt has resolved the problem of inaccurate or inconsistent transformations within our workflows. It has simple implementation and I use it frequently hence my usage frequency is also high. It has many features that can be used to test and keep track of the version that helps in uncovering errors at the earlier stages. It is lean cooperation throughout the team, reduced manual checks that have to be done multiple times, and ensures our data is reliable and can be used in reporting and business decisions.

  ### 8. Reliable data project workflow

**Rating:** 4.0/5.0 stars

**Reviewed by:** Elia M. | Data transformation engineer, Accounting, Small-Business (50 or fewer emp.)

**Reviewed Date:** November 30, 2025

**What do you like best about dbt?**

What I like about dbt most is that my modeling work is much more comfortable. I spend my entire time modifying logic or verifying the changes and the set of features is what I actually need. I operate it with Snowflake, and that integration ensures that my updates are regular. The installation was relatively fast and the number of times the tool has been used demonstrates the extent to which it has come to my rescue in order to maintain projects and ensure they are well organized.

**What do you dislike about dbt?**

The thing I do not like is that there are some spots that do not provide me with the flexibility that I need when I work on large portions of work. It imposes additional procedures which disrupt my rhythm. These weak spots are visible since I am in dbt so many times. There is a good response of the customer support but still the restrictions influence my speed during peak weeks.

**What problems is dbt solving and how is that benefiting you?**

Dbt addresses our problem of disorganized model upkeep in the team. I apply it in my daily activities keeping track of the changes and updating and ensuring that all goes in the right direction. The size of the features suits very well into our workflow and the frequency of use demonstrates how much easier our process is now. It has assisted us in preventing the instances of miscommunication and enabled all of us to be more certain of the work we drive forward.

  ### 9. If its worth it, a data transformer with amazing features

**Rating:** 4.5/5.0 stars

**Reviewed by:** Ali A. | Analytics Engineer / BI Analyst, Mid-Market (51-1000 emp.)

**Reviewed Date:** October 01, 2025

**What do you like best about dbt?**

I appreciate how this tool brings software engineering principles to our data collection process, making it more scalable, auditable, and reliable. I also really enjoy the ability to write straightforward tests that execute automatically.

**What do you dislike about dbt?**

At times, handling very complex transformations or preprocessing tasks requires the use of more advanced Python packages, which means I often need to rely on external solutions.

**What problems is dbt solving and how is that benefiting you?**

At this stage, we are able to use it to reliably scale our business metrics, which provides us with greater speed and is transforming our data stack due to its robust operations and data preparation features. Integration with DBT is seamless, making the entire process smooth.

  ### 10. DBT at my point of view

**Rating:** 4.0/5.0 stars

**Reviewed by:** Anandhakumar R. | Data Engineer, Mid-Market (51-1000 emp.)

**Reviewed Date:** May 28, 2025

**What do you like best about dbt?**

Data transformation logic can be expressed in SQL
Data can be transformed in batch
Ease to use, it has lot of good features similar in Django web application

**What do you dislike about dbt?**

Joining multiple database types is not possible. Ie., combining two databases like oracle and mysql.
Persistent cluster is required for running the sql statements.
Like Presto/Hive it can’t be connected to BI Directly

**What problems is dbt solving and how is that benefiting you?**

Enables engineers to transform data in their data lakehouse using select statements in SQL. 
Converts SQL select statements into Tables or Views
Supports DW process such as incremental, SCD etc.,
Graphical representation of pipelines

  ### 11. User-Friendly Data Modeling with Seamless Integration

**Rating:** 5.0/5.0 stars

**Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.)

**Reviewed Date:** December 09, 2025

**What do you like best about dbt?**

DBT is great for organizing data models. It is user friendly, integrates well with other tools, and they had a great onboarding process.

**What do you dislike about dbt?**

In dbt Cloud, I cant work on two different branches at the same time in different browsers.

**What problems is dbt solving and how is that benefiting you?**

DBT allows us to take raw data from many sources and output it in clean, easy to use output tables that are used in our bi tool.

  ### 12. We majorly use DBT cloud for ETL in the organization

**Rating:** 4.5/5.0 stars

**Reviewed by:** Sushanth U. | Tableau Developer and Admin, Information Technology and Services, Enterprise (> 1000 emp.)

**Reviewed Date:** May 16, 2025

**What do you like best about dbt?**

It's simple, SQL based approach and easy to version control.

**What do you dislike about dbt?**

Challenging to manage large scale and complex dependencies

**What problems is dbt solving and how is that benefiting you?**

Since it's SQL based, its easy to manage ETL pipeline.


## dbt Discussions
  - [What is DBT data Modelling?](https://www.g2.com/discussions/what-is-dbt-data-modelling) - 2 comments
  - [What is DBT technology?](https://www.g2.com/discussions/what-is-dbt-technology) - 2 comments
  - [What is DBT database tool?](https://www.g2.com/discussions/what-is-dbt-database-tool) - 1 comment
  - [What is DBT tool used for?](https://www.g2.com/discussions/what-is-dbt-tool-used-for) - 2 comments

- [View dbt pricing details and edition comparison](https://www.g2.com/products/dbt/reviews?filters%5Bsentiment_snippet%5D=1932889&qs=pros-and-cons&section=pricing&secure%5Bexpires_at%5D=2026-08-14+18%3A25%3A47+-0500&secure%5Bsession_id%5D=766596ce-5f4e-4fbc-b723-684b412af446&secure%5Btoken%5D=b8ddf0fecea379840e4afd2c77dbd17b708148a985034fcf1ad6c4b9b98d38a5&format=llm_user)
## dbt Integrations
  - [Amazon EC2](https://www.g2.com/products/amazon-ec2/reviews)
  - [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews)
  - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews)
  - [Fivetran](https://www.g2.com/products/fivetran/reviews)
  - [GitHub](https://www.g2.com/products/github/reviews)
  - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)
  - [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews)
  - [PostgreSQL](https://www.g2.com/products/postgresql/reviews)
  - [Snowflake](https://www.g2.com/products/snowflake/reviews)
  - [Spark](https://www.g2.com/products/apache-spark/reviews)

## dbt Features
**Reports**
- Reports Interface
- Steps to Answer
- Graphs and Charts
- Score Cards
- Dashboards
- Customizable Reports
- Marketing Reports
- Sales Reports
- Activity Dashboard
- Interactive Reports
- Customizable Reports
- Customizable Reports
- Activity Dashboard
- Customizable Dashboard

**Data Source Access**
- Breadth of Data Sources
- Ease of Data Connectivity
- API Connectivity

**Management**
- Auditing

**Data Management**
- Data Integration
- Metadata
- Self-service
- Automated workflows

**Automation**
- Workflow Automation
- Multi-platform support
- Data Management

**Agentic AI - DataOps Platforms**
- Autonomous Task Execution
- Multi-step Planning
- Cross-system Integration
- Adaptive Learning
- Decision Making

**Deployment & Integration - Semantic Layer Tools**
- Multi-Environment & Multi-Cloud Support
- Open API & SDK Integration

**Data Transformation**
- Real-Time Analytics
- Data Querying
- Reporting/Analytics
- Predictive Analytics
- Visual Analytics

**Data Interaction**
- Profiling and Classification
- Metadata Management
- Data Modeling
- Data Joining
- Data Blending
- Data Quality and Cleansing
- Data Sharing
- Data Governance

**Functionality**
- Transformation
- Automation
- Scalability
- Non-Relational Transformations
- Data Extraction

**Analytics**
- Analytics capabilities
- Dasboard visualizations

**Functionality**
- Documentation management
- Platform support
- Template functionality

**Data Connectivity & Federation - Semantic Layer Tools**
- Cross-Source Query Federation
- Dynamic Schema & Metadata Adaptation

**Additional Functionality**
- Data Mapping
- Monitoring
- Charting
- Integration Management
- Reporting/Analytics
- Ad hoc Analysis
- Access Controls/Permissions
- API
- Match & Merge
- Real-Time Monitoring
- Metadata Management
- Pipeline Management
- Job Scheduling
- Dashboard Creation
- Data Storage Management
- Multiple Data Sources
- Data Import/Export
- Generative AI
- Data Quality Control
- Data Connectors
- Customizable Reports
- Single Sign On
- Version Control
- Visual Analytics
- Accounting Integration
- Real-Time Data
- eCommerce Management
- AI Copilot
- CRM
- Data Visualization
- SSL Security
- Search/Filter
- Real-Time Analytics
- Data Capture and Transfer
- Collaboration Tools
- Performance Management
- Data Synchronization
- Drag & Drop
- Data Replication
- Activity Dashboard
- Database Support
- Workflow Management
- Alerts/Notifications
- Predictive Analytics
- Data Migration
- Third-Party Integrations
- Reporting & Statistics
- Data Analysis Tools

**Data Exporting**
- Breadth of Integrations
- Ease of Integrations
- Data Workflows

**Monitoring and Management**
- Data Observability
- Testing capabilities

**Administration**
- Error Alerts
- Service Automation
- Workflow management

**Data Modeling & Metrics - Semantic Layer Tools**
- Derived & Calculated Metrics
- Time Intelligence Functions

**Connectivity**
- Hadoop Integration
- Spark Integration
- Multi-Source Analysis
- Data Lake
- Real-Time Data
- Data Capture and Transfer
- Trend Analysis
- What-if Analysis
- Statistical Analysis
- Data Blending
- Ad hoc Analysis
- Third-Party Integrations

**Functionality**
- Identification
- Correction
- Normalization
- Preventative Cleaning
- Data Matching
- Real-Time Data

**Cloud Deployment**
- Hybrid cloud support
- Cloud migration capabilities

**Generative AI**
- AI Text Generation
- Generative AI

**Agentic AI - Data Warehouse Automation**
- Proactive Assistance

**Performance Optimization - Semantic Layer Tools**
- Query Caching & Acceleration
- Adaptive Query Optimization

**Additional Functionality**
- Data Analysis Tools
- AI/Machine Learning
- Data Extraction
- Reporting/Analytics
- AI Copilot
- Data Mapping
- ETL
- Big Data Analytics
- Data Quality Control

**Additional Functionality**
- Parallel Processing
- Ad hoc Analysis
- Multiple Data Sources
- API
- In-Database Processing
- Monitoring
- Real-Time Reporting
- Data Synchronization
- Real-Time Monitoring
- Data Connectors
- Performance Metrics
- Ad hoc Reporting
- Alerts/Notifications
- Access Controls/Permissions
- Drag & Drop
- Data Visualization
- Data Import/Export
- Match & Merge
- Data Transformation
- Secure Data Storage
- Data Extraction
- AI Copilot
- Customizable Reports
- Activity Dashboard
- Data Migration
- In-Memory Processing
- Data Mapping

**Self Service **
- Calculated Fields
- Data Column Filtering
- Data Discovery
- Search
- Collaboration / Workflow
- Automodeling
- Natural Language Search
- Visual Discovery
- Data Blending
- Data Blending

**Operations**
- Data Workflow
- Governed Discovery
- Embedded Analytics
- Notebooks
- Data Discovery

**Management**
- Reporting
- Automation
- Quality Audits
- Dashboard
- Governance

**Generative AI**
- AI Text Generation
- AI Text Summarization

**Governance - Semantic Layer Tools**
- AI Governance & Observability
- Metric Lineage for AI Training Data
- Version Control & Change Impact Analysis

**Additional Functionality**
- Data Warehousing
- Drag & Drop
- Activity Dashboard
- Data Connectors
- Ad hoc Reporting
- Customizable Reports
- Alerts/Escalation
- Templates
- Forecasting
- Data Migration
- Data Transformation
- Access Controls/Permissions
- API
- Data Cleansing
- Data Synchronization
- AI Copilot
- Dashboard Creation
- Data Extraction
- Data Security
- SSL Security
- Collaboration Tools
- No-Code
- High Volume Processing
- Database Support
- Search/Filter
- Generative AI

**Advanced Analytics**
- Predictive Analytics
- Data Visualization
- Big Data Services
- Real-Time Analytics
- Reporting/Analytics
- Real-Time Analytics
- Reporting/Analytics
- Visual Analytics

**Generative AI**
- AI Text Generation
- AI Text Summarization
- Generative AI

**Advanced Intelligence - Semantic Layer Tools**
- Natural Language Query Interface
- Semantic Layer for AI/ML Models
- Recommendation Engine

**Additional Functionality**
- Metadata Management
- Collaboration Tools
- Search/Filter
- Workflow Management
- AI Copilot
- Third-Party Integrations
- Data Synchronization
- Data Import/Export
- Customizable Rules
- Master Data Management
- Monitoring
- Data Transformation
- Multiple Data Sources
- Self Service Portal
- Customer Database
- Data Verification
- Data Migration
- Multi-Language
- Single Sign On
- Duplicate Detection
- Email Address Extraction
- Reporting/Analytics
- Data Profiling
- Data Extraction
- Data Mapping
- Address Validation
- Match & Merge
- Performance Metrics
- Visual Analytics
- Version Control
- API
- Data Capture and Transfer
- Access Controls/Permissions
- Compliance Management
- Data Discovery

**Agentic AI Enablement - Semantic Layer Tools**
- Agentic Query Orchestration
- Contextual Reasoning Layer
- Workflow Automation via Semantic Agents

**Building Reports**
- Data Transformation
- Data Modeling
- Integration APIs
- Real-Time Data
- Real-Time Data
- Third-Party Integrations
- Third-Party Integrations

**Platform**
- Mobile User Support
- Customization 
- User, Role, and Access Management
- Internationalization
- Sandbox / Test Environments
- Performance and Reliability
- Breadth of Partner Applications
- Mobile Access
- Metadata Management

**Additional Functionality**
- Customizable Branding
- Natural Language Processing
- Data Extraction
- AI Copilot
- Ad hoc Reporting
- Real-Time Reporting
- Publishing/Sharing
- Collaboration Tools
- Strategic Planning
- Self Service Data Preparation
- Trend Analysis
- Text Analysis
- Real-Time Monitoring
- Data Synchronization
- Widgets
- Benchmarking
- Performance Metrics
- Data Mapping
- Generative AI
- Trend/Problem Indicators
- Customizable Templates
- Access Controls/Permissions
- Data Import/Export
- Profitability Analysis
- OLAP
- Alerts/Notifications
- Search/Filter
- Drag & Drop
- Data Connectors
- Multiple Data Sources
- Key Performance Indicators
- Dashboard Creation
- Role-Based Permissions
- Data Mining
- Forecasting
- Ad hoc Query

## Top dbt Alternatives
  - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (1,337 reviews)
  - [Alteryx](https://www.g2.com/products/alteryx/reviews) - 4.6/5.0 (861 reviews)
  - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - 4.5/5.0 (1,145 reviews)