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dbt

dbt

Overview

What is dbt?

dbt is an SQL development environment, developed by Fishtown Analytics, now known as dbt Labs. The vendor states that with dbt, analysts take ownership of the entire analytics engineering workflow, from writing data transformation code to deployment and documentation. dbt…

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Recent Reviews

TrustRadius Insights

Users of dbt have found several key use cases for this powerful data transformation tool. With dbt, users are able to easily transform …
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Popular Features

View all 7 features
  • Complex transformations (5)
    9.9
    99%
  • Simple transformations (5)
    9.5
    95%
  • Data model creation (5)
    9.1
    91%
  • Metadata management (5)
    8.6
    86%
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Pricing

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What is dbt?

dbt is an SQL development environment, developed by Fishtown Analytics, now known as dbt Labs. The vendor states that with dbt, analysts take ownership of the entire analytics engineering workflow, from writing data transformation code to deployment and documentation. dbt Core is distributed under…

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://www.getdbt.com/pricing

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Demos

MFMS DBT IN FERTILIZER WEB VERSION FULL! SETUP PROCESS ON WINDOW 7!LIVE DEMO

YouTube

CenturionPro Dry Batch Trimmer Model 2 - Intro & Demo

YouTube

DBT: Powerful, Open Source Data Transformations | Fishtown Analytics / DBT

YouTube
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Features

Data Transformations

Data transformations include calculations, search and replace, data normalization and data parsing

9.7
Avg 8.4

Data Modeling

A data model is a diagram or flowchart that illustrates the relationships between data

9
Avg 8.1
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Product Details

What is dbt?

dbt is a development framework that lets analysts and engineers collaborate on transformation workflows using their shared knowledge of SQL. Through the application of software engineering best practices like modularity, version control, testing, and documentation, dbt’s analytics engineering workflow helps teams work faster and more efficiently to produce data the entire organization can trust.

dbt Core is an open source command line framework that enables data teams to transform data following analytics engineering best practices.

dbt Cloud is presented as the fastest and most reliable way to deploy dbt. dbt Cloud provides a centralized development experience to safely deploy, monitor, and investigate transformation code in a web-based UI.

dbt Features

Data Transformations Features

  • Supported: Simple transformations
  • Supported: Complex transformations

Data Modeling Features

  • Supported: Data model creation
  • Supported: Metadata management
  • Supported: Business rules and workflow
  • Supported: Collaboration
  • Supported: Testing and debugging

dbt Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

dbt is an SQL development environment, developed by Fishtown Analytics, now known as dbt Labs. The vendor states that with dbt, analysts take ownership of the entire analytics engineering workflow, from writing data transformation code to deployment and documentation. dbt Core is distributed under the Apache 2.0 license, and paid Teams and Enterprise editions are available.

dbt starts at $0.

Matillion, Dataform, and Informatica Cloud Data Integration are common alternatives for dbt.

Reviewers rate Complex transformations highest, with a score of 9.9.

The most common users of dbt are from Small Businesses (1-50 employees).
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Comparisons

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Reviews and Ratings

(43)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Users of dbt have found several key use cases for this powerful data transformation tool. With dbt, users are able to easily transform source data into meaningful report data, providing valuable insights to management and empowering them to make informed decisions. By integrating with other tools like Fivetran and Snowflake data warehouse, dbt allows for efficient data landing, transformation, and utilization.

One of the major benefits of using dbt is the ability to open up data modeling to non-data engineering teams. Previously, teams had to wait for Airflow changes, causing bottlenecks and slowing down the data model change process. However, with dbt, data engineering teams can enable other teams to take part in data modeling without having to rely on Airflow changes. This has resulted in faster data model changes and reduced waiting times.

BI and Analytics teams have also found great value in dbt for creating and managing models in the data warehouse. These models serve as the foundation for LOB reporting, allowing teams to generate accurate reports that support informed decision-making. Additionally, by leveraging dbt for the transformation part of their ETL process, users ensure accurate data and efficient change control.

By using dbt in their data pipelines, users are able to adapt all their data transformations easily. This flexibility leads to increased productivity, improved accuracy of results, and fewer introduced bugs. Furthermore, dbt plays a crucial role in the overall data strategy by enabling users to transform the data layer to solve key business questions.

The continuous integration feature of dbt also deserves mention as it allows users to manage deployments in various environments while enabling engineering teams to work on separate projects simultaneously. This ensures smooth coordination among different teams and facilitates seamless development and deployment processes.

Overall, users have found dbt to be an invaluable tool for transforming data, generating meaningful insights, facilitating collaboration among different teams, and supporting informed decision-making across the organization.

Efficient Deployment Process: Many users have praised dbt for simplifying the complexity of deploying to multiple environments. This streamlines the deployment process and saves time for developers, making it easier to manage data transformations across different stages.

Powerful Templating Feature: Users appreciate dbt's powerful templating feature, which allows them to effortlessly write dynamic SQL. This enables them to easily modify and customize queries as needed, providing flexibility in their data transformations.

Excellent Documentation and Support: A common sentiment among reviewers is the availability of excellent documentation and support from both the customer success team and the dbt community. This comprehensive documentation helps users understand and navigate various features, including model creation, deployments, CI/CD, and automatically generating documentation. The presence of a Slack app, training resources, and timely assistance from the customer success team further enhances the user experience with dbt.

Limited field-level lineage: Some users have expressed that the field-level lineage feature in dbt is currently limited to table level, suggesting a need for more granular documentation inheritance.

Lack of customization options for incremental models: Several reviewers have mentioned the need for increased customization options for incremental models in order to handle larger data sets more effectively.

Difficulty managing multiple projects and environments: A number of users have found it challenging to handle multiple projects and manage multiple environments in dbt, indicating a need for improved functionality in this area.

Reviews

(1-2 of 2)
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Obed Espina | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I'll quickly summarize one pain point. We have data transformation jobs (SQL-only) written in Airflow, and often an analyst teammate had most of the business context. However, there is a higher barrier to entry to jump into Airflow-based development, so data engineering was becoming the bottleneck to data model changes. By introducing dbt (data build tool) along with support from data engineering, we were able to open up data modeling to other teams without having to wait for Airflow changes. This helps because these teams have the business context for that data model and are best equipped to make those changes. There is more detail at this public blog post: https://medium.com/vimeo-engineering-blog/dbt-development-at-vimeo-fe1ad9eb212
  • user experience makes it easy to work with SQL and version control
  • customer success team and the dbt (data build tool) community help establish best practices
  • thorough and clear documentation
  • increased customization for incremental models to support larger data sets
  • suggestions for project structure to fit legacy models (e.g. a legacy table built by another ETL)
dbt (data build tool) has the capability to make your data models more accessible; other teams can read documentation, follow along the lineage, and even collaborate to make changes themselves dbt (data build tool) also has the capability to easily increase your database cost and write complex data models. The key to mitigating this risk is to adhere to best practices from the community and within your organization. Look to your data engineering teams to help guide scalable and efficient dbt (data build tool) processes and listen to your analysts for building well-documented and reusable data models.
  • browser-based UI to simplify development
  • configurable and cloud-based Jobs
  • dbt's flexibility with fitting to the our organizations' data standards
Data Source Connection
N/A
N/A
Data Transformations (2)
90%
9.0
Simple transformations
90%
9.0
Complex transformations
90%
9.0
Data Modeling (5)
82%
8.2
Data model creation
90%
9.0
Metadata management
90%
9.0
Business rules and workflow
60%
6.0
Collaboration
90%
9.0
Testing and debugging
80%
8.0
Data Governance
N/A
N/A
  • increased data model commits by non-data engineering teams
  • clearer project and data model structures guided by business domains
Airflow can accomplish the same work as dbt (data build tool), however, dbt's (data build tool) development workflow and UI can open up data transformation and modeling work to non-data engineering teams. Looker might also be able to define data models via LookML with a version control-like workflow. However, there's an added step here to learn LookML, compared to dbt (data build tool) which primarily relies on SQL (note: there is also Jinja so you could argue that you need to learn this too).
October 22, 2021

Easy to use

Score 8 out of 10
Vetted Review
Verified User
Incentivized
Data loaded in Snowflake (data warehouse) is transformed using dbt
  • Transforms data for easy and quick reporting
  • Optimizes report speed and performance
  • Able to schedule
  • Ability to trigger alerts when a job fails
It is great for transforming data already loaded into a cloud data warehouse
  • SQL and Jinja to transform data
  • deployment abilities to schedule load
  • Testing features to test the quality of data
  • Increased customer satisfaction as reports load faster because of preprocessing done in dbt
  • Data Analysts are able to transform data using SQL and Jinja so there's no need to wait for the data engineer
dbt is great because of its transformation capabilities
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