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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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TrustRadius Insights
TrustRadius Insights
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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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dbt

dbt

Free

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 Integrations

dbt Competitors

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 Mid-sized Companies (51-1,000 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

(1-7 of 7)
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September 21, 2022

dbt is THE way to manage your SQL transformations - version control and all kinds of other tools!

Verified User
Analyst in Information Technology
Hospital & Health Care Company, 201-500 employees
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Dbt is a revelation for the analytics/BI engineering space. The seamless version control and 'don't repeat yourself' tools make your data and analytics pipelines WAY more reliable and efficient. Build data like a developer!
September 16, 2022

dbt - an excellent transformation tool for the masses

Judy Campion | TrustRadius Reviewer
Judy Campion
Data Architect
UserTesting (Information Services, 501-1000 employees)
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
If you can load your data first into your warehouse, dbt is excellent. It does the T(ransformation) part of ELT brilliantly but does not do the E(xtract) or L(oad) part. If you know SQL or your development team knows SQL, it's a framework and extension around that. So, it's easy to learn and easy to hire people with that technical skill (as opposed to specific Informatica, SnapLogic, etc. experience). dbt uses plain text files and integrates with GitHub. You can easily see the changes made between versions. In GUI-based UIs it was always hard to tell what someone had changed. Each "model" is essentially a "SELECT" statement. You never need to do a "CREATE TABLE" or "CREATE VIEW" - it's all done for you, leaving you to work on the business logic. Instead of saying "FROM specific_db.schema.table" you indicate "FROM ref('my_other_model')". It creates an internal dependency diagram you can view in a DAG. When you deploy, the dependencies work like magic in your various environments. They also have great documentation, an active slack community, training, and support. I like the enhancements they have been making and I believe they are headed in a good direction.
September 16, 2022

DBT is King in the world of data

Verified User
Engineer in Engineering
Health, Wellness and Fitness Company, 11-50 employees
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
DBT seems to work well when your data needs arise from your production environment. The IDE allows for integration with a GitHub repository but the current setup makes it a little complicated if you need to develop in other environments for system integration & user acceptance testing. However, the tool does perform its duties well & works with current modern tools such as Snowflake.
June 24, 2022

dbt - bang for your buck for transformation in ETL stack

YP
Yoshlin Pillay
Senior Data Entry
Kandua (Construction, 1-10 employees)
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
I use dbt on the T part of ETL in my data tech stack. It is amazing and also does the change control quite well. So, to summarize - ingest, do a multitude of transforms, spit out to data mart. Allows various business logic applications to data to happen simultaneously and well tracked for the data marts. Not very well suited so a single data mart, low scale data volume.
May 26, 2022

when implemented with efficiency and care, dbt helps creating reusable and accessible data models

Obed Espina | TrustRadius Reviewer
Obed Espina
Senior Data Engineer
Vimeo (Information Technology & Services, 1001-5000 employees)
Score 9 out of 10
Vetted Review
Verified User
Likelihood to Recommend
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.
April 12, 2021

dbt and the Modern Data Stack

DD
Daniel Denunzio
Data Architect
Precision Nutrition (Health, Wellness and Fitness, 51-200 employees)
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Very well suited for Data Engineering and Analytics Engineering. Works very well in modern data stacks with other tools such as Fivetran and Snowflake. Not appropriate for teams with little SQL experience or who require no-code analytic/engineering options.
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