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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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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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Popular Features
- Complex transformations (5)9.999%
- Simple transformations (5)9.595%
- Data model creation (5)9.191%
- Metadata management (5)8.686%
Pricing
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…
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MFMS DBT IN FERTILIZER WEB VERSION FULL! SETUP PROCESS ON WINDOW 7!LIVE DEMO
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DBT: Powerful, Open Source Data Transformations | Fishtown Analytics / DBT
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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 Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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.
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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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!
- Business Problems Solved
- Pros
- Cons
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)Companies can't remove reviews or game the system. Here's why
September 21, 2022
dbt is THE way to manage your SQL transformations - version control and all kinds of other tools!
- SQL Transformation
- Data pipeline management
- SQL data warehouse management
- Slow load times of the dbt cloud environment (they're working on it via a new UI though)
- More out-of-the-box solutions for managing procedures, functions, etc would be nice to have, but honestly, it's pretty easy to figure out how to adapt dbt macros
September 16, 2022
dbt - an excellent transformation tool for the masses
- Text based integration with github - it's very easy to see changes to code over time.
- Leverages SQL which makes it a fast learning curve for most developers.
- Removes complexity of deployment to multiple environments.
- Adds powerful templating, making dynamic sql easy.
- Data lineage and documentation.
- Easy to add automated testing for data quality.
- Easy to switch output between tables and views by setting a flag.
- Excellent documentation, slack app, training, and support.
- Packages (libraries) exist with helpful code readily available.
- Failsafe - dbt core is open source so our investment in code is sound even if they hike the prices.
- Field-level lineage (currently at table level)
- Documentation inheritance - if a field is documented the downstream field of the same name could inherit the doc info
- Adding python model support (in beta now)
September 16, 2022
DBT is King in the world of data
- Transform data
- Allow for development in your data layer
- Provide easy-to-deploy tests to ensure high data quality
- Some of the packages available for use are limited in functionality
- Multiple projects can be difficult to handle
- Multiple environments can be difficult to manage
- Transformation
- Change Control
- Organisation
- Flow build
- Learning Curve is steep
- Better documentation
- Better YouTube tutorials
May 26, 2022
when implemented with efficiency and care, dbt helps creating reusable and accessible data models
Score 9 out of 10
Vetted Review
Verified User
- 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)
October 22, 2021
Easy to use
- Transforms data for easy and quick reporting
- Optimizes report speed and performance
- Able to schedule
- Ability to trigger alerts when a job fails
April 12, 2021
dbt and the Modern Data Stack
- Model creation and management.
- Deployments and CI/CD.
- Automatically generates documentation.
- Deployment and sharing of generated documentation outside of dbt cloud could be simplified.
- Make the artifacts generated by dbt easier to consume (build log, test results, manifest) for use in analytics and ops.