TrustRadius Insights for dbt are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
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.
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dbt Reviews
2 Reviews
Professional, Scientific, and Technical ServicesInformation Technology & Services1Market Research1
At [...], dbt (Data Build Tool) is used for data transformation in the ELT processes. As [...] is a data rich company, there are lot of instances where the data needs to be transformed after it is loaded into the data warehouse and dbt handles this perfectly. dbt helps our company to maintain data quality with its transformation capabilities using the SQL queries.
Pros
dbt supports version control through GIT, this allows teams to collaborate and track the data transformation logic.
dbt allows us to build data models which helps to break complex transformation logic into simple and smaller logic.
dbt is completely based on SQL which allows data analyst and data engineers to build the transformation logic.
dbt can be easily integrated with snowflake.
Cons
dbt can improve their debugging and error messaging.
dbt does not support python based transformation which are needed in advanced cases like machine learning.
dbt should provide the feature of query cost estimation and usage reports to reduce high compute cost.
Likelihood to Recommend
dbt (Data Build Tool) is best suited for doing the data transformation. dbt is just a transformation tool and it is not suitable for building a data pipeline which requires extraction of data and loading. dbt is well suited for SQL based transformation logic and it is less appropriate when transformation logic requires python.
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
Pros
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
Cons
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)
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.