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
Engineering
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DBT is essential to our data strategy. We use it on a day-to-day basis in order to transform our data layer to solve & answer key business questions. It allows us to clean & deliver high-quality data to our internal reports & dashboards. The continuous integration feature of DBT also allows us to manage deployments in various environments while still allowing our engineering team to work on separate projects at the same time.
Pros
Transform data
Allow for development in your data layer
Provide easy-to-deploy tests to ensure high data quality
Cons
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
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.
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.
