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.
We used dbt to transform source data into data tables, push these data tables into our data warehouse, establish sources of truth for data, track data lineage / dependencies / downstream impacts, and as a source of truth for business logic (metric definitions, what data can be used for what, and so forth).
Pros
Easy to create data tables (analysts can do what used to require engineer)
Documentation & lineage is built in
It can run tests to make sure data is being transformed correctly
Cons
You have to use a separate tool for job orchestration
You need a separate tool for ingestion
Table optimization work is manual (no automations there)
Likelihood to Recommend
dbt is really good for certain more mature data organizations that are primarily doing SQL based analytics. If your organization is younger (you don't haver very many data tables you need to manager), dbt is probably overkill. If the ways you use data are more complex (e.g. primarily data science using python) then dbt may not be the most efficient platform for your workflow.
We use dbt to manage all the data transformation logic in our data warehouse, all the way from raw data to modeled data ready for analysis. This allows us to harmonize and clean our data and create models combining data from multiple sources. Our scope contains billing and payment data, CRM data, marketing, and lead pipeline data, etc.
Pros
Automation
Version control.
Automated generation of lineage graphs.
Cons
Tried hard, but cannot think of anything.
Likelihood to Recommend
The prerequisite is that you have a supported database/data warehouse and have already found a way to ingest your raw data. Then dbt is very well suited to manage your transformation logic if the people using it are familiar with SQL. If you want to benefit from bringing engineering practices to data, dbt is a great fit. It can bring CI/CD practices, version control, automated testing, documentation generation, etc. It is not so well suited if the people managing the transformation logic do not like to code (in SQL) but prefer graphical user interfaces.
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'm now adapting ALL my data transformations in my Fivetran -> Snowflake -> visualizations data pipelines using dbt. The productivity gains + better accuracy/fewer bugs introduced are HUGE. There's definitely some upfront work to learn dbt (not hard if you're already a SQL expert AND if you have a little git or coding familiarity), but man is it worth it!
Pros
SQL Transformation
Data pipeline management
SQL data warehouse management
Cons
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
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!
VU
Verified User
Analyst in Information Technology (201-500 employees)
We use dbt to transform source data into meaningful report data, so it can be easily consumed in dashboards, allowing our management insights and the ability to steer the company. We use Fivetran and other tools to land the data in our Snowflake data warehouse, and then dbt to transform and utilize that data.
Pros
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.
Cons
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)
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.
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 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.
Pros
Transformation
Change Control
Organisation
Flow build
Cons
Learning Curve is steep
Better documentation
Better YouTube tutorials
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.
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.
dbt is used by our BI and Analytics team to create and manage models in our data warehouse. The models created with dbt are used for LOB reporting.
Pros
Model creation and management.
Deployments and CI/CD.
Automatically generates documentation.
Cons
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.
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.
