dbt creates your analytics code and runs it on your data platform. This allows you and your team to work together on a single source of truth for metrics, insights and business definitions. This single source of truth, combined with the ability to define tests for your data, reduces errors when making logic changes and alerts you when difficulties arise.
dbt (data build tool) is an open-source tool that simplifies data transformation by allowing data analysts and data engineers to transform data by writing SQL statements that are then converted into tables and views.
dbt is a SQL-first transformation workflow that allows teams to deploy analytics code while following software development best practices such as modularity, portability, CI/CD and documentation quickly and collaboratively. With dbt, everyone on the data team can confidently contribute to production-ready data pipelines.
By combining modular SQL with software engineering best practices, dbt makes data transformation fast and reliable. With dbt, data analysts can write business logic over SQL, automate data quality tests, run the code and deliver data documentation along with the code. Given the shortage of data engineers, this is critical when dealing with big data.
dbt optimizes your workflow
With dbt, you avoid writing boilerplate DML and DDL by managing transactions, dropping tables and managing schema changes. Using dbt, you write business logic with an SQL select statement or a Python DataFrame that returns the record you need, and dbt takes care of the implementation.
Create reusable or modular data models with dbt that you can reference in subsequent work, rather than starting with the raw data for each analysis.
Dramatically reduce the time it takes to run your queries with dbt: use metadata to find long-running models you want to optimise and use incremental models that are easy to configure and use with dbt.
With dbt, you write less code by using macros, hooks and package management.
dbt enables more reliable analyses
With dbt, there’s no more copying and pasting SQL, which can lead to errors when logic changes. Instead, with dbt, create reusable data models that are included in subsequent models and analyses. Using dbt, just change a model once and the change is propagated to all its dependencies.
With dbt, publish the canonical version of a particular data model that includes all the complex business logic. All analyses built on this model include the same business logic without having to re-implement it.
With dbt, you use sophisticated version control processes such as branching, pull requests and code reviews.
Using dbt, you quickly and easily write data quality tests for the underlying data. Many analysis errors are caused by vulnerabilities in the data: dbt tests help analysts find and address these vulnerabilities.
Over 5,500 companies use dbt every week (12/2022).
This means that dbt has now become mainstream. But what exactly are the benefits of dbt for businesses?
Easier Data Transformation
With dbt, you can do all the work in SQL. dbt allows data analysts to write transformations using SELECT statements. This means that with dbt, boilerplate code is no longer required, and analysts can transform data even if they are not familiar with other programming languages.
With dbt, you can arrange all data transformations clearly in discrete data models. Each dbt model converts raw data into the target data set or acts as an intermediate step in the conversion process. dbt allows you to organize and materialize frequently used business logic in a collaborative, version-controlled and fast way.
dbt makes testing data integrity fairly effortless. Because dbt allows you to combine Jinja with SQL, you can turn your dbt project into a programming environment for SQL, which allows you to do things you normally can’t do in SQL (e.g. use control structures and environment variables). dbt also allows you to apply a test to a specific column by simply referencing it under the same YAML file.
Analytics as Code
Because dbt integrates with Git, any new code can be safely tested, reviewed, and documented before being integrated into the master branch. This means that the risk of accidentally overwriting or changing a production table when working on something new is much lower.
dbt cloud, a hosted service that helps bring dbt implementations into production, provides you with continuous integration. This allows for continuous deployment and less time spent testing. With dbt cloud, there is no longer a need to push the entire repository when changes need to be implemented. Instead, only the components that need to be changed are addressed. With dbt cloud and Git together, you can automate continuous integration pipelines, saving management time and simplifying the process.
Easier Data Updates and Quality Checks
In dbt cloud, you don’t need to host an orchestration tool. It has a feature that fully automates the scheduling of production updates at the pace or frequency you want.
dbt also offers several ways to create and enforce data quality checks. You can create data integrity checks when you create documentation for a specific model. It also provides a function to create custom data tests driven by business logic. Finally, it allows you to create snapshot tables that track changes to the data. This method is particularly useful when dealing with mutable data, as you have full access to any changes previously made to the source data.
dbt automates the creation of documentation on descriptions, model dependencies, model SQL, sources, and tests. The documentation displays existing models, relevant database objects and detailed information about each model.
dbt makes the data documentation transparent and visible through the generated lineage graphs. dbt displays the documentation for the project in its web app and contains information about the project (model code, project DAG, tests added to a column) and the data warehouse (column data types, table sizes).
Version control and CI/CD
Secure deployment with development environments. Git-enabled version control enables collaboration and reversion to previous states.
Testing and documentation
Test each model before production and share the dynamically generated documentation with all stakeholders.
Write modular data transformations in .sql or .py files – dbt takes care of the dependency management.
With dbt, replace standard DDL/DML with simple SQL SELECT statements that derive dependencies, create tables and views, and execute models in sequence. Develop code that writes itself with macros, ref statements and auto-complete commands in the Cloud IDE. Use Python packages to speed up complex analyses.
dbt’s pre-built and custom tests help developers create a “paper trail” of validated assumptions for data workers. Automatically generated dependency diagrams and dynamic data dictionaries promote confidence and transparency for data consumers.
Integrate monitoring capabilities into transformation workflows with in-app scheduling, logging, and alerting. Branch protection policies ensure that data passes through governed processes, including development, engineering and production environments created during each CI run.
Now your data science team can create models that are connected to those of the analytics team, each using their preferred language. dbt supports modelling in SQL or Python, enabling a common workspace for everyone working on analytic code.
Manage risk with compliance to SOC-2, CI/CD deployment, RBAC and ELT architecture.
Remove doubts about data with version control, testing, logging, and alerts. Create snapshots of changes over time and provide open access to hosted documentation.
Security and support that scales
With dbt cloud Enterprise, you enable all members of your data team to contribute to transformation – the most secure, reliable and accessible way to create and maintain organizational knowledge.
“The new workflow with dbt and Snowflake isn’t a small improvement. It’s a complete redesign of our entire approach to data that will establish a new strategic foundation for analysts at JetBlue to build on.”
“I didn’t pick Snowflake and dbt for cost purposes. I picked them because they are best-in-class data infrastructure tools,”
Ben Singleton, Director of Data Science & Analytics at JetBlue
Automated dependency management
Teams with legacy data architectures face significant disruption when data structures change. dbt Cloud Enterprise lets you safely update upstream models in seconds. Log and share changes for easy audit and transparency.
Git-based version control
Use your git provider of choice to safely collaborate on shared repositories without duplicating or disrupting previous work. Review, merge, and productionize code using version control best practices tuned for speed and quality.
SSO & RBAC
Leverage your identity provider of choice and apply role-based access permissions for faster and more secure access to dbt Cloud.
Regional deployment options
Host your multi-tenant dbt Cloud instance in a custom deployment region to ensure data residency compliance.
dbt Cloud Enterprise customers have exclusive access to product and process training by experienced analytics engineers. Live workshops reduce time and improve collaboration.
The command line shouldn’t be a barrier to transformation. dbt Cloud’s intuitive browser-based IDE centralizes development. Plus, IT teams don’t have to worry about local installations
Whether your analytics data is stored in a cloud warehouse, data lake or data lakehouse, dbt lets you transform, test and document it.
Click here for the complete overview of supported data platforms.
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