What is Data Build Tool(dbt)? A Comprehensive 101 Guide

With the improvement in technology for replicating data from various sources to one central location, many tools are available that handle ELT (Extract, Transform, and Load). Despite this, businesses still struggle with data modeling. Data Build Tool(dbt) is a robust, open-source tool based on SQL that changes how organizations write, test, and deploy data transformations. 

We think empowering analysts to own the data transformation pipeline is the only way to build a productive analytics team at scale. dbt makes it easier to do data modeling the right way, and harder to do it the wrong way.

James Densmore, HubSpot, Director of Data Infrastructure

It is specifically designed for data analysts, data engineers, and data scientists who work with large amounts of data stored in data warehouses and other data storage systems. This article will provide a comprehensive introduction to dbt, explaining what it is, how it works, and why it is becoming increasingly popular among data professionals.

What is Data Build Tool(dbt)?

dbt is a transformation workflow that allows any business analyst comfortable with SQL to design and implement their own data transformations. Eliminating the dependency on the engineering teams for making changes in the pipelines, dbt allows analysts to collaborate on data models & deploy analytics code with software engineering’s best practices such as modularity, portability, CI/CD, and documentation. With features like version control in Data Build Tool, you can first test your analytics code in the development before deploying them to a production environment.

How does dbt work?

With the advent of ETL technology, where you first load the raw data to your warehouse and then transform, no-code automated tools like Hevo Data and Stich Data completely automated the process. They allow you to simply replicate raw data from multiple sources to your central repository. However, data modeling(transformation) remains a big problem.

Businesses opt to go for a custom code using Airflow. This method is often inaccessible as it is totally written in python and requires a large infrastructure. Or, they go for GUI(Graphical user interface) modeling with tools like Looker. These tools generally come with huge licensing fees and create a host of maintenance issues. This is where Data Build Tool comes in and acts as an orchestration layer on top of your data warehouse. The basic processes involved in dbt are as follows:

• Development: Using simple SQL SELECT statements, you can write modular transformations, build tables and views, and run models in order. You can also use python packages for complex analysis.
• Testing and Documentation: As you develop a model, you can first test it before sending it to production. Data Build Tool also dynamically generates detailed documentation consisting of validated assumptions, dependency graphs, and dynamic data dictionaries. You can quickly share these with all the stakeholders, promoting more data transparency.
• Deployment with Version Control: Deploy your code by going through the dev, stage, and prod environments. Enjoy Git-enabled version control to return to previous states. You can also have better visibility over your transformation workflows with in-app scheduling, logging, and alerting. 

How dbt differs from Other Transformation Tools?

There is no single data transformation tool like dbt that does it all. Let’s see how Data Build Tool differs from other complementary tools in the market, such as Airflow, Great Expectations, and Flyway.

• Like Data Build Tool, Airflow helps create automated operations, run commands, and integrate with a data warehouse. Though it is not meant for full querying. The dbt platform offers a fleshed-out front-end interface for query development and coding, compared to Airflow, which is designed to focus more on the actual flow of data in its interface.
• Flyways assists in version control by tracking changes made to tables in the data warehouse. Unlike Data Build Tool, which manages documentation via integrations with GitHub and DevOps, Flyway needs a separate environment.
• Great Expectations allows you to design tests that run against your database. However, unlike Data Build Tool, it doesn’t allow for integrations with ETL features as well as any editing of the actual database.

Data Build Tool and Data Pipeline – What to expect?

The Data Build Tool takes complete care of both data modeling and testing. You can easily leverage Data Build Tool’s features in your ELT pipeline as it allows you to do the following:

• With Data Build Tool, you don’t need much coding experience as it allows you to perform custom transformations using simple SQL SELECT statements.
• dbt Cloud offers you continuous integration where you only need to push the components that change rather than the entire repository when there are necessary changes to deploy. The seamless integration with GitHub provides complete automation of your continuous integration pipelines.
• You can write Macros in Jinja as a reusable code that can be referred to multiple times.
• As the documentation gets automatically generated, dbt creates lineage graphs of the data pipeline, showing what the data is describing and how it maps to business logic.
• dbt gives you the power to schedule production refreshes at whatever sequence your business needs.
• You get a whole bunch of prebuilt testing modules in Data Build Tool, including unique, not null, referential integrity, and accepted value testing. Carrying out a test becomes completely effortless as you can reference the test under the same YAML file used for documentation for a given table or schema.   

Benefits of the dbt Tool 

dbt offers a complete set of eye-catching advantages that make it a powerful data transformation tool:

• You don’t need to write the boilerplate code to create tables and views. dbt wraps your select statements in another statement at the backend to create a new relation or update an existing one.
• dbt uses Jinja, a lightweight templating language. It allows you to use control structures like For Loops in your queries. It also enables repeated SQL to be shared through macros. 
• Using the ref function, you get complete control over the order of execution of your model.
• For cases when you want to reconstruct past values, dbt offers a mechanism to snapshot raw data for a point in time.
• You get Git-enabled version control and auto-generated documentation of your models that can be easily shared with all the stakeholders.
• After you have designed and executed tests, dbt provides assertions about the test results generated by a model to improve the integrity of SQL. 

Disadvantages of the dbt Tool

Now, let’s check out a few of the limitations of dbt:

• You still need data integration tools to extract and load data from multiple sources to your data warehouse, as dbt only takes care of the transformation part in ELT.
• Compared to tools offering a GUI, dbt can seem less user-friendly as it is SQL based.
• For cases when you need to make changes to the boilerplate code at the backend, you need sufficient technical expertise.
• To keep the data transformation process as readable as possible in the UI, your data engineers need to keep it clean and comprehensible. 

dbt and Modern Data Stack: 4 Core Use Cases 

dbt has become a popular data transformation tool that fits nicely with the current cloud-based modern data stack. Simplifying the data transformation, testing, and deployment process, Data Build Tool offers higher flexibility for all businesses as it offers support for multiple data warehouses:

• dbt Redshift
• dbt BigQuery 
• dbt Snowflake
• dbt Databricks 
• dbt Postgres 
• dbt Azure Synapse 

It can run on top of any of the above data warehouses and can be effectively used for several business use cases such as:

• With the ability to run tests to validate data and track the lineage of data to understand how it has been transformed over time, you can ensure data quality and integrity using dbt.
• Following the software development best practices, dbt provides a consistent and standardized approach to data transformation and analysis.
• Enhancing collaboration and communication across teams, Data Build Tool allows analysts and engineers to work together on the same complex data models.
• Allowing you to have a scalable data infrastructure, dbt can be used to define data models using SQL and then generate optimized SQL for your data warehouse.

dbt Best Practices

There are a few best practices suggested by Data Build Tool you can follow for the best data transformation experience:

• While managing your dbt projects in version control, it is recommended that all code changes should be reviewed in a Pull Request before merging into master. Go for a dev target when running dbt from your command line and only run against a prod target when executing from a production deployment.
• To maintain readability for multiple users in a dbt project, it is a good practice to have a well-defined SQL style guide.
• Instead of using the direct relation reference, you can use a ref function when selecting from another model.
• As your raw data structure might change over time, it is suggested to reference raw data in only one place to easily make updates to your models when required.
• Renamed the field and tables to naming conventions you wish to use for analytics.
• Instead of having multiple common table expressions(CTEs) for complex projects, you can separate these CTEs into separate models that build on top of each other in dbt.
• You can group your models in directories, allowing you to easily run subsections of your DAG and communicate modeling steps to collaborators.
• Add tests to your projects to ensure the SQL is transforming the data as you expect.  

dbt Cloud vs dbt Core

You might have seen 2 Data Build Tool products, i.e., dbt Cloud and dbt Core. But what’s the difference between them? Let’s have a closer look at the differences:

• dbt Core is a free, open-source, command-line tool that enables users to design their data models using SQL. It then converts these models into optimized SQL code that can be executed on data warehouses or other data storage systems.
• In contrast, dbt Cloud is a cloud-based solution that offers additional features and capabilities in addition to those offered by dbt Core. It provides a web interface for managing data models and also includes scheduling options, collaboration tools, and integrations with other data tools.

FAQs on Data Build Tool

Here are the common FAQs on the Data Build Tool:

Who uses dbt?

dbt is used by data analysts, data engineers, and data scientists who work with data warehouses and other data storage systems. It is especially useful for organizations that have a large number of data sources and need to combine and transform data into a single source of truth.

Is there a cost to using dbt?

dbt Core is an open-source tool and is available for free. dbt Cloud is an enterprise-level subscription-based solution, and pricing is based on usage and features.

What kind of data storage systems does dbt support?

dbt supports various data storage systems, including data warehouses such as Snowflake, Redshift, and BigQuery, as well as databases such as PostgreSQL and MySQL.

Conclusion 

Finally, you now have the complete know-how of dbt. Based on your business requirements, you can choose whether to opt for dbt as a data transformation solution or go for custom coding. As dbt supports most data warehouses, you can quickly onboard the tool and get started. However, there is still a gap that remains! You need to pull data from all your sources and load them into your data warehouse. 

For cases when you rarely need to replicate data, your engineering team can easily do it. Though, for frequent and massive volumes of data transfers from multiple sources, your engineering team would need to constantly monitor and fix any data leaks. Or you can simply hop onto a smooth ride with cloud-based ELT solutions like Hevo Data which automates the data integration process for you and runs your dbt projects to transform data present in your data warehouse. At this time, the dbt Core™ on Hevo is in BETA. Please reach out to Hevo Support or your account executive to enable it for your team.

Visit our Website to Explore Hevo

Offering 150+ plug-and-play integrations and saving countless hours of manual data cleaning & standardizing, Hevo Data also offers in-built pre-load data transformations that get it done in minutes via a simple drag-n-drop interface or your custom python scripts. 

Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

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