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

Despite all the ELT tools available out there, 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 deep understanding to the answer of question what is dbt data build tool, 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.

Core Principles of Dbt

The data building tool does data transformation and modeling based on certain core principles such as:

• Data Warehouse centric: Once the raw data is ingested into the data warehouse, dbt uses its in-database transformation capabilities for heavy computations
• ELT Workflow: Dbt does ELT on databases wherein data is first loaded and then transformed. 
• SQL-based: It uses SQL as its domain-specific language. This enables direct transformation instead of dependence on externally controlled transformation languages or ELT tools based on GUIs.
• Git-based version control: Usually Git is involved in version control of dbt projects. This enables collaboration using pull requests and branch-based development.
• Data Testing: It enables custom data tests in SQL to be written for specific constraints. Its Schema test ensures data integrity. 
• Model Dependencies: dbt models can refer to other models using the ref function. This allows the formation of DAG(Directed Acyclic Graph) of dependencies, which can be used to run models in the right order. 

DBT Architecture

Dbt uses SQL and Jinja2 to transform and model data. It is a command-line tool with a unique architecture having the following components:

• Command Line Interface: This enables you to run commands for transforming, testing, and documentation. 
• Projects: It is the foundation of DBT, which consists of models, tests, snapshots, etc.
• Models: These are the SQL files that show transformation logic. 
• Tests: dbt supports both built-in and custom testing to ensure data integrity. The below query restricts the bk_source_driver field from model “fact_interaction” to not have a NULL value of more than 5% of its values set. 

• Auto-generation: It automatically generates a web-based documentation portal to visualize model metadata and lineage. 
• In-database computation: The dbt tool has the capability to run SQL directly in the targeted data warehouse. 

The image shows the data lineage of some tables in the DBT user interface. This helps in understanding the flow of data from source to target. 

Also, you can create a project structure by just running “dbt init” in CLI. This initializes a project and ensures all the team members work with the same template.

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.

How Can I Get Started with Data Build Tool (dbt)?

Before studying Data Build Tool (DBT), we recommend the following three prerequisites:

1. SQL: Because dbt employs SQL as its primary language to accomplish modifications, you must be skilled in SQL SELECT statements. If you don’t have this background, there are many online courses available, so look for one that will provide you with the foundation you need to begin learning dbt.
2. Modeling: Data modeling, like any other data transformation tool, requires a plan. This will be crucial for code reuse, drill-down, and performance optimization. Don’t merely follow your data sources’ models; we advocate translating data into the business’s language and structure. Modeling is vital for structuring your project and achieving long-term success.
3. Git: If you want to learn how to utilize dbt Core, you will need to be familiar with Git. We recommend taking a course that covers Git Workflow, Git Branching, and team collaboration. There are many fantastic options accessible online, so explore and select one that you prefer.

How does dbt integrate with Platforms for Models?

Data Building Tool is a transformation tool that allows you to view and consume the data loaded to your destination tables in a suitable form supported for analytics. Data Building tools can be integrated with various platforms such as Hevo Data for models. 

Let’s look at how to integrate dbt with the Hevo workflow

1. Connect the dbt projects you have created in any Git repository and run the dbt models on your destination data. You can configure dbt projects in destination types such as PostgreSQL, BigQuery, Amazon Redshift, Snowflake, and Data Bricks. 
2. Schedule your dbt project for 

• Running all the models together
• Running all models individually
• Triggering when any event is loaded to the destination table

3. After completing the activity log, integrate your dbt project with Hevo Workflow. 

Check out our documentation to learn more about dbt models.

The dbt project consists of resources such as models, seeds, snapshots, docs, etc. 

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. 
• All data transformations can be arranged with the help of dbt tool into discrete data models. dbt allows you to convert raw data into target datasets, and organize and materialize frequently used business logic in a fast way.
• dbt data build tool has a feature that automates the scheduling of production refreshes at your frequency. It also provides various ways to create and enforce data quality checks. You can create data integrity checks while creating documentation for any given model. Further, it has a function to make custom data tests driven by business logic. 
• Testing data integrity becomes easier with dbt. By combining Jinja with SQL, it is easy to turn your dbt project into a programming environment for SQL. Application of a test on a given column can be made by dbt by simply referencing it under the same YAML file.
• The Data Build tool behaves like an orchestration layer on top of your data warehouse. This improves and accelerates the data transformation and integration process. It pushes code to do all the calculations at the database level. This enables faster, more secure, and easier to maintain the transformation process. 
• dbt is open-source and offers a large library of reference documents, installation guides, and FAQs.Additionally, it also provides access to dbt packages. This enables you access to libraries of models and macros containing specific problems that have already been answered.

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: 5 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 offers a consistent and standardized method for data transformations and analysis, making it easier for analysts and engineers to deal with it. This can help organizations enhance the quality and dependability of their data, making it simpler to extract insights and make business choices.

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

dbt Core	dbt Cloud
Open-Source data transformation	A fully managed dbt experience
Open Source: Apache 2.0	SaaS Managed
Interface via the CLI	Full IDE to develop and test your dbt Code
Includes core SQL compilation logic, Jinja templating, database adapters	Orchestrate your Jobs, Logging and Alerting Integrated documentation, User auth/SSO

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.

What dbt isn’t? 

dbt is not a data warehouse or a database. Instead, it is a tool that can be used in combination with a data warehouse to make its functioning easier and manage data. Also, dbt is not a programming language. It uses programming-like syntax to specify to load and transform data in the data warehouse. It is also not a visualization tool. However, it can be used with visualization tools like Tableau or Looker to help you understand and analyze your data. 

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.

See how to link DBT to Snowflake for optimized data management. Explore our detailed guide for straightforward instructions on setting up the integration.

Learn More About:

DBT Commands

Conclusion 

Finally, you now have the complete know-how of dbt. This article provided you with comprehensive information on what is dbt in data engineering, along with its use cases. 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.

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. 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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