What is Data Build Tool(dbt) and How It Transforms Your Data Pipeline?

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, engineers, and scientists who work with large amounts of data stored in data warehouses and other data storage systems. This article will provide a deep understanding of the Data Build Tool(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. By eliminating the dependency on engineering teams for making changes in the pipelines, dbt allows analysts to collaborate on data models and deploy analytics code using software engineering best practices such as modularity, portability, CI/CD, and documentation. With features like version control, you can test your analytics code in development before deploying it to a production environment.

Core Principles of dbt

dbt 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 a 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 can run SQL directly in the targeted data warehouse. 

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 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 dbt 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. dbt 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 Can I Get Started with dbt?

Before studying about dbt, we recommend the following three prerequisites:

Git: Since dbt Core uses git for all its operations, knowledge of git is necessary. You may take a course on GIT Workflow, GIT Branching, and Collaborative Work. Many great choices are open to you online; take a look and find one you like.

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, many online courses are available, so look for one that will provide you with the foundation you need to begin learning dbt.

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.

How Does dbt Differ From Other Transformation Tools?

There is no single data transformation tool like dbt that does it all. Let’s see how dbt differs from other complementary tools in the market, such as Airflow, Talend, Apache Spark, Hevo, etc.

Feature	dbt	Apache Spark	Talend	Airflow	Hevo
Focus	Data transformation and modeling	Big data processing and analytics	Data integration and transformation	Orchestration of workflows	Data integration, ETL, and Data transformation
Data Processing	SQL-based transformations	Distributed computing for large datasets	ETL processes with a graphical interface	Orchestrates tasks, not direct transformation	Real-time data ingestion and transformation
Ease of Use	Simple, SQL-centric approach	Requires advanced programming knowledge	User-friendly with visual tools	Needs Python/other code for tasks	No-code interface, easy to use
Language	SQL	Scala, Python, Java	Java, SQL, Talend-specific language	Python	No-code with predefined integrations
Real-Time Processing	No, batch processing	Yes, it supports real-time data processing	Yes, it supports real-time and batch	Yes, it can be integrated with real-time systems	Yes, real-time data pipelines
Scalability	Works well for medium to large datasets	Highly scalable for massive datasets	Scalable for enterprise use	Scalable with proper configuration	Highly scalable, handles large volumes
Community Support	Strong open-source community	Large, active community	Strong community and enterprise support	Large open-source community	Excellent community and customer support
Deployment	Cloud-based or on-premise	Cloud-based or on-premise	Cloud-based or on-premise	Cloud-based or on-premise	Cloud-based or on-premise

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. 

How Can You Use dbt to Enhance Your Data Pipelines?

The data build tool takes complete care of both data modeling and testing. You can easily leverage its features in your ELT pipeline, as it allows you to do the following:

• With dbt, 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 describes and how it maps to business logic.
• dbt allows you to schedule production refreshes in whatever sequence your business needs.
• You get many prebuilt testing modules in dbt, 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:

1. SQL-Based Transformation

• dbt lets you write transformation logic directly in SQL, a language familiar to most data analysts and engineers. It lets you create discrete data models, transforming raw data into target datasets while organizing and materializing frequently used business logic efficiently.

2. Jinja for Enhanced SQL Functionality

• dbt uses Jinja, a lightweight templating language, to extend SQL functionality.
• You can leverage control structures like For Loops to simplify repetitive queries.
• Reusable SQL code can be shared through macros, reducing redundancy.
• With Jinja, dbt effectively turns your project into a SQL programming environment.

3. Ref and Dependency Management

• The ref function in dbt allows you to control the execution order of models, ensuring dependencies are handled seamlessly.
• dbt acts as an orchestration layer on top of your data warehouse, pushing all calculations to the database level. This ensures faster, more secure, and easier maintenance of the transformation process.

4. Data Snapshots

• dbt provides a snapshot feature that captures raw data at specific points in time. This is particularly useful for reconstructing past values and tracking historical changes.

5. Testing and Data Integrity

• dbt simplifies data quality checks by enabling:
• Built-in tests for data integrity and validation.
• Custom tests are driven by business logic and are applied directly within YAML configuration files.
• Assertions about test results to improve SQL model accuracy.

6. Automated Scheduling

• dbt automates the scheduling of production refreshes at user-defined intervals, ensuring data is always up-to-date and reliable.

7. Version Control and Documentation

• With Git-enabled version control, dbt allows seamless collaboration and project versioning.
• It auto-generates model documentation that is easy to share with stakeholders and clearly shows dependencies and logic.

8. Community and Open-Source Ecosystem

• dbt is open-source and supported by an extensive library of resources, including installation guides, FAQs, and reference documents.
• Access to dbt packages allows users to leverage prebuilt models and macros to solve common problems efficiently.

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 is less user-friendly as it is SQL-based.
• You need sufficient technical expertise when you need to make changes to the boilerplate code at the backend.
• To keep the data transformation process as readable as possible in the UI, your data engineers must 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, dbt 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 the above data warehouses and can be effectively used for several business use cases, such as:

• Ensure Data Quality and Integrity: Using dbt, you can run tests to validate data and track its lineage to understand how it has been transformed over time.
• Following the software development best practices, dbt provides a consistent and standardized approach to data transformation and analysis.
• Enhanced Collaboration: dbt enhances collaboration and team communication by allowing analysts and engineers to work together on the same complex data models.
• Scalability: 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 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 the 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.
• A well-defined SQL style guide is a good practice for maintaining readability for multiple users in a dbt project.
• 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 update your models when required easily.
• 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, Jinjatemplating, database adapters	Orchestrate your Jobs, Logging and AlertingIntegrated documentation, User auth/SSO

You might have seen two dbt products, i.e., dbt Cloud and dbt Core. But what’s the difference between them? Let’s take 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 combined 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 and 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. 

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.

 Sign up for Hevo’s 14-day free trial and experience seamless data migration. Check out the pricing details to understand which plan fulfills all your business needs.

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