What is dbt (Data Build Tool)?

Overview: What is dbt and what can it do for your data pipeline?

One tool that we recommend and often use is dbt (data build tool), which focuses on simplifying and accelerating the process of data transformation. In this blog, we will explore what dbt is and how it can revolutionize the way your organization manages data for decision-making. Additionally, we will guide you on how to get started with dbt.

What is dbt (data build tool)?

dbt is a development framework that combines modular SQL with software engineering best practices. This approach makes data transformation reliable, fast, and enjoyable.

The key advantage of dbt is making data engineering tasks accessible to individuals with data analyst skills. It allows the transformation of data in the warehouse using simple select statements. This approach enables the creation of the entire transformation process through code. With dbt, users can write custom business logic using SQL, automate data quality testing, deploy code, and deliver trusted data.

Importantly, it includes data documentation alongside the code. In the current market, where there is a shortage of data engineering professionals, dbt’s approach is particularly valuable. It enables anyone with SQL knowledge to build production-grade data pipelines, significantly lowering the barrier to entry compared to legacy technologies.

In summary, dbt transforms data analysts into engineers. It empowers them to fully own the analytics engineering workflow.

{{ config(materialized="table", schema="staging") }}
WITH source AS (
   SELECT
       item_id,
       quantity,
       price,
       description
   FROM source_table
)
SELECT *
FROM source

An example dbt Model in the staging schema that gets materialized on each run.

How does dbt (Data Build Tool) Stand Out?

dbt enables anyone familiar with SQL to build models, write tests, and schedule jobs for reliable analytics datasets. It serves as an orchestration layer over your data warehouse, enhancing and expediting data transformation and integration. dbt processes code directly in the database, making transformations faster, more secure, and easier to maintain. Its user-friendliness allows those with basic SQL knowledge, not just data engineers, to efficiently build data pipelines.

What Benefits Does dbt (Data Build Tool) Offer for Data Pipelines?

dbt excels in two key areas: building and testing data models. Its compatibility with the modern data stack and cloud-agnostic nature ensures seamless integration with major cloud ecosystems like Azure, GCP, and AWS.

dbt empowers data analysts to fully manage the analytics engineering workflow. This encompasses everything from writing data transformation code to deployment and documentation. Moreover, it enables analysts to foster a data-driven culture within their organization. They can:

1. Quickly and easily provide clean, transformed data ready for analysis:

dbt enables data analysts to custom-write transformations through SQL SELECT statements. There is no need to write boilerplate code. This makes data transformation accessible for analysts that don’t have extensive experience in other programming languages.

2. Apply software engineering practices—such as modular code, version control, testing, and continuous integration/continuous deployment (CI/CD)—to analytics code:

Continuous integration with dbt Cloud streamlines testing and accelerates development. Unlike traditional methods where an entire repository must be pushed for changes, dbt Cloud allows for deploying only the modified components. This enables thorough testing of all changes before production deployment. Additionally, dbt Cloud’s integration with GitHub automates continuous integration pipelines, eliminating the need for manual orchestration and simplifying the process.

3. Build Reusable and Modular Code with Jinja in dbt

dbt (data build tool) enables the creation of macros and the integration of functions beyond SQL’s native capabilities for advanced use cases. Macros, written in Jinja, are reusable code segments. This allows analysts to avoid beginning from scratch with raw data for each analysis. Instead, they build up reusable data models that serve as references for future work.

Instead of repeating code to create a hashed surrogate key, create a dynamic macro with Jinja and SQL to consolidate the logic in one spot using dbt.

Instead of repeating code to create a hashed surrogate key, create a dynamic macro with Jinja and SQL to consolidate the logic in one spot using dbt.

4. Maintain Data Documentation and Develop Lineage Graphs within dbt

Data documentation in dbt is accessible and easily updated, facilitating the delivery of trusted data organization-wide. dbt automatically generates comprehensive documentation, including descriptions, model dependencies, model SQL, sources, and tests. Furthermore, dbt creates detailed lineage graphs, offering clear visibility into the data’s description, production process, and its alignment with business logic.

5. Perform simplified data refreshes within dbt Cloud:

There is no need to host an orchestration tool when using dbt Cloud. It includes a feature that provides full autonomy with scheduling production refreshes at whatever cadence the business wants.

6. Automated Testing in dbt

dbt comes equipped with built-in tests for uniqueness, non-null values, referential integrity, and accepted values. Additionally, you can create custom tests using Jinja and SQL. To test a specific column, you just reference it in the YAML file used for documentation of the respective table or schema. This approach simplifies the process of ensuring data integrity.

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

Prerequisites to Getting Started with dbt (Data Build Tool)

Before diving into dbt, we recommend mastering three key skills:

  1. SQL Proficiency: Since dbt relies heavily on SQL for data transformations, proficiency in SQL SELECT statements is essential. If you’re not yet experienced, numerous online courses can provide a solid foundation in SQL, preparing you for dbt learning.
  2. Data Modeling Skills: Effective data modeling is crucial for code reusability, detailed analysis, and performance optimization in any data transformation tool. Instead of merely replicating your data sources’ structure, learn to transform data to align with your business’s language and structure. This approach is vital for structuring your project and achieving long-term success.
  3. Git Knowledge: For those interested in dbt Core, a good understanding of Git is necessary. Seek out courses that cover Git Workflow, Git Branching, and collaborative Git usage. Plenty of comprehensive online resources are available for this purpose.

Training To Learn How to Use dbt (Data Build Tool)

There are many ways you can dive in and learn how to use dbt (data build tool). Here are three tips on the best places to start:

  1. The dbt Labs Free dbt Fundamentals CourseThis course is a great starting point for any individual interested in learning the basics on using dbt (data build cloud). This covers many critical concepts like setting up dbt, creating models and tests, generating documentation, deploying your project, and much more.
  2. The “Getting Started Tutorial” from dbt Labs: Although there is some overlap with concepts from the fundamentals course above, the “getting started tutorial” is a comprehensive hands-on way to learn as you go. There are video series offered for both using dbt Core and dbt Cloud. If you really want to dive in, you can find a sample dataset from online to model out as you go through the videos. This is a great way to learn how to use dbt (data build tool) in a way that will directly reflect how you would build out a project for your organization.
  3. Join the dbt Slack Community: This is an active community of thousands of members that range from beginner to advanced. There are channels like #learn-on-demand and #advice-dbt-for-beginners that will be very helpful for a beginner to ask questions as they go through the above resources.

dbt (data build tool) simplifies and speeds up the process of transforming data and building data pipelines. Now is the time to dive in and learn how to use it to help your organization curate its data for better decision-making.

Matt von Rohr
Matt von Rohr

#ai #datascience #machinelearning #dataengineering #dataintegration

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