Leveraging DBT for Data Modeling and Transformation

Guest blog by Shashank Mishra, Data Engineer @ Expedia

TLDR

DBT (Data Build Tool) streamlines data modeling and transformations in modern data platforms. This tech blog dives into the essentials of setting up DBT projects, executing transformations, and offers an illustrative project example, capped off with best practices for creating robust DBT models.

Outline

• Introduction to data modeling with DBT
• Understanding DBT projects and packages
• Writing and running DBT transformations
• Best practices for writing DBT models
• Conclusion

Introduction to data modeling with DBT

Data modeling is about creating a structured representation of data to ensure it is useful and meaningful. With DBT, this means defining the structure, relations, and integrity of data tables and views within your warehouse.

DBT Models:

• Definition: At its core, a DBT model is a SQL select statement. When DBT runs a model, it translates this SQL into a table or view in the data warehouse.
• File-based: Each model lives in its file and is written in SQL. This ensures clarity and separation of different data entities.

Materializations:

• One of the core concepts in DBT. It dictates how the SQL code in a model is executed in the warehouse.
• Common types include:
  - table: creates a table and rebuilds it on every DBT run.
  - view: creates a SQL view.
  - incremental: only adds new records since the last DBT run.

DBT’s Approach to Data Modeling:

• Source Tables: Start with raw data, often termed ‘source tables’ in DBT. This raw data is then refined into more structured and useful entities.
• Building Layers: DBT promotes creating multiple layers of transformation, often starting with a base layer (raw data with minimal changes), followed by intermediate layers, and ending with a final business-logic layer. Each layer is a refined version of the previous one.

Relations and Joins:

• Like any data modeling tool, DBT models can define relations between different data entities using SQL joins. This allows for the creation of comprehensive datasets from disparate sources.

Testing Data Models:

• DBT allows you to write tests against your models, ensuring data integrity and consistency. For instance, you can check if a column has unique values or if there are null values where they shouldn’t be.

Documentation:

• One of DBT’s strengths in the realm of data modeling is its ability to auto-generate documentation based on the models and their descriptions. This ensures stakeholders have clarity on data structures and transformations.

(Source: Giphy)

Understanding DBT projects and packages

DBT projects and packages are core components of the DBT framework that allow data teams to organize, reuse, and share their transformation logic. This organization and modularity play a crucial role in scaling and maintaining a clean data transformation workflow.

DBT Projects:

• Definition:
  - A DBT project is a directory of configuration files, models, tests, and other assets that define how DBT should transform your data.
• Initialization:
  - Projects are initiated with the dbt init <project-name> command. This creates a standard directory structure to begin your work.
• Key Components:
  - Models: Core SQL transformations that you’ll run.
  - Tests: Assertions and checks on your data models.
  - Macros: Reusable SQL snippets or logic.
  - Snapshots: Track changes in data over time.
  - Analysis: One-off analyses that don’t result in persistent models.
  - Documentation: Auto-generated and custom documentation for your data transformations.
• dbt_project.yml Configuration:
  - The heart of a DBT project, it includes settings such as project name, configuration versions, source data configurations, and default materializations.

DBT Packages:

• Definition:
  - Packages are bundles of DBT models, macros, and other assets that can be reused across multiple DBT projects.
• Why Use Packages?
  - Reusability: Avoid rewriting common transformations or macros.
  - Community Contributions: The DBT community has developed numerous packages for common tasks or specific platforms (e.g., Google Analytics, HubSpot).
  - Consistency: Standardize certain transformations across different projects or teams.
• Using Packages:
  - You can incorporate a package into your DBT project by adding it to your packages.yml file and then running dbt deps to download the specified packages.
  - Example packages.yml entry:

packages:
  - package: fishtown-analytics/dbt_utils
    version: 0.6.4

Building Your Own Package:

• If you have a set of models or macros reused across projects, consider turning them into a package.
• Organize the reusable assets in a directory, ensure it has a valid dbt_project.yml, and then reference this directory in the packages.yml of other projects.

Updating and Versioning:

• DBT’s package manager supports versioning. This means you can specify which version of a package to use, ensuring stability and compatibility.
• Regularly running dbt deps ensures you have the latest compatible versions of all packages.

(Source: Giphy)

Writing and running DBT transformations

DBT (Data Build Tool) is revolutionizing the way data engineers and analysts work with data transformations, specifically in the ‘T’ of the ELT (Extract, Load, Transform) paradigm. Writing and executing transformations in DBT requires a blend of SQL skills, understanding of DBT’s command-line interface, and familiarity with its core principles. Let’s walk through the process:

Setting Up Your DBT Project:

• Before you write transformations, ensure you’ve set up a DBT project using dbt init <project-name>
• This will create a directory structure with folders such as models, tests, and macros.

Writing Transformations:

• Transformations are written as SQL SELECT statements in DBT models.
• Each model corresponds to a single file in the models directory.
• Example:

SELECT
    order_id,
    SUM(order_amount) as total_amount
FROM source_data.orders
GROUP BY order_id;

Materializations:

• Determine how the transformation will be executed in the database.
• Types include:
  - table: Creates or replaces a table.
  - incremental: Adds new records.
  - view: Creates a database view.
• Specify the materialization type in the model’s configuration using:

{{
    config(materialized='table')
}}

Using Macros for Reusability:

• Macros allow you to write reusable SQL snippets.
• Defined in the macros directory.
• Called using the {{ }} Jinja syntax.
• Useful for repetitive logic, like date transformations.

Running the Transformation:

• Use the command line to navigate to your DBT project directory.
• Use the dbt run command. This will execute all models in your project.
• For a specific model: dbt run — models <model_name>

Incremental Builds:

• To save time and resources, DBT supports incremental runs.
• Models with incremental materialization will only process new data.
• Define logic using the is_incremental() function in your SQL.

Testing Your Transformations:

• Ensure data consistency by writing tests.
• Use dbt test to run tests defined in the tests directory.
• Common tests include checking for null values or verifying unique keys.

Viewing Execution Results:

• After running transformations, DBT provides a log in the console.
• Details like execution time, data scanned, and any errors are visible.

Compiling SQL for Debugging:

• Before running, you can compile the SQL to see the generated SQL statement.
• Use dbt compile to achieve this. Useful for debugging and understanding macros.

Documentation and Lineage:

• Once transformations are written and executed, generate documentation using dbt docs generate
• View it using dbt docs serve, offering insights into model lineage and dependencies.

(Source: Giphy)

Best practices for writing DBT models

DBT (Data Build Tool) brings a software engineering approach to the realm of data transformations. Writing maintainable, efficient, and clear models is essential for the long-term success of your data operations. Here are some best practices to consider:

Modularize Your Models:

• Why? Modular models are easier to understand, test, and maintain.
• How? Break down transformations into logical components. If a transformation step becomes too complex, consider splitting it into multiple models.

Leverage Naming Conventions:

• Why? Consistency aids clarity.
• How? Adopt a consistent naming scheme (e.g., prefixes like stg_ for staging models, dim_ for dimensions, fct_ for facts).

Use Descriptive Model Names:

• Why? Understand the purpose of a model at a glance.
• How? Choose names that capture the essence of the data or transformation (e.g., orders_summary).

Document as You Go:

• Why? Future you, and other team members, will appreciate it.
• How? Use the description field in models. DBT will use this when auto-generating documentation.

Test Your Models:

• Why? Ensure data quality and catch issues early.
• How? Regularly use DBT’s built-in testing framework to validate things like uniqueness, non-null constraints, or relationships.

Optimize for Incremental Builds:

• Why? Faster build times and reduced data processing costs.
• How? Use the incremental materialization and leverage the is_incremental() function in your SQL logic.

Abstract Reusable Logic with Macros:

• Why? DRY (Don’t Repeat Yourself) principle.
• How? For frequently used SQL snippets or logic, turn them into macros. This also aids in code consistency across models.

Ensure Readability:

• Why? Code is read more often than it’s written.
• How? Format SQL consistently, use comments generously, and keep lines of code to a reasonable length.

Organize with Folders:

• Why? Easier navigation and model management.
• How? Group related models in subdirectories, like orders or inventory.

Manage Dependencies Explicitly:

• Why? Ensure models run in the correct order.
• How? Use ref() function to reference other models. DBT will then build a dependency graph and execute models in the correct sequence.

Be Cognizant of Warehouse Costs:

• Why? Optimize for cost and performance.
• How? Use the table materialization judiciously, especially on large datasets, to avoid excessive compute costs. Lean towards views where suitable.

Regularly Review and Refactor:

• Why? Needs change, and so does data.
• How? Periodically reassess your models in light of new business requirements or data changes. Refactoring can improve efficiency, clarity, and relevance.

(Source: Giphy)

Conclusion

In the evolving landscape of data engineering, DBT emerges as a powerful tool to streamline and optimize the transformation process. By adopting best practices in modeling, understanding the intricacies of projects and packages, and leveraging its robust framework, teams can revolutionize their ELT workflows. Embracing DBT today paves the way for a more agile, transparent, and efficient data-driven future.

Link to the original blog: https://www.mage.ai/blog/leveraging-dbt-for-data-modeling-and-transformation