1. What is dbt?

What is dbt and what is it used for?

dbt (Data Build Tool) is an open-source tool used for transforming raw data into clean data. It allows data teams to define, document, test, and version control SQL transformations directly in their warehouse.

Key Features of dbt:

Transforming data after it has been loaded into a warehouse. (It’s the T in ELT).
Building and managing data models.
Testing the data quality by running tests in dbt.
dbt automatically can create documentation for data pipelines.

How does dbt differ from other data transformation tools?

SQL-First Approach: dbt focuses on transformations using SQL, making it highly accessible for analysts and engineers familiar with SQL, rather than requiring knowledge of other programming languages like Python or Java.
In-Database Transformation: Instead of extracting data to transform it outside the data warehouse, dbt performs transformations directly inside the data warehouse, utilizing the warehouse’s compute power for scalability and efficiency (part of the ELT process).
Modular, Version-Controlled Models: dbt allows users to define modular models that can be version-controlled with Git, encouraging collaboration, reusability, and consistency across teams.
Automated Documentation: dbt can automatically generate documentation and a data catalog from the SQL models, making it easier to understand the transformation logic and dependencies.
Testing and Data Quality: dbt includes built-in capabilities for testing data quality, allowing users to add tests to ensure data integrity as part of the transformation process.
End-to-End Pipeline Management: While many tools focus on either extraction/loading (ETL) or scheduling, dbt focuses primarily on the transformation layer, bridging the gap between raw data and cleaned, transformed datasets ready for analysis.

2. How does dbt work?

How does dbt function at a high level? (Models, Sources, Tests, Documentation)

Models:

A model in dbt is a SQL file that represents a transformation of data.
Models are used to define how raw data should be transformed into a more structured format.
Models can depend on other models, allowing users to build a chain of transformations, from raw data to final, refined tables.
dbt manages these dependencies automatically and compiles the SQL models into actual SQL queries that run inside the data warehouse.

Sources:

Sources in dbt are external raw datasets loaded into the data warehouse (such as from a data lake or an ETL process).
dbt allows you to define these raw data tables explicitly, track them, and reference them in your models.
By defining sources, dbt helps ensure that the transformations are connected back to the original data.

Tests:

dbt allows users to implement both built-in and custom tests to ensure data quality.
Tests can check for common issues like null values, uniqueness, or referential integrity across different models.
These tests are automatically run when dbt builds models, helping to catch data issues early in the transformation process.

Documentation:

dbt automatically generates documentation based on the models, sources, and tests you define.
It includes model descriptions, column-level documentation, relationships between models, and test results.
You can also view the data lineage and dependencies, making it easier to understand the overall data flow.
This documentation is rendered as a web-based data catalog, making it accessible to teams for collaboration and transparency.

Summary:

dbt organizes the transformation process by managing SQL models, defining sources, ensuring data quality with tests, and generating comprehensive documentation, all within the warehouse environment. This modular and automated approach simplifies the data transformation pipeline.

What is the principle behind the “T” in the ELT process (Extract, Load, Transform)?

The principle behind the “T” in the ELT process is to transform data directly within the data warehouse after it has been extracted from source systems and loaded. This allows for scalable, efficient transformations using the data warehouse’s compute power, enabling more flexible and faster processing of large datasets.

3. Models in dbt

How are models created and managed in dbt?

In dbt, models are created as SQL files that define data transformations.
These models are organized into a directory structure, version-controlled with Git, and managed through dbt’s automated system, which handles dependencies, compiles the SQL into queries, and runs them in the data warehouse to transform raw data into structured, analyzed tables.

What is a “model file” and how do you define SQL transformation logic?

A “model file” in dbt is a SQL file that contains the transformation logic for processing data. It defines how raw data should be transformed into a more structured format.
To define SQL transformation logic, you write SQL queries within the model file, and dbt compiles and runs these queries in the data warehouse, managing dependencies between models automatically.

4. Sources and Dependencies

How does dbt connect to data sources?

dbt connects to data sources by defining sources in the project. A source is a raw dataset that has been loaded into the data warehouse, typically through an ETL or ELT process. In your dbt project, you define these sources in your models using a source block.

The my_source and my_table refer to the schema and table within the data warehouse. By defining sources, dbt allows you to reference raw tables in your transformations and track the lineage of data from its raw form to its transformed state. It also makes it easier to manage and ensure data quality at the source level.

How are dependencies between different models handled?

In dbt, dependencies between different models are handled automatically by dbt’s model compilation and execution process. These dependencies are determined based on how models reference each other using ref() functions.

Here’s how it works:

Model References (ref): When a model references another model, it uses the ref() function. This establishes a dependency between the models. In this case, the current model depends on the orders model. dbt ensures that the orders model is built first before the dependent model is run.
Dependency Graph: dbt automatically creates a dependency graph based on how models reference each other. This graph determines the order in which models are run. dbt will only execute models once their dependencies have been successfully built.
Parallel Execution: dbt can run models in parallel when they do not have dependencies on each other. This optimizes performance, especially for large projects.
Automatic Handling: dbt tracks all these dependencies and ensures that when you run a specific model or an entire project, the models are executed in the correct order, without you needing to manually manage the sequence.

In summary, dbt handles dependencies between models by analyzing ref() functions, automatically building a dependency graph, and executing models in the appropriate order based on these relationships.

5. Transformations and Tests

How does dbt perform data transformations?

Dbt performs data transformations by compiling SQL models you define into SQL queries that run directly in your data warehouse. It uses the ELT (Extract, Load, Transform) approach, where raw data is first loaded into the warehouse (often a schema named staging), and dbt transforms the data in place using SQL. dbt manages dependencies between models, supports incremental processing, and can materialize transformations as views, tables, ephemeral or incremental tables.

How does dbt verify and test data quality? (e.g., with dbt test)

Dbt verifies and tests data quality using built-in tests and custom tests. The dbt test command runs these tests against your data models to ensure accuracy and integrity.

Built-in Tests: dbt provides common tests such as: • unique: Ensures values in a column are unique. • not_null: Checks that there are no null values in a column. • accepted_values: Verifies that a column contains only specific, expected values. • relationships: Ensures referential integrity between tables.
Custom Tests: You can write custom tests by defining SQL queries that return failing records. If the query returns any rows, the test fails.
Automated Testing: These tests are automatically run when you execute dbt test, allowing you to catch data quality issues early in the transformation process. This ensures that the data is consistent and reliable before being used in downstream analysis.

6. Macros and Variables

What are macros in dbt and how are they used?

Macros in dbt are reusable SQL code snippets written in Jinja. They allow you to create dynamic and reusable SQL logic across multiple models, simplifying complex queries and reducing redundancy. You can use macros to define functions that perform common tasks, like generating keys or formatting data, and call them throughout your project.

How can variables be used to make the project more flexible?

Variables in dbt can be used to make a project more flexible by allowing dynamic values to be passed into models, macros, or tests. You can define variables in the dbt_project.yml file or through the command line and reference them using var('variable_name') in your SQL. This helps customize behavior based on different environments (e.g., dev vs. prod) or parameterize logic for reuse.

7. Documentation and Data Catalog

How is documentation created and maintained in dbt? (with dbt docs)

In dbt, documentation is created and maintained by adding descriptions to models, columns, and tests directly in YAML files. The dbt docs generate command compiles this metadata into a web-based data catalog. The catalog includes model descriptions, column-level details, and data lineage, making it easy to explore and maintain documentation over time.

8. Best Practices in dbt

What are some general best practices when working with dbt?

Keep models simple: Break down complex transformations into smaller, modular models.
Use version control: Manage changes in models and tests with Git.
Test data quality: Regularly run built-in and custom tests to ensure data integrity.
Document thoroughly: Add descriptions to models and columns for clarity.
Use incremental models: Optimize performance by only processing new data.
Leverage macros: Reuse logic and reduce duplication across models.

How do you organize dbt projects in a way that promotes scalability and simplicity?

Use a clear folder structure: Organize models into meaningful directories (e.g., staging, intermediate, marts) to separate raw data, transformations, and business logic.
Modularize models: Break complex transformations into smaller, reusable models.
Consistent naming conventions: Use clear, consistent names for models, columns, and tests to improve readability and maintainability.
Leverage macros and variables: Centralize logic for reuse and flexibility.
Incremental models: Use incremental models for large datasets to improve performance.
Document and test: Keep thorough documentation and regularly run tests to ensure data quality.

9. Onboarding New Data Engineers

What should a new data engineer learn about dbt first?

Project structure: Understand how dbt organizes models, sources, and tests.
SQL models: Learn how to create and manage SQL transformations using models.
ref() and dependencies: Understand how to reference other models and manage dependencies.
Data quality tests: Learn how to implement and run built-in tests like not_null and unique.
Documentation: Get familiar with adding descriptions and generating documentation.
Incremental models: Learn how to optimize performance using incremental models for large datasets.

DanielSjoholm/What_is_dbt_documentation