dbt

https://www.getdbt.com/blog/what-exactly-is-dbt

data build tool s an open-source framework designed for data transformation within a modern data stack. It enables analysts and engineers to transform, model, and manage data using SQL while adhering to software engineering best practices like version control, testing, and documentation.

Key Concepts of dbt:

1. SQL-based Transformation: dbt allows users to write SQL queries to define transformations and models, making it accessible for analysts who are already familiar with SQL. It doesn’t handle extraction or loading of data, but focuses purely on transforming data that is already in a data warehouse.

2. Modular and Reusable Models: dbt encourages the creation of modular, reusable SQL "models." Each model represents a transformation, and these models can be built on top of each other. A model is essentially a SQL query stored as a .sql file that dbt uses to transform raw data into a refined dataset. Models are run in sequence, with dbt handling dependencies between models.

3. Version Control and Collaboration: dbt integrates with Git for version control, making it easy for teams to collaborate, track changes, and roll back to previous versions if needed. This promotes transparency and accountability within the data team.

4. Testing: dbt allows users to write and run tests to ensure data integrity and consistency. You can define tests for specific models or fields, like checking for non-null values or ensuring data uniqueness.

5. Documentation: dbt auto-generates documentation from your models, providing a clear overview of your data transformations, lineage, and dependencies. You can also add descriptions for models and fields to improve the clarity of your data pipelines.

6. Data Lineage: dbt automatically tracks the lineage of your data by mapping dependencies between models. This makes it easy to understand how data flows through the pipeline and where any upstream or downstream issues might originate.

7. Extensibility: dbt has a plugin architecture that allows users to extend functionality. For example, there are adapters for popular data warehouses like Snowflake, BigQuery, Redshift, and others, making dbt highly flexible in different data stack environments.

8. Cloud and Core Versions:

   • dbt Core is the open-source version that you run locally or in your cloud infrastructure.
   • dbt Cloud is a fully managed service that adds features like scheduling, logging, and a web-based IDE for dbt workflows.

Workflow with dbt:

1. Data Loading: First, data is loaded into a data warehouse from various sources using ELT tools (e.g., Fivetran, Stitch).
2. Transform with dbt: Using dbt, you write SQL models to clean, transform, and aggregate the raw data into useful, analytical datasets.
3. Build Data Models: You organize your models into layers, often referred to as staging, intermediate, and final models.
4. Testing and Documentation: Run tests to validate data, generate lineage diagrams, and create documentation.
5. Deploy: Schedule or trigger dbt jobs to run in production environments, ensuring consistent and accurate data transformations.

Example of a dbt Model:

-- models/staging_orders.sql
WITH raw_orders AS (
    SELECT * FROM {{ ref('raw_orders_data') }}
)
SELECT 
    order_id,
    customer_id,
    order_date,
    amount
FROM raw_orders
WHERE order_status = 'completed';

In this model:

• ref('raw_orders_data') is referencing another model that contains raw order data.
• The model selects and transforms only the completed orders.

Benefits of Using dbt:

1. Analyst Empowerment: dbt empowers data analysts to own the transformation process using SQL, reducing dependency on data engineers for transformations.
2. Version Control and Testing: Built-in version control and testing improve data reliability and reduce risks of errors in production.
3. Modularity and Scalability: The modular nature of dbt models makes it easier to scale transformations and manage complex pipelines.
4. Transparency and Documentation: dbt creates clear documentation and lineage automatically, improving visibility across teams.

Tools Integrating with dbt:

• Data Warehouses: Redshift, Snowflake, BigQuery, Postgres.
• ELT Tools: Stitch, Fivetran, Airbyte (for the extraction and loading phase).
• Version Control: GitHub, GitLab, Bitbucket (for managing dbt code).

Overall, dbt helps bridge the gap between data engineering and analytics, offering a robust, SQL-based framework for transforming data inside the warehouse.