DBT : Learn what is dbt docs. Explain with example

dbt (data build tool) is an open-source command line tool that helps data analysts and engineers write, test, and organize their data transformation code. It is designed to work with data warehousing systems such as Snowflake, BigQuery, and Redshift, and can be integrated with other data infrastructure tools such as Apache Airflow and Apache Nifi.

The dbt docs are a collection of documentation and resources for learning and using dbt. They include the following sections:

• Getting Started: This section provides an introduction to dbt and a step-by-step guide for setting up dbt and running your first dbt command.
• Models: This section provides an in-depth explanation of the dbt model, which is the core unit of code in dbt. It explains how to write, test, and organize dbt models, and how to use advanced features such as computed columns and macros.
• Documentation: This section provides documentation for dbt commands and configuration options, as well as examples of common use cases and best practices.
• Reference: This section provides reference documentation for dbt’s built-in functions, macros, and other features.
• Community: This section provides resources for the dbt community, such as the dbt slack channel, dbt meetups, and the dbt forum.

Here is an example of how you might use dbt to build a model that aggregates data from a raw events table and creates a daily summary table:

# in your dbt project's `models` directory, create a new file called `freshers_in_day_summary.sql`

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

select
  date_trunc('day', occurred_at) as date,
  count(*) as event_view_count,
  sum(price) as revenue
from {{ ref('events') }}
group by 1