What is dbt

Introduction

dbt helps you organize and manage your data transformations easily. It makes your data clean and ready for analysis.

When you want to turn raw data into clear, useful tables for reports.

When you need to keep track of how your data changes over time.

When you want to share your data work with teammates in a simple way.

When you want to automate data updates without manual work.

Syntax

dbt

model_name.sql

-- SQL code to transform data
SELECT * FROM source_table WHERE condition;

dbt uses simple SQL files called models to define data transformations.

Each model creates a new table or view in your data warehouse.

Examples

This model selects active customers from raw data.

dbt

-- models/customers.sql
SELECT id, name, email FROM raw_customers WHERE active = true;

This model summarizes sales by date.

dbt

-- models/sales_summary.sql
SELECT date, SUM(amount) AS total_sales FROM raw_sales GROUP BY date;

Sample Program

This example model selects users who signed up after January 1, 2023, and makes their names uppercase.

dbt

-- models/example_model.sql
SELECT id, UPPER(name) AS name_upper FROM raw_users WHERE signup_date > '2023-01-01';

Important Notes

dbt runs your SQL models in order and manages dependencies automatically.

You can test your data with dbt to catch errors early.

dbt works well with many data warehouses like Snowflake, BigQuery, and Redshift.

Summary

dbt helps turn raw data into clean, organized tables using SQL.

It automates data transformations and tracks changes clearly.

dbt makes teamwork on data projects easier and more reliable.