How to Build a Data Pipeline with DBT and Python

Are you tired of managing your data pipeline manually? Do you want to make your data work more efficiently and effectively? Have you heard about DBT and Python? If your answer is "yes" to any of these questions, this article is for you!

DBT (Data Build Tool) is an open-source command-line tool that transforms your raw data into organized, structured datasets ready for analysis. It's a modern solution for building data pipelines that are easy to manage, test, and maintain. Python, on the other hand, is a widely used high-level programming language that is perfect for data processing and manipulation.

In this article, we will show you how to use DBT and Python together to build a data pipeline that transforms data from raw files to a structured, clean, and consolidated data warehouse. We will go step by step, and by the end of this article, you will have a complete data pipeline ready to use. So, let's get started!

Prerequisites

Before we start building our data pipeline, let's make sure we have all the necessary tools installed. Here is a list of tools we need for this tutorial:

• DBT: You can install DBT by following their installation guide.
• Python: You can download and install Python from their official website. Make sure you have Python 3.x installed on your machine.
• A code editor: You can use any code editor you prefer. Some popular editors are VS Code, PyCharm, and Sublime.

Setting Up the Project

Let's create a new folder for our project and navigate to that folder using your code editor's terminal.

$ mkdir my-project
$ cd my-project

Now, let's initialize our project and set up some basic configurations. To do this, create a new file called dbt_project.yml and enter the following YAML code:

name: my-project
version: '1.0.0'
config-version: 2

profile: default

This file tells DBT that this is a project named my-project, its version number, and the configuration version to use.

Next, let's create a new folder called models. This folder will contain all of our SQL and Python files that will transform our raw data into clean, structured datasets.

$ mkdir models

Connecting to a Database

Before we can start building our data pipeline, we need to connect to a database. In this tutorial, we will use a PostgreSQL database, but you can use any other database system that DBT supports.

To connect to a database, create a new file called profiles.yml and enter the following YAML code:

default:
  target: dev
  
  outputs:
    dev:
      type: postgres
      host: localhost
      port: 5432
      user: your_username
      password: your_password
      database: your_database
      schema: public

Replace your_username, your_password, your_database with your own database credentials.

Now, we can test the connection by running the following command:

$ dbt debug

If everything is set up correctly, you should see a message saying that DBT has connected to the database.

Configuring DBT

Before we start building our data pipeline, let's configure DBT. To do this, create a new file called dbt_project.yml and enter the following code:

name: my-project
version: '1.0.0'
config-version: 2

profile: default

models:
  my:
    model-paths: ['models']
    pre-hook: ['python my_script.py']

This YAML code tells DBT that the folder models will contain all of our models (transformations) and that we want to run a Python script called my_script.py before we run the models.

Our my_script.py script will prepare the raw data for transformation. Here is an example of what the script could look like:

import pandas as pd

# Load the raw data into a Pandas dataframe
df = pd.read_csv('raw_data.csv')

# Transform the data
df = df.drop_duplicates()

# Save the cleaned data
df.to_csv('cleaned_data.csv', index=False)

This script reads a CSV file called raw_data.csv, removes any duplicate rows, and then saves the cleaned data to cleaned_data.csv.

Creating Models

Now that we have configured DBT, let's create our first model. A model is a SQL or Python file that defines a transformation that takes raw data and produces a structured dataset.

Let's create a new file called users.sql in the models folder and enter the following SQL code:

-- models/users.sql

-- Define the source table
{{ config(
    materialized='table',
    schema='public'
  )
}}

WITH raw_data AS (
  SELECT * FROM {{ source('my_raw_data_table') }}
)

SELECT
  user_id,
  email,
  first_name,
  last_name
FROM raw_data;

This file defines a transformation that selects the user_id, email, first_name, and last_name columns from the my_raw_data_table table and saves the result in a table called users.

Note the config statement that specifies the materialization and schema of the resulting table. We use table as the materialization type to create a new table called users, and public as the schema for that table.

Next, let's create a Python model that cleans the data before it's used in the SQL model we just created. To do this, create a new Python file called clean_data.py in the models folder and enter the following Python code:

import pandas as pd

# Load the cleaned data
df = pd.read_csv('cleaned_data.csv')

# Clean the data
df['email'] = df['email'].str.lower()

# Save the cleaned data
df.to_csv('cleaned_data.csv', index=False)

This script reads cleaned_data.csv, converts all email addresses to lowercase, and saves the resulting data in cleaned_data.csv.

Now that we have defined our models, we can use DBT to build our data pipeline:

$ dbt run

This command will run the clean_data.py script, then execute the SQL model in users.sql. When it's done, we should have a new table called users with the cleaned data.

Conclusion

Using DBT together with Python can be a powerful combination for building data pipelines. With DBT, we can easily manage, test, and maintain our data transformations, while Python provides us with powerful tools for data processing and manipulation.

In this article, we have shown you how to use DBT and Python to build a data pipeline from scratch. We hope this tutorial has been helpful, and that you are now ready to explore DBT and Python further to build your own data pipelines.

Happy data transforming!

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