Implementing ETL Workflows with dbt (Data Build Tool) for Data Science

Introduction

In the fast-paced world of data science, having clean, reliable, and well-structured data is critical. Before data scientists can build models or generate insights, they often need to perform extensive data preparation. This is where ETL (Extract, Transform, Load) workflows come into play. Traditionally, ETL was handled by data engineers through custom scripts or enterprise tools. However, with the emergence of modern data stacks, tools like dbt (Data Build Tool) have revolutionised how teams build and manage data pipelines.

This article explores how to implement ETL workflows using dbt for data science projects—highlighting its advantages, structure, and how it fits into the broader data science process. If you are currently enrolled in a Data Science Course, understanding dbt will give you a competitive edge in building robust and scalable pipelines.

What is dbt?

dbt (Data Build Tool) is an open-source command-line tool widely used by data analysts and engineers to convert data in their data warehouse more effectively. dbt does not handle data extraction or loading—instead, it focuses exclusively on the Transform part of ETL.

With dbt, you write SQL queries to define data transformations, which are then compiled into tables or views in your data warehouse. It encourages modular development, version control, testing, and documentation—all essential elements of production-grade data workflows.

Why dbt Is Great for Data Science

• Declarative transformations using SQL
• Version-controlled codebase with Git
• Automated testing of data quality
• Clear documentation and lineage tracking
• Integration with modern warehouses like Snowflake, BigQuery, Redshift, and Postgres

Many advanced analytics projects and data course projects such as those in a Data Science Course in Mumbai rely on dbt to create clean and consistent data layers.

ETL vs ELT: A Quick Note

dbt supports the ELT paradigm, which differs slightly from traditional ETL. In ELT:

• Extract raw data from sources (for example, APIs, CSVs, databases)
• Load it into a centralised data warehouse
• Transform it within the warehouse using tools like dbt

This model aligns perfectly with the modern data science workflow, where scalable warehouses handle large volumes of data and transformations are run close to the data.

Setting Up dbt

Before jumping into transformations, you will need to set up dbt in your project. A practice-oriented data course, for example, a Data Science Course in Mumbai will have you perform the following initial steps.

Installation

You can install dbt via pip:

pip install dbt-core dbt-postgres  # Replace with your target adapter

Initialise a dbt Project

Once installed, initialise your dbt project:

dbt init my_dbt_project

This creates a standard directory structure:

my_dbt_project/

├── models/

├── tests/

├── dbt_project.yml

└── profiles.yml (in ~/.dbt/)

Configure Your Warehouse

Edit your profiles.yml to connect to your data warehouse (for example, Postgres, BigQuery, Snowflake). Here is a simple example for Postgres:

my_dbt_profile:

  target: dev

  outputs:

dev:

   type: postgres

   host: localhost

   user: username

   password: password

   dbname: mydatabase

   schema: analytics

   threads: 4

Write Transformations in dbt

In dbt, each transformation is written as a SQL file in the models/ directory. These SQL files represent transformations that materialise as views or tables in your data warehouse.

Example: Creating a Cleaned Users Table

Here is a basic transformation that cleans and standardises user data:

models/cleaned_users.sql

sql

CopyEdit

SELECT

id,

LOWER(TRIM(name)) AS name,

email,

created_at::date AS signup_date

FROM raw.users

WHERE email IS NOT NULL;

You can then run:

dbt run

This compiles your SQL and creates the cleaned_users table or view in your target schema. Exercises like these are often included in project-based assignments of a Data Science Course, helping students understand real-world transformation layers.

dbt Models: Modular and Layered

dbt encourages you to structure transformations in staged layers, often following this pattern:

• Staging: Clean raw data with minimal transformations (for example, naming standardisation, null handling)
• Intermediate: Join multiple sources or perform aggregations
• Marts: Final datasets used for reporting, analysis, or feeding into ML models

This layered approach improves maintainability, testing, and readability. For data scientists, this makes it easier to trace data from raw sources to model inputs.

Testing Your Data

Testing is a core part of dbt and a key benefit for data science teams aiming to trust their data inputs.

dbt supports two main types of tests:

• Singular tests: Custom SQL queries that should return zero rows
• Generic tests: Predefined checks like not_null, unique, and accepted_values

Example:

models/schema.yml

yaml

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version: 2

models:

  – name: cleaned_users

columns:

   – name: id

     tests:

       – not_null

       – unique

   – name: email

     tests:

       – not_null

To run tests:

dbt test

A comprehensive Data Science Course heavily emphasises concepts like automated testing, particularly those focused on building production-grade pipelines.

Documenting with dbt

Documentation is crucial in collaborative data science environments. dbt lets you document your models directly in schema.yml, then generates a website you can share with your team.

dbt docs generate

dbt docs serve

This launches a browsable UI that includes your model descriptions, columns, tests, and lineage graphs—very useful for onboarding and team communication.

Integrating dbt into a Data Science Workflow

Here is how dbt fits into the modern data science process:

 Data Ingestion

Raw data is loaded into a data warehouse using tools like Fivetran, Airbyte, or custom Python scripts.

 Transformation with dbt

Use dbt to clean, standardise, and structure the raw data into analysis-ready formats.

Exploration and Modelling

Data scientists can then query the curated tables using Python (via libraries like pandas,   SQLAlchemy, or dbt’s Python API), or export data into Jupyter notebooks for modelling.

Automation

 ETL jobs can be scheduled using orchestration tools like Airflow, Prefect, or dbt Cloud’s built-in scheduler.

Benefits of Using dbt for Data Science

Benefit	Description
Reproducibility	Version control ensures that transformations are consistent across environments.
Transparency	Clear lineage helps data scientists trace model inputs back to raw data
Data Quality	Built-in testing prevents unexpected issues in pipelines.
Scalability	SQL is executed directly in the warehouse, enabling massive datasets.
Collaboration	dbt is designed for teams—making it easier for data engineers and scientists to work together.

 

Collaboration  dbt is designed for teams—making it easier for data engineers and scientists to work together.

If you are studying in a well-rounded, up-to-date data course such as a Data Science Course in Mumbai, you will gain adequate knowledge of dbt, which will dramatically enhance your ability to manage data pipelines in real-world scenarios.

Real-World Use Cases

• Churn Prediction: Use dbt to prepare customer behaviour data for predictive modelling.
• Sales Forecasting: Create time-series-ready tables from messy transaction logs.
• Fraud Detection: Cleanse and join multiple sources for anomaly detection inputs.
• Feature Engineering: Perform complex aggregations inside dbt before pulling features into ML models.

Conclusion

dbt empowers data scientists to own and trust their data pipelines by simplifying and structuring the transformation layer. With dbt, you can create reproducible, documented, and testable workflows that scale—without writing custom ETL code or relying on opaque processes.

Whether you are part of a large data science team or working solo, incorporating dbt into your ETL workflow can significantly enhance productivity and data reliability. For students enrolled in a Data Science Course in Mumbai, mastering dbt is a step toward becoming a well-rounded data professional in the modern analytics ecosystem.

 

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