Step-by-Step: ETL Pipeline with Snowflake and dbt

Quality Thought: The Best Data Engineering Snowflake Training Course Institute in Hyderabad

Topic: Step-by-Step – ETL Pipeline with Snowflake and dbt

In the modern data-driven world, organizations require efficient and scalable platforms to process and analyze vast volumes of data. Snowflake, a powerful cloud data platform, paired with dbt (data build tool), has become a leading combination for building flexible, reliable, and maintainable ETL (Extract, Transform, Load) pipelines. If you're a graduate, postgraduate, someone with an education gap, or looking for a career change into the high-growth data engineering field, Quality Thought is the best data engineering Snowflake training course institute in Hyderabad offering a live intensive internship program conducted by industry experts.

Why Snowflake and dbt?

Snowflake offers a cloud-native, scalable data warehouse with separate compute and storage.

dbt enables analysts and engineers to transform data directly in the warehouse using SQL and manage transformation logic with software engineering best practices.

Together, they streamline the ETL pipeline, reduce complexity, and support data teams in delivering trusted analytics.

Step-by-Step: Building an ETL Pipeline with Snowflake and dbt

Step 1: Data Extraction

Data is first extracted from source systems such as:

APIs

Cloud storage (AWS S3, GCS)

Relational databases (MySQL, PostgreSQL)

Tools like Fivetran, Airbyte, or custom Python scripts are often used to bring raw data into Snowflake staging tables.

Step 2: Load into Snowflake

Once extracted, the raw data is loaded into Snowflake:

sql

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COPY INTO raw_table

FROM @s3_stage/file.csv

FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"');

This creates raw, unprocessed tables for further transformation.

Step 3: Set Up dbt Project

Use dbt to manage SQL-based transformations:

bash

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dbt init my_project

Configure profiles.yml to connect dbt to Snowflake securely using credentials and warehouse settings.

Step 4: Create Models

Inside dbt, write SQL models to transform raw data into structured formats:

sql

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-- models/stg_customers.sql

SELECT

  id AS customer_id,

  name,

  created_at::date AS signup_date

FROM {{ source('raw', 'customers') }}

Step 5: Run dbt Transformations

Use dbt commands to run your transformations and materialize the models in Snowflake:

bash

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dbt run

You can also use dbt test to validate data quality and dbt docs generate for automated documentation.

Step 6: Schedule and Monitor

Integrate your dbt pipeline into workflow orchestration tools like Airflow or Dagster for scheduling and monitoring.

Why Choose Quality Thought?

Expert Trainers: Learn from real-time professionals with Snowflake, dbt, and cloud data engineering experience.

Live Internship Program: Gain hands-on experience working on enterprise-level ETL pipelines.

Tailored for All Backgrounds: Ideal for graduates, postgraduates, education gap candidates, and those switching to data careers.

Complete Curriculum: Covers Snowflake architecture, SQL optimization, dbt development, data modeling, and deployment.

Placement Support: Resume building, mock interviews, and job referrals to top data engineering roles.

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Conclusion

Building an ETL pipeline with Snowflake and dbt is an essential skill for modern data engineers. At Quality Thought, we teach you how to design, build, and manage scalable pipelines through our expert-led training and live internship program. As the best Snowflake data engineering course institute in Hyderabad, we empower learners from all backgrounds to succeed in the data industry. Enroll today and take your first step toward a rewarding career in data engineering.

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